Theorem Proving in Lean 4
by Jeremy Avigad, Leonardo de Moura, Soonho Kong and Sebastian Ullrich, with contributions from the Lean Community
This version of the text assumes you’re using Lean 4. See the Quickstart section of the Lean 4 Manual to install Lean. The first version of this book was written for Lean 2, and the Lean 3 version is available here.
Introduction
Computers and Theorem Proving
Formal verification involves the use of logical and computational methods to establish claims that are expressed in precise mathematical terms. These can include ordinary mathematical theorems, as well as claims that pieces of hardware or software, network protocols, and mechanical and hybrid systems meet their specifications. In practice, there is not a sharp distinction between verifying a piece of mathematics and verifying the correctness of a system: formal verification requires describing hardware and software systems in mathematical terms, at which point establishing claims as to their correctness becomes a form of theorem proving. Conversely, the proof of a mathematical theorem may require a lengthy computation, in which case verifying the truth of the theorem requires verifying that the computation does what it is supposed to do.
The gold standard for supporting a mathematical claim is to provide a proof, and twentiethcentury developments in logic show most if not all conventional proof methods can be reduced to a small set of axioms and rules in any of a number of foundational systems. With this reduction, there are two ways that a computer can help establish a claim: it can help find a proof in the first place, and it can help verify that a purported proof is correct.
Automated theorem proving focuses on the "finding" aspect. Resolution theorem provers, tableau theorem provers, fast satisfiability solvers, and so on provide means of establishing the validity of formulas in propositional and firstorder logic. Other systems provide search procedures and decision procedures for specific languages and domains, such as linear or nonlinear expressions over the integers or the real numbers. Architectures like SMT ("satisfiability modulo theories") combine domaingeneral search methods with domainspecific procedures. Computer algebra systems and specialized mathematical software packages provide means of carrying out mathematical computations, establishing mathematical bounds, or finding mathematical objects. A calculation can be viewed as a proof as well, and these systems, too, help establish mathematical claims.
Automated reasoning systems strive for power and efficiency, often at the expense of guaranteed soundness. Such systems can have bugs, and it can be difficult to ensure that the results they deliver are correct. In contrast, interactive theorem proving focuses on the "verification" aspect of theorem proving, requiring that every claim is supported by a proof in a suitable axiomatic foundation. This sets a very high standard: every rule of inference and every step of a calculation has to be justified by appealing to prior definitions and theorems, all the way down to basic axioms and rules. In fact, most such systems provide fully elaborated "proof objects" that can be communicated to other systems and checked independently. Constructing such proofs typically requires much more input and interaction from users, but it allows you to obtain deeper and more complex proofs.
The Lean Theorem Prover aims to bridge the gap between interactive and automated theorem proving, by situating automated tools and methods in a framework that supports user interaction and the construction of fully specified axiomatic proofs. The goal is to support both mathematical reasoning and reasoning about complex systems, and to verify claims in both domains.
Lean's underlying logic has a computational interpretation, and Lean can be viewed equally well as a programming language. More to the point, it can be viewed as a system for writing programs with a precise semantics, as well as reasoning about the functions that the programs compute. Lean also has mechanisms to serve as its own metaprogramming language, which means that you can implement automation and extend the functionality of Lean using Lean itself. These aspects of Lean are described in the free online book, Functional Programming in Lean, though computational aspects of the system will make an appearance here.
About Lean
The Lean project was launched by Leonardo de Moura at Microsoft Research Redmond in 2013. It is an ongoing, longterm effort, and much of the potential for automation will be realized only gradually over time. Lean is released under the Apache 2.0 license, a permissive open source license that permits others to use and extend the code and mathematical libraries freely.
To install Lean in your computer consider using the Quickstart instructions. The Lean source code, and instructions for building Lean, are available at https://github.com/leanprover/lean4/.
This tutorial describes the current version of Lean, known as Lean 4.
About this Book
This book is designed to teach you to develop and verify proofs in Lean. Much of the background information you will need in order to do this is not specific to Lean at all. To start with, you will learn the logical system that Lean is based on, a version of dependent type theory that is powerful enough to prove almost any conventional mathematical theorem, and expressive enough to do it in a natural way. More specifically, Lean is based on a version of a system known as the Calculus of Constructions with inductive types. Lean can not only define mathematical objects and express mathematical assertions in dependent type theory, but it also can be used as a language for writing proofs.
Because fully detailed axiomatic proofs are so complicated, the challenge of theorem proving is to have the computer fill in as many of the details as possible. You will learn various methods to support this in dependent type theory. For example, term rewriting, and Lean's automated methods for simplifying terms and expressions automatically. Similarly, methods of elaboration and type inference, which can be used to support flexible forms of algebraic reasoning.
Finally, you will learn about features that are specific to Lean, including the language you use to communicate with the system, and the mechanisms Lean offers for managing complex theories and data.
Throughout the text you will find examples of Lean code like the one below:
theorem and_commutative (p q : Prop) : p ∧ q → q ∧ p :=
fun hpq : p ∧ q =>
have hp : p := And.left hpq
have hq : q := And.right hpq
show q ∧ p from And.intro hq hp
If you are reading the book inside of VS Code, you will see a button that reads "try it!" Pressing the button copies the example to your editor with enough surrounding context to make the code compile correctly. You can type things into the editor and modify the examples, and Lean will check the results and provide feedback continuously as you type. We recommend running the examples and experimenting with the code on your own as you work through the chapters that follow. You can open this book on VS Code by using the command "Lean 4: Open Documentation View".
Acknowledgments
This tutorial is an open access project maintained on Github. Many people have contributed to the effort, providing corrections, suggestions, examples, and text. We are grateful to Ulrik Buchholz, Kevin Buzzard, Mario Carneiro, Nathan Carter, Eduardo Cavazos, Amine Chaieb, Joe Corneli, William DeMeo, Marcus Klaas de Vries, Ben Dyer, Gabriel Ebner, Anthony Hart, Simon Hudon, Sean Leather, Assia Mahboubi, Gihan Marasingha, Patrick Massot, Christopher John Mazey, Sebastian Ullrich, Floris van Doorn, Daniel Velleman, Théo Zimmerman, Paul Chisholm, Chris Lovett, and Siddhartha Gadgil for their contributions. Please see lean prover and lean community for an up to date list of our amazing contributors.
Dependent Type Theory
Dependent type theory is a powerful and expressive language, allowing you to express complex mathematical assertions, write complex hardware and software specifications, and reason about both of these in a natural and uniform way. Lean is based on a version of dependent type theory known as the Calculus of Constructions, with a countable hierarchy of noncumulative universes and inductive types. By the end of this chapter, you will understand much of what this means.
Simple Type Theory
"Type theory" gets its name from the fact that every expression has an
associated type. For example, in a given context, x + 0
may
denote a natural number and f
may denote a function on the natural
numbers. For those who like precise definitions, a Lean natural number
is an arbitraryprecision unsigned integer.
Here are some examples of how you can declare objects in Lean and check their types.
/ Define some constants. /
def m : Nat := 1  m is a natural number
def n : Nat := 0
def b1 : Bool := true  b1 is a Boolean
def b2 : Bool := false
/ Check their types. /
#check m  output: Nat
#check n
#check n + 0  Nat
#check m * (n + 0)  Nat
#check b1  Bool
#check b1 && b2  "&&" is the Boolean and
#check b1  b2  Boolean or
#check true  Boolean "true"
/ Evaluate /
#eval 5 * 4  20
#eval m + 2  3
#eval b1 && b2  false
Any text between /
and /
constitutes a comment block that is
ignored by Lean. Similarly, two dashes 
indicate that the rest of
the line contains a comment that is also ignored. Comment blocks can
be nested, making it possible to "comment out" chunks of code, just as
in many programming languages.
The def
keyword declares new constant symbols into the
working environment. In the example above, def m : Nat := 1
defines a new constant m
of type Nat
whose value is 1
.
The #check
command asks Lean to report their
types; in Lean, auxiliary commands that query the system for
information typically begin with the hash (#) symbol.
The #eval
command asks Lean to evaluate the given expression.
You should try
declaring some constants and type checking some expressions on your
own. Declaring new objects in this manner is a good way to experiment
with the system.
What makes simple type theory powerful is that you can build new types
out of others. For example, if a
and b
are types, a > b
denotes the type of functions from a
to b
, and a × b
denotes the type of pairs consisting of an element of a
paired
with an element of b
, also known as the Cartesian product. Note
that ×
is a Unicode symbol. The judicious use of Unicode improves
legibility, and all modern editors have great support for it. In the
Lean standard library, you often see Greek letters to denote types,
and the Unicode symbol →
as a more compact version of >
.
#check Nat → Nat  type the arrow as "\to" or "\r"
#check Nat > Nat  alternative ASCII notation
#check Nat × Nat  type the product as "\times"
#check Prod Nat Nat  alternative notation
#check Nat → Nat → Nat
#check Nat → (Nat → Nat)  same type as above
#check Nat × Nat → Nat
#check (Nat → Nat) → Nat  a "functional"
#check Nat.succ  Nat → Nat
#check (0, 1)  Nat × Nat
#check Nat.add  Nat → Nat → Nat
#check Nat.succ 2  Nat
#check Nat.add 3  Nat → Nat
#check Nat.add 5 2  Nat
#check (5, 9).1  Nat
#check (5, 9).2  Nat
#eval Nat.succ 2  3
#eval Nat.add 5 2  7
#eval (5, 9).1  5
#eval (5, 9).2  9
Once again, you should try some examples on your own.
Let's take a look at some basic syntax. You can enter the unicode
arrow →
by typing \to
or \r
or \>
. You can also use the
ASCII alternative >
, so the expressions Nat > Nat
and Nat → Nat
mean the same thing. Both expressions denote the type of
functions that take a natural number as input and return a natural
number as output. The unicode symbol ×
for the Cartesian product
is entered as \times
. You will generally use lowercase Greek
letters like α
, β
, and γ
to range over types. You can
enter these particular ones with \a
, \b
, and \g
.
There are a few more things to notice here. First, the application of
a function f
to a value x
is denoted f x
(e.g., Nat.succ 2
).
Second, when writing type expressions, arrows associate to the right; for
example, the type of Nat.add
is Nat → Nat → Nat
which is equivalent
to Nat → (Nat → Nat)
. Thus you can
view Nat.add
as a function that takes a natural number and returns
another function that takes a natural number and returns a natural
number. In type theory, this is generally more convenient than
writing Nat.add
as a function that takes a pair of natural numbers as
input and returns a natural number as output. For example, it allows
you to "partially apply" the function Nat.add
. The example above shows
that Nat.add 3
has type Nat → Nat
, that is, Nat.add 3
returns a
function that "waits" for a second argument, n
, which is then
equivalent to writing Nat.add 3 n
.
You have seen that if you have m : Nat
and n : Nat
, then
(m, n)
denotes the ordered pair of m
and n
which is of
type Nat × Nat
. This gives you a way of creating pairs of natural
numbers. Conversely, if you have p : Nat × Nat
, then you can write
p.1 : Nat
and p.2 : Nat
. This gives you a way of extracting
its two components.
Types as objects
One way in which Lean's dependent type theory extends simple type
theory is that types themselves  entities like Nat
and Bool
 are firstclass citizens, which is to say that they themselves are
objects. For that to be the case, each of them also has to have a
type.
#check Nat  Type
#check Bool  Type
#check Nat → Bool  Type
#check Nat × Bool  Type
#check Nat → Nat  ...
#check Nat × Nat → Nat
#check Nat → Nat → Nat
#check Nat → (Nat → Nat)
#check Nat → Nat → Bool
#check (Nat → Nat) → Nat
You can see that each one of the expressions above is an object of
type Type
. You can also declare new constants for types:
def α : Type := Nat
def β : Type := Bool
def F : Type → Type := List
def G : Type → Type → Type := Prod
#check α  Type
#check F α  Type
#check F Nat  Type
#check G α  Type → Type
#check G α β  Type
#check G α Nat  Type
As the example above suggests, you have already seen an example of a function of type
Type → Type → Type
, namely, the Cartesian product Prod
:
def α : Type := Nat
def β : Type := Bool
#check Prod α β  Type
#check α × β  Type
#check Prod Nat Nat  Type
#check Nat × Nat  Type
Here is another example: given any type α
, the type List α
denotes the type of lists of elements of type α
.
def α : Type := Nat
#check List α  Type
#check List Nat  Type
Given that every expression in Lean has a type, it is natural to ask:
what type does Type
itself have?
#check Type  Type 1
You have actually come up against one of the most subtle aspects of Lean's typing system. Lean's underlying foundation has an infinite hierarchy of types:
#check Type  Type 1
#check Type 1  Type 2
#check Type 2  Type 3
#check Type 3  Type 4
#check Type 4  Type 5
Think of Type 0
as a universe of "small" or "ordinary" types.
Type 1
is then a larger universe of types, which contains Type 0
as an element, and Type 2
is an even larger universe of types,
which contains Type 1
as an element. The list is indefinite, so
that there is a Type n
for every natural number n
. Type
is
an abbreviation for Type 0
:
#check Type
#check Type 0
The following table may help concretize the relationships being discussed. Movement along the xaxis represents a change in the universe, while movement along the yaxis represents a change in what is sometimes referred to as "degree".
sort  Prop (Sort 0)  Type (Sort 1)  Type 1 (Sort 2)  Type 2 (Sort 3)  ... 
type  True  Bool  Nat > Type  Type > Type 1  ... 
term  trivial  true  fun n => Fin n  fun (_ : Type) => Type  ... 
Some operations, however, need to be polymorphic over type
universes. For example, List α
should make sense for any type
α
, no matter which type universe α
lives in. This explains the
type signature of the function List
:
#check List  List.{u} (α : Type u) : Type u
Here u
is a variable ranging over type levels. The output of the
#check
command means that whenever α
has type Type n
,
List α
also has type Type n
. The function Prod
is
similarly polymorphic:
#check Prod  Prod.{u, v} (α : Type u) (β : Type v) : Type (max u v)
To define polymorphic constants, Lean allows you to
declare universe variables explicitly using the universe
command:
universe u
def F (α : Type u) : Type u := Prod α α
#check F  Type u → Type u
You can avoid the universe command by providing the universe parameters when defining F.
def F.{u} (α : Type u) : Type u := Prod α α
#check F  Type u → Type u
Function Abstraction and Evaluation
Lean provides a fun
(or λ
) keyword to create a function
from an expression as follows:
#check fun (x : Nat) => x + 5  Nat → Nat
#check λ (x : Nat) => x + 5  λ and fun mean the same thing
#check fun x => x + 5  Nat inferred
#check λ x => x + 5  Nat inferred
You can evaluate a lambda function by passing the required parameters:
#eval (λ x : Nat => x + 5) 10  15
Creating a function from another expression is a process known as
lambda abstraction. Suppose you have the variable x : α
and you can
construct an expression t : β
, then the expression fun (x : α) => t
, or, equivalently, λ (x : α) => t
, is an object of type α → β
. Think of this as the function from α
to β
which maps
any value x
to the value t
.
Here are some more examples
#check fun x : Nat => fun y : Bool => if not y then x + 1 else x + 2
#check fun (x : Nat) (y : Bool) => if not y then x + 1 else x + 2
#check fun x y => if not y then x + 1 else x + 2  Nat → Bool → Nat
Lean interprets the final three examples as the same expression; in
the last expression, Lean infers the type of x
and y
from the
expression if not y then x + 1 else x + 2
.
Some mathematically common examples of operations of functions can be described in terms of lambda abstraction:
def f (n : Nat) : String := toString n
def g (s : String) : Bool := s.length > 0
#check fun x : Nat => x  Nat → Nat
#check fun x : Nat => true  Nat → Bool
#check fun x : Nat => g (f x)  Nat → Bool
#check fun x => g (f x)  Nat → Bool
Think about what these expressions mean. The expression
fun x : Nat => x
denotes the identity function on Nat
, the
expression fun x : Nat => true
denotes the constant function that
always returns true
, and fun x : Nat => g (f x)
denotes the
composition of f
and g
. You can, in general, leave off the
type annotation and let Lean infer it for you. So, for example, you
can write fun x => g (f x)
instead of fun x : Nat => g (f x)
.
You can pass functions as parameters and by giving them names f
and g
you can then use those functions in the implementation:
#check fun (g : String → Bool) (f : Nat → String) (x : Nat) => g (f x)
 (String → Bool) → (Nat → String) → Nat → Bool
You can also pass types as parameters:
#check fun (α β γ : Type) (g : β → γ) (f : α → β) (x : α) => g (f x)
The last expression, for example, denotes the function that takes
three types, α
, β
, and γ
, and two functions, g : β → γ
and f : α → β
, and returns the composition of g
and f
.
(Making sense of the type of this function requires an understanding
of dependent products, which will be explained below.)
The general form of a lambda expression is fun x : α => t
, where
the variable x
is a "bound variable": it is really a placeholder,
whose "scope" does not extend beyond the expression t
. For
example, the variable b
in the expression fun (b : β) (x : α) => b
has nothing to do with the constant b
declared earlier. In fact,
the expression denotes the same function as fun (u : β) (z : α) => u
.
Formally, expressions that are the same up to a renaming of bound variables are called alpha equivalent, and are considered "the same." Lean recognizes this equivalence.
Notice that applying a term t : α → β
to a term s : α
yields
an expression t s : β
. Returning to the previous example and
renaming bound variables for clarity, notice the types of the
following expressions:
#check (fun x : Nat => x) 1  Nat
#check (fun x : Nat => true) 1  Bool
def f (n : Nat) : String := toString n
def g (s : String) : Bool := s.length > 0
#check
(fun (α β γ : Type) (u : β → γ) (v : α → β) (x : α) => u (v x)) Nat String Bool g f 0
 Bool
As expected, the expression (fun x : Nat => x) 1
has type Nat
.
In fact, more should be true: applying the expression (fun x : Nat => x)
to 1
should "return" the value 1
. And, indeed, it does:
#eval (fun x : Nat => x) 1  1
#eval (fun x : Nat => true) 1  true
You will see later how these terms are evaluated. For now, notice that this is an important feature of dependent type theory: every term has a computational behavior, and supports a notion of normalization. In principle, two terms that reduce to the same value are called definitionally equal. They are considered "the same" by Lean's type checker, and Lean does its best to recognize and support these identifications.
Lean is a complete programming language. It has a compiler that
generates a binary executable and an interactive interpreter. You can
use the command #eval
to execute expressions, and it is the
preferred way of testing your functions.
Definitions
Recall that the def
keyword provides one important way of declaring new named
objects.
def double (x : Nat) : Nat :=
x + x
This might look more familiar to you if you know how functions work in
other programming languages. The name double
is defined as a
function that takes an input parameter x
of type Nat
, where the
result of the call is x + x
, so it is returning type Nat
. You
can then invoke this function using:
def double (x : Nat) : Nat :=
x + x
#eval double 3  6
In this case you can think of def
as a kind of named lambda
.
The following yields the same result:
def double : Nat → Nat :=
fun x => x + x
#eval double 3  6
You can omit the type declarations when Lean has enough information to infer it. Type inference is an important part of Lean:
def double :=
fun (x : Nat) => x + x
The general form of a definition is def foo : α := bar
where
α
is the type returned from the expression bar
. Lean can
usually infer the type α
, but it is often a good idea to write it
explicitly. This clarifies your intention, and Lean will flag an
error if the righthand side of the definition does not have a matching
type.
The right hand side bar
can be any expression, not just a lambda.
So def
can also be used to simply name a value like this:
def pi := 3.141592654
def
can take multiple input parameters. Let's create one
that adds two natural numbers:
def add (x y : Nat) :=
x + y
#eval add 3 2  5
The parameter list can be separated like this:
def double (x : Nat) : Nat :=
x + x
def add (x : Nat) (y : Nat) :=
x + y
#eval add (double 3) (7 + 9)  22
Notice here we called the double
function to create the first
parameter to add
.
You can use other more interesting expressions inside a def
:
def greater (x y : Nat) :=
if x > y then x
else y
You can probably guess what this one will do.
You can also define a function that takes another function as input. The following calls a given function twice passing the output of the first invocation to the second:
def double (x : Nat) : Nat :=
x + x
def doTwice (f : Nat → Nat) (x : Nat) : Nat :=
f (f x)
#eval doTwice double 2  8
Now to get a bit more abstract, you can also specify arguments that are like type parameters:
def compose (α β γ : Type) (g : β → γ) (f : α → β) (x : α) : γ :=
g (f x)
This means compose
is a function that takes any two functions as input
arguments, so long as those functions each take only one input.
The type algebra β → γ
and α → β
means it is a requirement
that the type of the output of the second function must match the
type of the input to the first function  which makes sense, otherwise
the two functions would not be composable.
compose
also takes a 3rd argument of type α
which
it uses to invoke the second function (locally named f
) and it
passes the result of that function (which is type β
) as input to the
first function (locally named g
). The first function returns a type
γ
so that is also the return type of the compose
function.
compose
is also very general in that it works over any type
α β γ
. This means compose
can compose just about any 2 functions
so long as they each take one parameter, and so long as the type of
output of the second matches the input of the first. For example:
def compose (α β γ : Type) (g : β → γ) (f : α → β) (x : α) : γ :=
g (f x)
def double (x : Nat) : Nat :=
x + x
def square (x : Nat) : Nat :=
x * x
#eval compose Nat Nat Nat double square 3  18
Local Definitions
Lean also allows you to introduce "local" definitions using the
let
keyword. The expression let a := t1; t2
is
definitionally equal to the result of replacing every occurrence of
a
in t2
by t1
.
#check let y := 2 + 2; y * y  Nat
#eval let y := 2 + 2; y * y  16
def twice_double (x : Nat) : Nat :=
let y := x + x; y * y
#eval twice_double 2  16
Here, twice_double x
is definitionally equal to the term (x + x) * (x + x)
.
You can combine multiple assignments by chaining let
statements:
#check let y := 2 + 2; let z := y + y; z * z  Nat
#eval let y := 2 + 2; let z := y + y; z * z  64
The ;
can be omitted when a line break is used.
def t (x : Nat) : Nat :=
let y := x + x
y * y
Notice that the meaning of the expression let a := t1; t2
is very
similar to the meaning of (fun a => t2) t1
, but the two are not
the same. In the first expression, you should think of every instance
of a
in t2
as a syntactic abbreviation for t1
. In the
second expression, a
is a variable, and the expression
fun a => t2
has to make sense independently of the value of a
.
The let
construct is a stronger means of abbreviation, and there
are expressions of the form let a := t1; t2
that cannot be
expressed as (fun a => t2) t1
. As an exercise, try to understand
why the definition of foo
below type checks, but the definition of
bar
does not.
def foo := let a := Nat; fun x : a => x + 2
/
def bar := (fun a => fun x : a => x + 2) Nat
/
Variables and Sections
Consider the following three function definitions:
def compose (α β γ : Type) (g : β → γ) (f : α → β) (x : α) : γ :=
g (f x)
def doTwice (α : Type) (h : α → α) (x : α) : α :=
h (h x)
def doThrice (α : Type) (h : α → α) (x : α) : α :=
h (h (h x))
Lean provides you with the variable
command to make such
declarations look more compact:
variable (α β γ : Type)
def compose (g : β → γ) (f : α → β) (x : α) : γ :=
g (f x)
def doTwice (h : α → α) (x : α) : α :=
h (h x)
def doThrice (h : α → α) (x : α) : α :=
h (h (h x))
You can declare variables of any type, not just Type
itself:
variable (α β γ : Type)
variable (g : β → γ) (f : α → β) (h : α → α)
variable (x : α)
def compose := g (f x)
def doTwice := h (h x)
def doThrice := h (h (h x))
#print compose
#print doTwice
#print doThrice
Printing them out shows that all three groups of definitions have exactly the same effect.
The variable
command instructs Lean to insert the declared
variables as bound variables in definitions that refer to them by
name. Lean is smart enough to figure out which variables are used
explicitly or implicitly in a definition. You can therefore proceed as
though α
, β
, γ
, g
, f
, h
, and x
are fixed
objects when you write your definitions, and let Lean abstract the
definitions for you automatically.
When declared in this way, a variable stays in scope until the end of
the file you are working on. Sometimes, however, it is useful to limit
the scope of a variable. For that purpose, Lean provides the notion of
a section
:
section useful
variable (α β γ : Type)
variable (g : β → γ) (f : α → β) (h : α → α)
variable (x : α)
def compose := g (f x)
def doTwice := h (h x)
def doThrice := h (h (h x))
end useful
When the section is closed, the variables go out of scope, and cannot be referenced any more.
You do not have to indent the lines within a section. Nor do you have
to name a section, which is to say, you can use an anonymous
section
/ end
pair. If you do name a section, however, you
have to close it using the same name. Sections can also be nested,
which allows you to declare new variables incrementally.
Namespaces
Lean provides you with the ability to group definitions into nested, hierarchical namespaces:
namespace Foo
def a : Nat := 5
def f (x : Nat) : Nat := x + 7
def fa : Nat := f a
def ffa : Nat := f (f a)
#check a
#check f
#check fa
#check ffa
#check Foo.fa
end Foo
 #check a  error
 #check f  error
#check Foo.a
#check Foo.f
#check Foo.fa
#check Foo.ffa
open Foo
#check a
#check f
#check fa
#check Foo.fa
When you declare that you are working in the namespace Foo
, every
identifier you declare has a full name with prefix "Foo.
". Within
the namespace, you can refer to identifiers by their shorter names,
but once you end the namespace, you have to use the longer names.
Unlike section
, namespaces require a name. There is only one
anonymous namespace at the root level.
The open
command brings the shorter names into the current
context. Often, when you import a module, you will want to open one or
more of the namespaces it contains, to have access to the short
identifiers. But sometimes you will want to leave this information
protected by a fully qualified name, for example, when they conflict
with identifiers in another namespace you want to use. Thus namespaces
give you a way to manage names in your working environment.
For example, Lean groups definitions and theorems involving lists into
a namespace List
.
#check List.nil
#check List.cons
#check List.map
The command open List
allows you to use the shorter names:
open List
#check nil
#check cons
#check map
Like sections, namespaces can be nested:
namespace Foo
def a : Nat := 5
def f (x : Nat) : Nat := x + 7
def fa : Nat := f a
namespace Bar
def ffa : Nat := f (f a)
#check fa
#check ffa
end Bar
#check fa
#check Bar.ffa
end Foo
#check Foo.fa
#check Foo.Bar.ffa
open Foo
#check fa
#check Bar.ffa
Namespaces that have been closed can later be reopened, even in another file:
namespace Foo
def a : Nat := 5
def f (x : Nat) : Nat := x + 7
def fa : Nat := f a
end Foo
#check Foo.a
#check Foo.f
namespace Foo
def ffa : Nat := f (f a)
end Foo
Like sections, nested namespaces have to be closed in the order they
are opened. Namespaces and sections serve different purposes:
namespaces organize data and sections declare variables for insertion
in definitions. Sections are also useful for delimiting the scope of
commands such as set_option
and open
.
In many respects, however, a namespace ... end
block behaves the
same as a section ... end
block. In particular, if you use the
variable
command within a namespace, its scope is limited to the
namespace. Similarly, if you use an open
command within a
namespace, its effects disappear when the namespace is closed.
What makes dependent type theory dependent?
The short explanation is that types can depend on parameters. You
have already seen a nice example of this: the type List α
depends
on the argument α
, and this dependence is what distinguishes
List Nat
and List Bool
. For another example, consider the
type Vector α n
, the type of vectors of elements of α
of
length n
. This type depends on two parameters: the type of the
elements in the vector (α : Type
) and the length of the vector
n : Nat
.
Suppose you wish to write a function cons
which inserts a new
element at the head of a list. What type should cons
have? Such a
function is polymorphic: you expect the cons
function for
Nat
, Bool
, or an arbitrary type α
to behave the same way.
So it makes sense to take the type to be the first argument to
cons
, so that for any type, α
, cons α
is the insertion
function for lists of type α
. In other words, for every α
,
cons α
is the function that takes an element a : α
and a list
as : List α
, and returns a new list, so you have cons α a as : List α
.
It is clear that cons α
should have type α → List α → List α
.
But what type should cons
have? A first guess might be
Type → α → List α → List α
, but, on reflection, this does not make
sense: the α
in this expression does not refer to anything,
whereas it should refer to the argument of type Type
. In other
words, assuming α : Type
is the first argument to the function,
the type of the next two elements are α
and List α
. These
types vary depending on the first argument, α
.
def cons (α : Type) (a : α) (as : List α) : List α :=
List.cons a as
#check cons Nat  Nat → List Nat → List Nat
#check cons Bool  Bool → List Bool → List Bool
#check cons  (α : Type) → α → List α → List α
This is an instance of a dependent function type, or dependent
arrow type. Given α : Type
and β : α → Type
, think of β
as a family of types over α
, that is, a type β a
for each
a : α
. In that case, the type (a : α) → β a
denotes the type
of functions f
with the property that, for each a : α
, f a
is an element of β a
. In other words, the type of the value
returned by f
depends on its input.
Notice that (a : α) → β
makes sense for any expression β : Type
. When the value of β
depends on a
(as does, for
example, the expression β a
in the previous paragraph),
(a : α) → β
denotes a dependent function type. When β
doesn't
depend on a
, (a : α) → β
is no different from the type
α → β
. Indeed, in dependent type theory (and in Lean), α → β
is just notation for (a : α) → β
when β
does not depend on a
.
Returning to the example of lists, you can use the command #check
to
inspect the type of the following List
functions. The @
symbol
and the difference between the round and curly braces will be
explained momentarily.
#check @List.cons  {α : Type u_1} → α → List α → List α
#check @List.nil  {α : Type u_1} → List α
#check @List.length  {α : Type u_1} → List α → Nat
#check @List.append  {α : Type u_1} → List α → List α → List α
Just as dependent function types (a : α) → β a
generalize the
notion of a function type α → β
by allowing β
to depend on
α
, dependent Cartesian product types (a : α) × β a
generalize
the Cartesian product α × β
in the same way. Dependent products
are also called sigma types, and you can also write them as
Σ a : α, β a
. You can use ⟨a, b⟩
or Sigma.mk a b
to create a
dependent pair. The ⟨
and ⟩
characters may be typed with
\langle
and \rangle
or \<
and \>
, respectively.
universe u v
def f (α : Type u) (β : α → Type v) (a : α) (b : β a) : (a : α) × β a :=
⟨a, b⟩
def g (α : Type u) (β : α → Type v) (a : α) (b : β a) : Σ a : α, β a :=
Sigma.mk a b
def h1 (x : Nat) : Nat :=
(f Type (fun α => α) Nat x).2
#eval h1 5  5
def h2 (x : Nat) : Nat :=
(g Type (fun α => α) Nat x).2
#eval h2 5  5
The functions f
and g
above denote the same function.
Implicit Arguments
Suppose we have an implementation of lists as:
universe u
def Lst (α : Type u) : Type u := List α
def Lst.cons (α : Type u) (a : α) (as : Lst α) : Lst α := List.cons a as
def Lst.nil (α : Type u) : Lst α := List.nil
def Lst.append (α : Type u) (as bs : Lst α) : Lst α := List.append as bs
#check Lst  Lst.{u} (α : Type u) : Type u
#check Lst.cons  Lst.cons.{u} (α : Type u) (a : α) (as : Lst α) : Lst α
#check Lst.nil  Lst.nil.{u} (α : Type u) : Lst α
#check Lst.append  Lst.append.{u} (α : Type u) (as bs : Lst α) : Lst α
Then, you can construct lists of Nat
as follows.
universe u
def Lst (α : Type u) : Type u := List α
def Lst.cons (α : Type u) (a : α) (as : Lst α) : Lst α := List.cons a as
def Lst.nil (α : Type u) : Lst α := List.nil
def Lst.append (α : Type u) (as bs : Lst α) : Lst α := List.append as bs
#check Lst  Type u_1 → Type u_1
#check Lst.cons  (α : Type u_1) → α → Lst α → Lst α
#check Lst.nil  (α : Type u_1) → Lst α
#check Lst.append  (α : Type u_1) → Lst α → Lst α → Lst α
#check Lst.cons Nat 0 (Lst.nil Nat)
def as : Lst Nat := Lst.nil Nat
def bs : Lst Nat := Lst.cons Nat 5 (Lst.nil Nat)
#check Lst.append Nat as bs
Because the constructors are polymorphic over types, we have to insert
the type Nat
as an argument repeatedly. But this information is
redundant: one can infer the argument α
in
Lst.cons Nat 5 (Lst.nil Nat)
from the fact that the second argument, 5
, has
type Nat
. One can similarly infer the argument in Lst.nil Nat
, not
from anything else in that expression, but from the fact that it is
sent as an argument to the function Lst.cons
, which expects an element
of type Lst α
in that position.
This is a central feature of dependent type theory: terms carry a lot
of information, and often some of that information can be inferred
from the context. In Lean, one uses an underscore, _
, to specify
that the system should fill in the information automatically. This is
known as an "implicit argument."
universe u
def Lst (α : Type u) : Type u := List α
def Lst.cons (α : Type u) (a : α) (as : Lst α) : Lst α := List.cons a as
def Lst.nil (α : Type u) : Lst α := List.nil
def Lst.append (α : Type u) (as bs : Lst α) : Lst α := List.append as bs
#check Lst  Type u_1 → Type u_1
#check Lst.cons  (α : Type u_1) → α → Lst α → Lst α
#check Lst.nil  (α : Type u_1) → Lst α
#check Lst.append  (α : Type u_1) → Lst α → Lst α → Lst α
#check Lst.cons _ 0 (Lst.nil _)
def as : Lst Nat := Lst.nil _
def bs : Lst Nat := Lst.cons _ 5 (Lst.nil _)
#check Lst.append _ as bs
It is still tedious, however, to type all these underscores. When a function takes an argument that can generally be inferred from context, Lean allows you to specify that this argument should, by default, be left implicit. This is done by putting the arguments in curly braces, as follows:
universe u
def Lst (α : Type u) : Type u := List α
def Lst.cons {α : Type u} (a : α) (as : Lst α) : Lst α := List.cons a as
def Lst.nil {α : Type u} : Lst α := List.nil
def Lst.append {α : Type u} (as bs : Lst α) : Lst α := List.append as bs
#check Lst.cons 0 Lst.nil
def as : Lst Nat := Lst.nil
def bs : Lst Nat := Lst.cons 5 Lst.nil
#check Lst.append as bs
All that has changed are the braces around α : Type u
in the
declaration of the variables. We can also use this device in function
definitions:
universe u
def ident {α : Type u} (x : α) := x
#check ident  ?m → ?m
#check ident 1  Nat
#check ident "hello"  String
#check @ident  {α : Type u_1} → α → α
This makes the first argument to ident
implicit. Notationally,
this hides the specification of the type, making it look as though
ident
simply takes an argument of any type. In fact, the function
id
is defined in the standard library in exactly this way. We have
chosen a nontraditional name here only to avoid a clash of names.
Variables can also be specified as implicit when they are declared with
the variable
command:
universe u
section
variable {α : Type u}
variable (x : α)
def ident := x
end
#check ident
#check ident 4
#check ident "hello"
This definition of ident
here has the same effect as the one
above.
Lean has very complex mechanisms for instantiating implicit arguments,
and we will see that they can be used to infer function types,
predicates, and even proofs. The process of instantiating these
"holes," or "placeholders," in a term is often known as
elaboration. The presence of implicit arguments means that at times
there may be insufficient information to fix the meaning of an
expression precisely. An expression like id
or List.nil
is
said to be polymorphic, because it can take on different meanings in
different contexts.
One can always specify the type T
of an expression e
by
writing (e : T)
. This instructs Lean's elaborator to use the value
T
as the type of e
when trying to resolve implicit
arguments. In the second pair of examples below, this mechanism is
used to specify the desired types of the expressions id
and
List.nil
:
#check List.nil  List ?m
#check id  ?m → ?m
#check (List.nil : List Nat)  List Nat
#check (id : Nat → Nat)  Nat → Nat
Numerals are overloaded in Lean, but when the type of a numeral cannot
be inferred, Lean assumes, by default, that it is a natural number. So
the expressions in the first two #check
commands below are
elaborated in the same way, whereas the third #check
command
interprets 2
as an integer.
#check 2  Nat
#check (2 : Nat)  Nat
#check (2 : Int)  Int
Sometimes, however, we may find ourselves in a situation where we have
declared an argument to a function to be implicit, but now want to
provide the argument explicitly. If foo
is such a function, the
notation @foo
denotes the same function with all the arguments
made explicit.
#check @id  {α : Sort u_1} → α → α
#check @id Nat  Nat → Nat
#check @id Bool  Bool → Bool
#check @id Nat 1  Nat
#check @id Bool true  Bool
Notice that now the first #check
command gives the type of the
identifier, id
, without inserting any placeholders. Moreover, the
output indicates that the first argument is implicit.
Propositions and Proofs
By now, you have seen some ways of defining objects and functions in Lean. In this chapter, we will begin to explain how to write mathematical assertions and proofs in the language of dependent type theory as well.
Propositions as Types
One strategy for proving assertions about objects defined in the language of dependent type theory is to layer an assertion language and a proof language on top of the definition language. But there is no reason to multiply languages in this way: dependent type theory is flexible and expressive, and there is no reason we cannot represent assertions and proofs in the same general framework.
For example, we could introduce a new type, Prop
, to represent
propositions, and introduce constructors to build new propositions
from others.
def Implies (p q : Prop) : Prop := p → q
#check And  Prop → Prop → Prop
#check Or  Prop → Prop → Prop
#check Not  Prop → Prop
#check Implies  Prop → Prop → Prop
variable (p q r : Prop)
#check And p q  Prop
#check Or (And p q) r  Prop
#check Implies (And p q) (And q p)  Prop
We could then introduce, for each element p : Prop
, another type
Proof p
, for the type of proofs of p
. An "axiom" would be a
constant of such a type.
def Implies (p q : Prop) : Prop := p → q
structure Proof (p : Prop) : Type where
proof : p
#check Proof  Proof : Prop → Type
axiom and_comm (p q : Prop) : Proof (Implies (And p q) (And q p))
variable (p q : Prop)
#check and_comm p q  Proof (Implies (And p q) (And q p))
In addition to axioms, however, we would also need rules to build new proofs from old ones. For example, in many proof systems for propositional logic, we have the rule of modus ponens:
From a proof of
Implies p q
and a proof ofp
, we obtain a proof ofq
.
We could represent this as follows:
def Implies (p q : Prop) : Prop := p → q
structure Proof (p : Prop) : Type where
proof : p
axiom modus_ponens : (p q : Prop) → Proof (Implies p q) → Proof p → Proof q
Systems of natural deduction for propositional logic also typically rely on the following rule:
Suppose that, assuming
p
as a hypothesis, we have a proof ofq
. Then we can "cancel" the hypothesis and obtain a proof ofImplies p q
.
We could render this as follows:
def Implies (p q : Prop) : Prop := p → q
structure Proof (p : Prop) : Type where
proof : p
axiom implies_intro : (p q : Prop) → (Proof p → Proof q) → Proof (Implies p q)
This approach would provide us with a reasonable way of building assertions and proofs.
Determining that an expression t
is a correct proof of assertion p
would then
simply be a matter of checking that t
has type Proof p
.
Some simplifications are possible, however. To start with, we can
avoid writing the term Proof
repeatedly by conflating Proof p
with p
itself. In other words, whenever we have p : Prop
, we
can interpret p
as a type, namely, the type of its proofs. We can
then read t : p
as the assertion that t
is a proof of p
.
Moreover, once we make this identification, the rules for implication
show that we can pass back and forth between Implies p q
and
p → q
. In other words, implication between propositions p
and q
corresponds to having a function that takes any element of p
to an
element of q
. As a result, the introduction of the connective
Implies
is entirely redundant: we can use the usual function space
constructor p → q
from dependent type theory as our notion of
implication.
This is the approach followed in the Calculus of Constructions, and
hence in Lean as well. The fact that the rules for implication in a
proof system for natural deduction correspond exactly to the rules
governing abstraction and application for functions is an instance of
the CurryHoward isomorphism, sometimes known as the
propositionsastypes paradigm. In fact, the type Prop
is
syntactic sugar for Sort 0
, the very bottom of the type hierarchy
described in the last chapter. Moreover, Type u
is also just
syntactic sugar for Sort (u+1)
. Prop
has some special
features, but like the other type universes, it is closed under the
arrow constructor: if we have p q : Prop
, then p → q : Prop
.
There are at least two ways of thinking about propositions as
types. To some who take a constructive view of logic and mathematics,
this is a faithful rendering of what it means to be a proposition: a
proposition p
represents a sort of data type, namely, a
specification of the type of data that constitutes a proof. A proof of
p
is then simply an object t : p
of the right type.
Those not inclined to this ideology can view it, rather, as a simple
coding trick. To each proposition p
we associate a type that is
empty if p
is false and has a single element, say *
, if p
is true. In the latter case, let us say that (the type associated
with) p
is inhabited. It just so happens that the rules for
function application and abstraction can conveniently help us keep
track of which elements of Prop
are inhabited. So constructing an
element t : p
tells us that p
is indeed true. You can think of
the inhabitant of p
as being the "fact that p
is true." A
proof of p → q
uses "the fact that p
is true" to obtain "the
fact that q
is true."
Indeed, if p : Prop
is any proposition, Lean's kernel treats any
two elements t1 t2 : p
as being definitionally equal, much the
same way as it treats (fun x => t) s
and t[s/x]
as
definitionally equal. This is known as proof irrelevance, and is
consistent with the interpretation in the last paragraph. It means
that even though we can treat proofs t : p
as ordinary objects in
the language of dependent type theory, they carry no information
beyond the fact that p
is true.
The two ways we have suggested thinking about the propositionsastypes paradigm differ in a fundamental way. From the constructive point of view, proofs are abstract mathematical objects that are denoted by suitable expressions in dependent type theory. In contrast, if we think in terms of the coding trick described above, then the expressions themselves do not denote anything interesting. Rather, it is the fact that we can write them down and check that they are welltyped that ensures that the proposition in question is true. In other words, the expressions themselves are the proofs.
In the exposition below, we will slip back and forth between these two ways of talking, at times saying that an expression "constructs" or "produces" or "returns" a proof of a proposition, and at other times simply saying that it "is" such a proof. This is similar to the way that computer scientists occasionally blur the distinction between syntax and semantics by saying, at times, that a program "computes" a certain function, and at other times speaking as though the program "is" the function in question.
In any case, all that really matters is the bottom line. To formally
express a mathematical assertion in the language of dependent type
theory, we need to exhibit a term p : Prop
. To prove that
assertion, we need to exhibit a term t : p
. Lean's task, as a
proof assistant, is to help us to construct such a term, t
, and to
verify that it is wellformed and has the correct type.
Working with Propositions as Types
In the propositionsastypes paradigm, theorems involving only →
can be proved using lambda abstraction and application. In Lean, the
theorem
command introduces a new theorem:
variable {p : Prop}
variable {q : Prop}
theorem t1 : p → q → p := fun hp : p => fun hq : q => hp
Compare this proof to the expression fun x : α => fun y : β => x
of type α → β → α
, where α
and β
are data types.
This describes the function that takes arguments x
and y
of type α
and β
, respectively, and returns x
.
The proof of t1
has the same form, the only difference being that
p
and q
are elements of Prop
rather than Type
.
Intuitively, our proof of
p → q → p
assumes p
and q
are true, and uses the first
hypothesis (trivially) to establish that the conclusion, p
, is
true.
Note that the theorem
command is really a version of the
def
command: under the propositions and types
correspondence, proving the theorem p → q → p
is really the same
as defining an element of the associated type. To the kernel type
checker, there is no difference between the two.
There are a few pragmatic differences between definitions and theorems, however. In normal circumstances, it is never necessary to unfold the "definition" of a theorem; by proof irrelevance, any two proofs of that theorem are definitionally equal. Once the proof of a theorem is complete, typically we only need to know that the proof exists; it doesn't matter what the proof is. In light of that fact, Lean tags proofs as irreducible, which serves as a hint to the parser (more precisely, the elaborator) that there is generally no need to unfold it when processing a file. In fact, Lean is generally able to process and check proofs in parallel, since assessing the correctness of one proof does not require knowing the details of another.
As with definitions, the #print
command will show you the proof of
a theorem.
variable {p : Prop}
variable {q : Prop}
theorem t1 : p → q → p := fun hp : p => fun hq : q => hp
#print t1
Notice that the lambda abstractions hp : p
and hq : q
can be
viewed as temporary assumptions in the proof of t1
. Lean also
allows us to specify the type of the final term hp
, explicitly,
with a show
statement.
variable {p : Prop}
variable {q : Prop}
theorem t1 : p → q → p :=
fun hp : p =>
fun hq : q =>
show p from hp
Adding such extra information can improve the clarity of a proof and
help detect errors when writing a proof. The show
command does
nothing more than annotate the type, and, internally, all the
presentations of t1
that we have seen produce the same term.
As with ordinary definitions, we can move the lambdaabstracted variables to the left of the colon:
variable {p : Prop}
variable {q : Prop}
theorem t1 (hp : p) (hq : q) : p := hp
#print t1  p → q → p
Now we can apply the theorem t1
just as a function application.
variable {p : Prop}
variable {q : Prop}
theorem t1 (hp : p) (hq : q) : p := hp
axiom hp : p
theorem t2 : q → p := t1 hp
Here, the axiom
declaration postulates the existence of an
element of the given type and may compromise logical consistency. For
example, we can use it to postulate the empty type False
has an
element.
axiom unsound : False
 Everything follows from false
theorem ex : 1 = 0 :=
False.elim unsound
Declaring an "axiom" hp : p
is tantamount to declaring that p
is true, as witnessed by hp
. Applying the theorem
t1 : p → q → p
to the fact hp : p
that p
is true yields the theorem
t1 hp : q → p
.
Recall that we can also write theorem t1
as follows:
theorem t1 {p q : Prop} (hp : p) (hq : q) : p := hp
#print t1
The type of t1
is now ∀ {p q : Prop}, p → q → p
. We can read
this as the assertion "for every pair of propositions p q
, we have
p → q → p
." For example, we can move all parameters to the right
of the colon:
theorem t1 : ∀ {p q : Prop}, p → q → p :=
fun {p q : Prop} (hp : p) (hq : q) => hp
If p
and q
have been declared as variables, Lean will
generalize them for us automatically:
variable {p q : Prop}
theorem t1 : p → q → p := fun (hp : p) (hq : q) => hp
In fact, by the propositionsastypes correspondence, we can declare
the assumption hp
that p
holds, as another variable:
variable {p q : Prop}
variable (hp : p)
theorem t1 : q → p := fun (hq : q) => hp
Lean detects that the proof uses hp
and automatically adds
hp : p
as a premise. In all cases, the command #print t1
still yields
∀ p q : Prop, p → q → p
. Remember that this type can just as well
be written ∀ (p q : Prop) (hp : p) (hq : q), p
, since the arrow
denotes nothing more than an arrow type in which the target does not
depend on the bound variable.
When we generalize t1
in such a way, we can then apply it to
different pairs of propositions, to obtain different instances of the
general theorem.
theorem t1 (p q : Prop) (hp : p) (hq : q) : p := hp
variable (p q r s : Prop)
#check t1 p q  p → q → p
#check t1 r s  r → s → r
#check t1 (r → s) (s → r)  (r → s) → (s → r) → r → s
variable (h : r → s)
#check t1 (r → s) (s → r) h  (s → r) → r → s
Once again, using the propositionsastypes correspondence, the
variable h
of type r → s
can be viewed as the hypothesis, or
premise, that r → s
holds.
As another example, let us consider the composition function discussed in the last chapter, now with propositions instead of types.
variable (p q r s : Prop)
theorem t2 (h₁ : q → r) (h₂ : p → q) : p → r :=
fun h₃ : p =>
show r from h₁ (h₂ h₃)
As a theorem of propositional logic, what does t2
say?
Note that it is often useful to use numeric unicode subscripts,
entered as \0
, \1
, \2
, ..., for hypotheses, as we did in
this example.
Propositional Logic
Lean defines all the standard logical connectives and notation. The propositional connectives come with the following notation:
Ascii  Unicode  Editor shortcut  Definition 

True  True  
False  False  
Not  ¬  \not , \neg  Not 
/\  ∧  \and  And 
\/  ∨  \or  Or 
>  →  \to , \r , \imp  
<>  ↔  \iff , \lr  Iff 
They all take values in Prop
.
variable (p q : Prop)
#check p → q → p ∧ q
#check ¬p → p ↔ False
#check p ∨ q → q ∨ p
The order of operations is as follows: unary negation ¬
binds most
strongly, then ∧
, then ∨
, then →
, and finally ↔
. For
example, a ∧ b → c ∨ d ∧ e
means (a ∧ b) → (c ∨ (d ∧ e))
. Remember that →
associates to the right (nothing changes
now that the arguments are elements of Prop
, instead of some other
Type
), as do the other binary connectives. So if we have
p q r : Prop
, the expression p → q → r
reads "if p
, then if q
,
then r
." This is just the "curried" form of p ∧ q → r
.
In the last chapter we observed that lambda abstraction can be viewed
as an "introduction rule" for →
. In the current setting, it shows
how to "introduce" or establish an implication. Application can be
viewed as an "elimination rule," showing how to "eliminate" or use an
implication in a proof. The other propositional connectives are
defined in Lean's library in the file Prelude.core
(see
importing files for more information on the library
hierarchy), and each connective comes with its canonical introduction
and elimination rules.
Conjunction
The expression And.intro h1 h2
builds a proof of p ∧ q
using
proofs h1 : p
and h2 : q
. It is common to describe
And.intro
as the andintroduction rule. In the next example we
use And.intro
to create a proof of p → q → p ∧ q
.
variable (p q : Prop)
example (hp : p) (hq : q) : p ∧ q := And.intro hp hq
#check fun (hp : p) (hq : q) => And.intro hp hq
The example
command states a theorem without naming it or storing
it in the permanent context. Essentially, it just checks that the
given term has the indicated type. It is convenient for illustration,
and we will use it often.
The expression And.left h
creates a proof of p
from a proof
h : p ∧ q
. Similarly, And.right h
is a proof of q
. They
are commonly known as the left and right andelimination rules.
variable (p q : Prop)
example (h : p ∧ q) : p := And.left h
example (h : p ∧ q) : q := And.right h
We can now prove p ∧ q → q ∧ p
with the following proof term.
variable (p q : Prop)
example (h : p ∧ q) : q ∧ p :=
And.intro (And.right h) (And.left h)
Notice that andintroduction and andelimination are similar to the
pairing and projection operations for the Cartesian product. The
difference is that given hp : p
and hq : q
, And.intro hp hq
has type p ∧ q : Prop
, while Prod hp hq
has type
p × q : Type
. The similarity between ∧
and ×
is another instance
of the CurryHoward isomorphism, but in contrast to implication and
the function space constructor, ∧
and ×
are treated separately
in Lean. With the analogy, however, the proof we have just constructed
is similar to a function that swaps the elements of a pair.
We will see in Chapter Structures and Records that certain
types in Lean are structures, which is to say, the type is defined
with a single canonical constructor which builds an element of the
type from a sequence of suitable arguments. For every p q : Prop
,
p ∧ q
is an example: the canonical way to construct an element is
to apply And.intro
to suitable arguments hp : p
and
hq : q
. Lean allows us to use anonymous constructor notation
⟨arg1, arg2, ...⟩
in situations like these, when the relevant type is an
inductive type and can be inferred from the context. In particular, we
can often write ⟨hp, hq⟩
instead of And.intro hp hq
:
variable (p q : Prop)
variable (hp : p) (hq : q)
#check (⟨hp, hq⟩ : p ∧ q)
These angle brackets are obtained by typing \<
and \>
, respectively.
Lean provides another useful syntactic gadget. Given an expression
e
of an inductive type Foo
(possibly applied to some
arguments), the notation e.bar
is shorthand for Foo.bar e
.
This provides a convenient way of accessing functions without opening
a namespace. For example, the following two expressions mean the same
thing:
variable (xs : List Nat)
#check List.length xs
#check xs.length
As a result, given h : p ∧ q
, we can write h.left
for
And.left h
and h.right
for And.right h
. We can therefore
rewrite the sample proof above conveniently as follows:
variable (p q : Prop)
example (h : p ∧ q) : q ∧ p :=
⟨h.right, h.left⟩
There is a fine line between brevity and obfuscation, and omitting
information in this way can sometimes make a proof harder to read. But
for straightforward constructions like the one above, when the type of
h
and the goal of the construction are salient, the notation is
clean and effective.
It is common to iterate constructions like "And." Lean also allows you to flatten nested constructors that associate to the right, so that these two proofs are equivalent:
variable (p q : Prop)
example (h : p ∧ q) : q ∧ p ∧ q :=
⟨h.right, ⟨h.left, h.right⟩⟩
example (h : p ∧ q) : q ∧ p ∧ q :=
⟨h.right, h.left, h.right⟩
This is often useful as well.
Disjunction
The expression Or.intro_left q hp
creates a proof of p ∨ q
from a proof hp : p
. Similarly, Or.intro_right p hq
creates a
proof for p ∨ q
using a proof hq : q
. These are the left and
right orintroduction rules.
variable (p q : Prop)
example (hp : p) : p ∨ q := Or.intro_left q hp
example (hq : q) : p ∨ q := Or.intro_right p hq
The orelimination rule is slightly more complicated. The idea is
that we can prove r
from p ∨ q
, by showing that r
follows
from p
and that r
follows from q
. In other words, it is a
proof by cases. In the expression Or.elim hpq hpr hqr
, Or.elim
takes three arguments, hpq : p ∨ q
, hpr : p → r
and
hqr : q → r
, and produces a proof of r
. In the following example, we use
Or.elim
to prove p ∨ q → q ∨ p
.
variable (p q r : Prop)
example (h : p ∨ q) : q ∨ p :=
Or.elim h
(fun hp : p =>
show q ∨ p from Or.intro_right q hp)
(fun hq : q =>
show q ∨ p from Or.intro_left p hq)
In most cases, the first argument of Or.intro_right
and
Or.intro_left
can be inferred automatically by Lean. Lean
therefore provides Or.inr
and Or.inl
which can be viewed as
shorthand for Or.intro_right _
and Or.intro_left _
. Thus the
proof term above could be written more concisely:
variable (p q r : Prop)
example (h : p ∨ q) : q ∨ p :=
Or.elim h (fun hp => Or.inr hp) (fun hq => Or.inl hq)
Notice that there is enough information in the full expression for
Lean to infer the types of hp
and hq
as well. But using the
type annotations in the longer version makes the proof more readable,
and can help catch and debug errors.
Because Or
has two constructors, we cannot use anonymous
constructor notation. But we can still write h.elim
instead of
Or.elim h
:
variable (p q r : Prop)
example (h : p ∨ q) : q ∨ p :=
h.elim (fun hp => Or.inr hp) (fun hq => Or.inl hq)
Once again, you should exercise judgment as to whether such abbreviations enhance or diminish readability.
Negation and Falsity
Negation, ¬p
, is actually defined to be p → False
, so we
obtain ¬p
by deriving a contradiction from p
. Similarly, the
expression hnp hp
produces a proof of False
from hp : p
and hnp : ¬p
. The next example uses both these rules to produce a
proof of (p → q) → ¬q → ¬p
. (The symbol ¬
is produced by
typing \not
or \neg
.)
variable (p q : Prop)
example (hpq : p → q) (hnq : ¬q) : ¬p :=
fun hp : p =>
show False from hnq (hpq hp)
The connective False
has a single elimination rule,
False.elim
, which expresses the fact that anything follows from a
contradiction. This rule is sometimes called ex falso (short for ex
falso sequitur quodlibet), or the principle of explosion.
variable (p q : Prop)
example (hp : p) (hnp : ¬p) : q := False.elim (hnp hp)
The arbitrary fact, q
, that follows from falsity is an implicit
argument in False.elim
and is inferred automatically. This
pattern, deriving an arbitrary fact from contradictory hypotheses, is
quite common, and is represented by absurd
.
variable (p q : Prop)
example (hp : p) (hnp : ¬p) : q := absurd hp hnp
Here, for example, is a proof of ¬p → q → (q → p) → r
:
variable (p q r : Prop)
example (hnp : ¬p) (hq : q) (hqp : q → p) : r :=
absurd (hqp hq) hnp
Incidentally, just as False
has only an elimination rule, True
has only an introduction rule, True.intro : true
. In other words,
True
is simply true, and has a canonical proof, True.intro
.
Logical Equivalence
The expression Iff.intro h1 h2
produces a proof of p ↔ q
from
h1 : p → q
and h2 : q → p
. The expression Iff.mp h
produces a proof of p → q
from h : p ↔ q
. Similarly,
Iff.mpr h
produces a proof of q → p
from h : p ↔ q
. Here is a proof
of p ∧ q ↔ q ∧ p
:
variable (p q : Prop)
theorem and_swap : p ∧ q ↔ q ∧ p :=
Iff.intro
(fun h : p ∧ q =>
show q ∧ p from And.intro (And.right h) (And.left h))
(fun h : q ∧ p =>
show p ∧ q from And.intro (And.right h) (And.left h))
#check and_swap p q  p ∧ q ↔ q ∧ p
variable (h : p ∧ q)
example : q ∧ p := Iff.mp (and_swap p q) h
We can use the anonymous constructor notation to construct a proof of
p ↔ q
from proofs of the forward and backward directions, and we
can also use .
notation with mp
and mpr
. The previous
examples can therefore be written concisely as follows:
variable (p q : Prop)
theorem and_swap : p ∧ q ↔ q ∧ p :=
⟨ fun h => ⟨h.right, h.left⟩, fun h => ⟨h.right, h.left⟩ ⟩
example (h : p ∧ q) : q ∧ p := (and_swap p q).mp h
Introducing Auxiliary Subgoals
This is a good place to introduce another device Lean offers to help
structure long proofs, namely, the have
construct, which
introduces an auxiliary subgoal in a proof. Here is a small example,
adapted from the last section:
variable (p q : Prop)
example (h : p ∧ q) : q ∧ p :=
have hp : p := h.left
have hq : q := h.right
show q ∧ p from And.intro hq hp
Internally, the expression have h : p := s; t
produces the term
(fun (h : p) => t) s
. In other words, s
is a proof of p
,
t
is a proof of the desired conclusion assuming h : p
, and the
two are combined by a lambda abstraction and application. This simple
device is extremely useful when it comes to structuring long proofs,
since we can use intermediate have
's as stepping stones leading to
the final goal.
Lean also supports a structured way of reasoning backwards from a goal, which models the "suffices to show" construction in ordinary mathematics. The next example simply permutes the last two lines in the previous proof.
variable (p q : Prop)
example (h : p ∧ q) : q ∧ p :=
have hp : p := h.left
suffices hq : q from And.intro hq hp
show q from And.right h
Writing suffices hq : q
leaves us with two goals. First, we have
to show that it indeed suffices to show q
, by proving the original
goal of q ∧ p
with the additional hypothesis hq : q
. Finally,
we have to show q
.
Classical Logic
The introduction and elimination rules we have seen so far are all
constructive, which is to say, they reflect a computational
understanding of the logical connectives based on the
propositionsastypes correspondence. Ordinary classical logic adds to
this the law of the excluded middle, p ∨ ¬p
. To use this
principle, you have to open the classical namespace.
open Classical
variable (p : Prop)
#check em p
Intuitively, the constructive "Or" is very strong: asserting p ∨ q
amounts to knowing which is the case. If RH
represents the Riemann
hypothesis, a classical mathematician is willing to assert
RH ∨ ¬RH
, even though we cannot yet assert either disjunct.
One consequence of the law of the excluded middle is the principle of doublenegation elimination:
open Classical
theorem dne {p : Prop} (h : ¬¬p) : p :=
Or.elim (em p)
(fun hp : p => hp)
(fun hnp : ¬p => absurd hnp h)
Doublenegation elimination allows one to prove any proposition,
p
, by assuming ¬p
and deriving false
, because that amounts
to proving ¬¬p
. In other words, doublenegation elimination allows
one to carry out a proof by contradiction, something which is not
generally possible in constructive logic. As an exercise, you might
try proving the converse, that is, showing that em
can be proved
from dne
.
The classical axioms also give you access to additional patterns of
proof that can be justified by appeal to em
. For example, one can
carry out a proof by cases:
open Classical
variable (p : Prop)
example (h : ¬¬p) : p :=
byCases
(fun h1 : p => h1)
(fun h1 : ¬p => absurd h1 h)
Or you can carry out a proof by contradiction:
open Classical
variable (p : Prop)
example (h : ¬¬p) : p :=
byContradiction
(fun h1 : ¬p =>
show False from h h1)
If you are not used to thinking constructively, it may take some time
for you to get a sense of where classical reasoning is used. It is
needed in the following example because, from a constructive
standpoint, knowing that p
and q
are not both true does not
necessarily tell you which one is false:
open Classical
variable (p q : Prop)
example (h : ¬(p ∧ q)) : ¬p ∨ ¬q :=
Or.elim (em p)
(fun hp : p =>
Or.inr
(show ¬q from
fun hq : q =>
h ⟨hp, hq⟩))
(fun hp : ¬p =>
Or.inl hp)
We will see later that there are situations in constructive logic where principles like excluded middle and doublenegation elimination are permissible, and Lean supports the use of classical reasoning in such contexts without relying on excluded middle.
The full list of axioms that are used in Lean to support classical reasoning are discussed in Axioms and Computation.
Examples of Propositional Validities
Lean's standard library contains proofs of many valid statements of propositional logic, all of which you are free to use in proofs of your own. The following list includes a number of common identities.
Commutativity:
p ∧ q ↔ q ∧ p
p ∨ q ↔ q ∨ p
Associativity:
(p ∧ q) ∧ r ↔ p ∧ (q ∧ r)
(p ∨ q) ∨ r ↔ p ∨ (q ∨ r)
Distributivity:
p ∧ (q ∨ r) ↔ (p ∧ q) ∨ (p ∧ r)
p ∨ (q ∧ r) ↔ (p ∨ q) ∧ (p ∨ r)
Other properties:
(p → (q → r)) ↔ (p ∧ q → r)
((p ∨ q) → r) ↔ (p → r) ∧ (q → r)
¬(p ∨ q) ↔ ¬p ∧ ¬q
¬p ∨ ¬q → ¬(p ∧ q)
¬(p ∧ ¬p)
p ∧ ¬q → ¬(p → q)
¬p → (p → q)
(¬p ∨ q) → (p → q)
p ∨ False ↔ p
p ∧ False ↔ False
¬(p ↔ ¬p)
(p → q) → (¬q → ¬p)
These require classical reasoning:
(p → r ∨ s) → ((p → r) ∨ (p → s))
¬(p ∧ q) → ¬p ∨ ¬q
¬(p → q) → p ∧ ¬q
(p → q) → (¬p ∨ q)
(¬q → ¬p) → (p → q)
p ∨ ¬p
(((p → q) → p) → p)
The sorry
identifier magically produces a proof of anything, or
provides an object of any data type at all. Of course, it is unsound
as a proof method  for example, you can use it to prove False

and Lean produces severe warnings when files use or import theorems
which depend on it. But it is very useful for building long proofs
incrementally. Start writing the proof from the top down, using
sorry
to fill in subproofs. Make sure Lean accepts the term with
all the sorry
's; if not, there are errors that you need to
correct. Then go back and replace each sorry
with an actual proof,
until no more remain.
Here is another useful trick. Instead of using sorry
, you can use
an underscore _
as a placeholder. Recall this tells Lean that
the argument is implicit, and should be filled in automatically. If
Lean tries to do so and fails, it returns with an error message "don't
know how to synthesize placeholder," followed by the type of
the term it is expecting, and all the objects and hypotheses available
in the context. In other words, for each unresolved placeholder, Lean
reports the subgoal that needs to be filled at that point. You can
then construct a proof by incrementally filling in these placeholders.
For reference, here are two sample proofs of validities taken from the list above.
open Classical
 distributivity
example (p q r : Prop) : p ∧ (q ∨ r) ↔ (p ∧ q) ∨ (p ∧ r) :=
Iff.intro
(fun h : p ∧ (q ∨ r) =>
have hp : p := h.left
Or.elim (h.right)
(fun hq : q =>
show (p ∧ q) ∨ (p ∧ r) from Or.inl ⟨hp, hq⟩)
(fun hr : r =>
show (p ∧ q) ∨ (p ∧ r) from Or.inr ⟨hp, hr⟩))
(fun h : (p ∧ q) ∨ (p ∧ r) =>
Or.elim h
(fun hpq : p ∧ q =>
have hp : p := hpq.left
have hq : q := hpq.right
show p ∧ (q ∨ r) from ⟨hp, Or.inl hq⟩)
(fun hpr : p ∧ r =>
have hp : p := hpr.left
have hr : r := hpr.right
show p ∧ (q ∨ r) from ⟨hp, Or.inr hr⟩))
 an example that requires classical reasoning
example (p q : Prop) : ¬(p ∧ ¬q) → (p → q) :=
fun h : ¬(p ∧ ¬q) =>
fun hp : p =>
show q from
Or.elim (em q)
(fun hq : q => hq)
(fun hnq : ¬q => absurd (And.intro hp hnq) h)
Exercises
Prove the following identities, replacing the "sorry" placeholders with actual proofs.
variable (p q r : Prop)
 commutativity of ∧ and ∨
example : p ∧ q ↔ q ∧ p := sorry
example : p ∨ q ↔ q ∨ p := sorry
 associativity of ∧ and ∨
example : (p ∧ q) ∧ r ↔ p ∧ (q ∧ r) := sorry
example : (p ∨ q) ∨ r ↔ p ∨ (q ∨ r) := sorry
 distributivity
example : p ∧ (q ∨ r) ↔ (p ∧ q) ∨ (p ∧ r) := sorry
example : p ∨ (q ∧ r) ↔ (p ∨ q) ∧ (p ∨ r) := sorry
 other properties
example : (p → (q → r)) ↔ (p ∧ q → r) := sorry
example : ((p ∨ q) → r) ↔ (p → r) ∧ (q → r) := sorry
example : ¬(p ∨ q) ↔ ¬p ∧ ¬q := sorry
example : ¬p ∨ ¬q → ¬(p ∧ q) := sorry
example : ¬(p ∧ ¬p) := sorry
example : p ∧ ¬q → ¬(p → q) := sorry
example : ¬p → (p → q) := sorry
example : (¬p ∨ q) → (p → q) := sorry
example : p ∨ False ↔ p := sorry
example : p ∧ False ↔ False := sorry
example : (p → q) → (¬q → ¬p) := sorry
Prove the following identities, replacing the "sorry" placeholders with actual proofs. These require classical reasoning.
open Classical
variable (p q r : Prop)
example : (p → q ∨ r) → ((p → q) ∨ (p → r)) := sorry
example : ¬(p ∧ q) → ¬p ∨ ¬q := sorry
example : ¬(p → q) → p ∧ ¬q := sorry
example : (p → q) → (¬p ∨ q) := sorry
example : (¬q → ¬p) → (p → q) := sorry
example : p ∨ ¬p := sorry
example : (((p → q) → p) → p) := sorry
Prove ¬(p ↔ ¬p)
without using classical logic.
Quantifiers and Equality
The last chapter introduced you to methods that construct proofs of statements involving the propositional connectives. In this chapter, we extend the repertoire of logical constructions to include the universal and existential quantifiers, and the equality relation.
The Universal Quantifier
Notice that if α
is any type, we can represent a unary predicate
p
on α
as an object of type α → Prop
. In that case, given
x : α
, p x
denotes the assertion that p
holds of
x
. Similarly, an object r : α → α → Prop
denotes a binary
relation on α
: given x y : α
, r x y
denotes the assertion
that x
is related to y
.
The universal quantifier, ∀ x : α, p x
is supposed to denote the
assertion that "for every x : α
, p x
" holds. As with the
propositional connectives, in systems of natural deduction, "forall"
is governed by an introduction and elimination rule. Informally, the
introduction rule states:
Given a proof of
p x
, in a context wherex : α
is arbitrary, we obtain a proof∀ x : α, p x
.
The elimination rule states:
Given a proof
∀ x : α, p x
and any termt : α
, we obtain a proof ofp t
.
As was the case for implication, the propositionsastypes interpretation now comes into play. Remember the introduction and elimination rules for dependent arrow types:
Given a term
t
of typeβ x
, in a context wherex : α
is arbitrary, we have(fun x : α => t) : (x : α) → β x
.
The elimination rule states:
Given a term
s : (x : α) → β x
and any termt : α
, we haves t : β t
.
In the case where p x
has type Prop
, if we replace
(x : α) → β x
with ∀ x : α, p x
, we can read these as the correct rules
for building proofs involving the universal quantifier.
The Calculus of Constructions therefore identifies dependent arrow
types with forallexpressions in this way. If p
is any expression,
∀ x : α, p
is nothing more than alternative notation for
(x : α) → p
, with the idea that the former is more natural than the latter
in cases where p
is a proposition. Typically, the expression p
will depend on x : α
. Recall that, in the case of ordinary
function spaces, we could interpret α → β
as the special case of
(x : α) → β
in which β
does not depend on x
. Similarly, we
can think of an implication p → q
between propositions as the
special case of ∀ x : p, q
in which the expression q
does not
depend on x
.
Here is an example of how the propositionsastypes correspondence gets put into practice.
example (α : Type) (p q : α → Prop) : (∀ x : α, p x ∧ q x) → ∀ y : α, p y :=
fun h : ∀ x : α, p x ∧ q x =>
fun y : α =>
show p y from (h y).left
As a notational convention, we give the universal quantifier the
widest scope possible, so parentheses are needed to limit the
quantifier over x
to the hypothesis in the example above. The
canonical way to prove ∀ y : α, p y
is to take an arbitrary y
,
and prove p y
. This is the introduction rule. Now, given that
h
has type ∀ x : α, p x ∧ q x
, the expression h y
has type
p y ∧ q y
. This is the elimination rule. Taking the left conjunct
gives the desired conclusion, p y
.
Remember that expressions which differ up to renaming of bound
variables are considered to be equivalent. So, for example, we could
have used the same variable, x
, in both the hypothesis and
conclusion, and instantiated it by a different variable, z
, in the
proof:
example (α : Type) (p q : α → Prop) : (∀ x : α, p x ∧ q x) → ∀ x : α, p x :=
fun h : ∀ x : α, p x ∧ q x =>
fun z : α =>
show p z from And.left (h z)
As another example, here is how we can express the fact that a relation, r
, is transitive:
variable (α : Type) (r : α → α → Prop)
variable (trans_r : ∀ x y z, r x y → r y z → r x z)
variable (a b c : α)
variable (hab : r a b) (hbc : r b c)
#check trans_r  ∀ (x y z : α), r x y → r y z → r x z
#check trans_r a b c  r a b → r b c → r a c
#check trans_r a b c hab  r b c → r a c
#check trans_r a b c hab hbc  r a c
Think about what is going on here. When we instantiate trans_r
at
the values a b c
, we end up with a proof of r a b → r b c → r a c
.
Applying this to the "hypothesis" hab : r a b
, we get a proof
of the implication r b c → r a c
. Finally, applying it to the
hypothesis hbc
yields a proof of the conclusion r a c
.
In situations like this, it can be tedious to supply the arguments
a b c
, when they can be inferred from hab hbc
. For that reason, it
is common to make these arguments implicit:
variable (α : Type) (r : α → α → Prop)
variable (trans_r : ∀ {x y z}, r x y → r y z → r x z)
variable (a b c : α)
variable (hab : r a b) (hbc : r b c)
#check trans_r
#check trans_r hab
#check trans_r hab hbc
The advantage is that we can simply write trans_r hab hbc
as a
proof of r a c
. A disadvantage is that Lean does not have enough
information to infer the types of the arguments in the expressions
trans_r
and trans_r hab
. The output of the first #check
command is r ?m.1 ?m.2 → r ?m.2 ?m.3 → r ?m.1 ?m.3
, indicating
that the implicit arguments are unspecified in this case.
Here is an example of how we can carry out elementary reasoning with an equivalence relation:
variable (α : Type) (r : α → α → Prop)
variable (refl_r : ∀ x, r x x)
variable (symm_r : ∀ {x y}, r x y → r y x)
variable (trans_r : ∀ {x y z}, r x y → r y z → r x z)
example (a b c d : α) (hab : r a b) (hcb : r c b) (hcd : r c d) : r a d :=
trans_r (trans_r hab (symm_r hcb)) hcd
To get used to using universal quantifiers, you should try some of the exercises at the end of this section.
It is the typing rule for dependent arrow types, and the universal
quantifier in particular, that distinguishes Prop
from other
types. Suppose we have α : Sort i
and β : Sort j
, where the
expression β
may depend on a variable x : α
. Then
(x : α) → β
is an element of Sort (imax i j)
, where imax i j
is the
maximum of i
and j
if j
is not 0, and 0 otherwise.
The idea is as follows. If j
is not 0
, then (x : α) → β
is
an element of Sort (max i j)
. In other words, the type of
dependent functions from α
to β
"lives" in the universe whose
index is the maximum of i
and j
. Suppose, however, that β
is of Sort 0
, that is, an element of Prop
. In that case,
(x : α) → β
is an element of Sort 0
as well, no matter which
type universe α
lives in. In other words, if β
is a
proposition depending on α
, then ∀ x : α, β
is again a
proposition. This reflects the interpretation of Prop
as the type
of propositions rather than data, and it is what makes Prop
impredicative.
The term "predicative" stems from foundational developments around the
turn of the twentieth century, when logicians such as Poincaré and
Russell blamed settheoretic paradoxes on the "vicious circles" that
arise when we define a property by quantifying over a collection that
includes the very property being defined. Notice that if α
is any
type, we can form the type α → Prop
of all predicates on α
(the "power type of α
"). The impredicativity of Prop
means that we
can form propositions that quantify over α → Prop
. In particular,
we can define predicates on α
by quantifying over all predicates
on α
, which is exactly the type of circularity that was once
considered problematic.
Equality
Let us now turn to one of the most fundamental relations defined in Lean's library, namely, the equality relation. In Chapter Inductive Types, we will explain how equality is defined from the primitives of Lean's logical framework. In the meanwhile, here we explain how to use it.
Of course, a fundamental property of equality is that it is an equivalence relation:
#check Eq.refl  Eq.refl.{u_1} {α : Sort u_1} (a : α) : a = a
#check Eq.symm  Eq.symm.{u} {α : Sort u} {a b : α} (h : a = b) : b = a
#check Eq.trans  Eq.trans.{u} {α : Sort u} {a b c : α} (h₁ : a = b) (h₂ : b = c) : a = c
We can make the output easier to read by telling Lean not to insert the implicit arguments (which are displayed here as metavariables).
universe u
#check @Eq.refl.{u}  @Eq.refl : ∀ {α : Sort u} (a : α), a = a
#check @Eq.symm.{u}  @Eq.symm : ∀ {α : Sort u} {a b : α}, a = b → b = a
#check @Eq.trans.{u}  @Eq.trans : ∀ {α : Sort u} {a b c : α}, a = b → b = c → a = c
The inscription .{u}
tells Lean to instantiate the constants at the universe u
.
Thus, for example, we can specialize the example from the previous section to the equality relation:
variable (α : Type) (a b c d : α)
variable (hab : a = b) (hcb : c = b) (hcd : c = d)
example : a = d :=
Eq.trans (Eq.trans hab (Eq.symm hcb)) hcd
We can also use the projection notation:
variable (α : Type) (a b c d : α)
variable (hab : a = b) (hcb : c = b) (hcd : c = d)
example : a = d := (hab.trans hcb.symm).trans hcd
Reflexivity is more powerful than it looks. Recall that terms in the Calculus of Constructions have a computational interpretation, and that the logical framework treats terms with a common reduct as the same. As a result, some nontrivial identities can be proved by reflexivity:
variable (α β : Type)
example (f : α → β) (a : α) : (fun x => f x) a = f a := Eq.refl _
example (a : α) (b : β) : (a, b).1 = a := Eq.refl _
example : 2 + 3 = 5 := Eq.refl _
This feature of the framework is so important that the library defines a notation rfl
for Eq.refl _
:
variable (α β : Type)
example (f : α → β) (a : α) : (fun x => f x) a = f a := rfl
example (a : α) (b : β) : (a, b).1 = a := rfl
example : 2 + 3 = 5 := rfl
Equality is much more than an equivalence relation, however. It has
the important property that every assertion respects the equivalence,
in the sense that we can substitute equal expressions without changing
the truth value. That is, given h1 : a = b
and h2 : p a
, we
can construct a proof for p b
using substitution:
Eq.subst h1 h2
.
example (α : Type) (a b : α) (p : α → Prop)
(h1 : a = b) (h2 : p a) : p b :=
Eq.subst h1 h2
example (α : Type) (a b : α) (p : α → Prop)
(h1 : a = b) (h2 : p a) : p b :=
h1 ▸ h2
The triangle in the second presentation is a macro built on top of
Eq.subst
and Eq.symm
, and you can enter it by typing \t
.
The rule Eq.subst
is used to define the following auxiliary rules,
which carry out more explicit substitutions. They are designed to deal
with applicative terms, that is, terms of form s t
. Specifically,
congrArg
can be used to replace the argument, congrFun
can be
used to replace the term that is being applied, and congr
can be
used to replace both at once.
variable (α : Type)
variable (a b : α)
variable (f g : α → Nat)
variable (h₁ : a = b)
variable (h₂ : f = g)
example : f a = f b := congrArg f h₁
example : f a = g a := congrFun h₂ a
example : f a = g b := congr h₂ h₁
Lean's library contains a large number of common identities, such as these:
variable (a b c : Nat)
example : a + 0 = a := Nat.add_zero a
example : 0 + a = a := Nat.zero_add a
example : a * 1 = a := Nat.mul_one a
example : 1 * a = a := Nat.one_mul a
example : a + b = b + a := Nat.add_comm a b
example : a + b + c = a + (b + c) := Nat.add_assoc a b c
example : a * b = b * a := Nat.mul_comm a b
example : a * b * c = a * (b * c) := Nat.mul_assoc a b c
example : a * (b + c) = a * b + a * c := Nat.mul_add a b c
example : a * (b + c) = a * b + a * c := Nat.left_distrib a b c
example : (a + b) * c = a * c + b * c := Nat.add_mul a b c
example : (a + b) * c = a * c + b * c := Nat.right_distrib a b c
Note that Nat.mul_add
and Nat.add_mul
are alternative names
for Nat.left_distrib
and Nat.right_distrib
, respectively. The
properties above are stated for the natural numbers (type Nat
).
Here is an example of a calculation in the natural numbers that uses substitution combined with associativity and distributivity.
example (x y : Nat) : (x + y) * (x + y) = x * x + y * x + x * y + y * y :=
have h1 : (x + y) * (x + y) = (x + y) * x + (x + y) * y :=
Nat.mul_add (x + y) x y
have h2 : (x + y) * (x + y) = x * x + y * x + (x * y + y * y) :=
(Nat.add_mul x y x) ▸ (Nat.add_mul x y y) ▸ h1
h2.trans (Nat.add_assoc (x * x + y * x) (x * y) (y * y)).symm
Notice that the second implicit parameter to Eq.subst
, which
provides the context in which the substitution is to occur, has type
α → Prop
. Inferring this predicate therefore requires an instance
of higherorder unification. In full generality, the problem of
determining whether a higherorder unifier exists is undecidable, and
Lean can at best provide imperfect and approximate solutions to the
problem. As a result, Eq.subst
doesn't always do what you want it
to. The macro h ▸ e
uses more effective heuristics for computing
this implicit parameter, and often succeeds in situations where
applying Eq.subst
fails.
Because equational reasoning is so common and important, Lean provides a number of mechanisms to carry it out more effectively. The next section offers syntax that allow you to write calculational proofs in a more natural and perspicuous way. But, more importantly, equational reasoning is supported by a term rewriter, a simplifier, and other kinds of automation. The term rewriter and simplifier are described briefly in the next section, and then in greater detail in the next chapter.
Calculational Proofs
A calculational proof is just a chain of intermediate results that are
meant to be composed by basic principles such as the transitivity of
equality. In Lean, a calculational proof starts with the keyword
calc
, and has the following syntax:
calc
<expr>_0 'op_1' <expr>_1 ':=' <proof>_1
'_' 'op_2' <expr>_2 ':=' <proof>_2
...
'_' 'op_n' <expr>_n ':=' <proof>_n
Note that the calc
relations all have the same indentation. Each
<proof>_i
is a proof for <expr>_{i1} op_i <expr>_i
.
We can also use _
in the first relation (right after <expr>_0
)
which is useful to align the sequence of relation/proof pairs:
calc <expr>_0
'_' 'op_1' <expr>_1 ':=' <proof>_1
'_' 'op_2' <expr>_2 ':=' <proof>_2
...
'_' 'op_n' <expr>_n ':=' <proof>_n
Here is an example:
variable (a b c d e : Nat)
variable (h1 : a = b)
variable (h2 : b = c + 1)
variable (h3 : c = d)
variable (h4 : e = 1 + d)
theorem T : a = e :=
calc
a = b := h1
_ = c + 1 := h2
_ = d + 1 := congrArg Nat.succ h3
_ = 1 + d := Nat.add_comm d 1
_ = e := Eq.symm h4
This style of writing proofs is most effective when it is used in
conjunction with the simp
and rewrite
tactics, which are
discussed in greater detail in the next chapter. For example, using
the abbreviation rw
for rewrite, the proof above could be written
as follows:
variable (a b c d e : Nat)
variable (h1 : a = b)
variable (h2 : b = c + 1)
variable (h3 : c = d)
variable (h4 : e = 1 + d)
theorem T : a = e :=
calc
a = b := by rw [h1]
_ = c + 1 := by rw [h2]
_ = d + 1 := by rw [h3]
_ = 1 + d := by rw [Nat.add_comm]
_ = e := by rw [h4]
Essentially, the rw
tactic uses a given equality (which can be a
hypothesis, a theorem name, or a complex term) to "rewrite" the
goal. If doing so reduces the goal to an identity t = t
, the
tactic applies reflexivity to prove it.
Rewrites can be applied sequentially, so that the proof above can be shortened to this:
variable (a b c d e : Nat)
variable (h1 : a = b)
variable (h2 : b = c + 1)
variable (h3 : c = d)
variable (h4 : e = 1 + d)
theorem T : a = e :=
calc
a = d + 1 := by rw [h1, h2, h3]
_ = 1 + d := by rw [Nat.add_comm]
_ = e := by rw [h4]
Or even this:
variable (a b c d e : Nat)
variable (h1 : a = b)
variable (h2 : b = c + 1)
variable (h3 : c = d)
variable (h4 : e = 1 + d)
theorem T : a = e :=
by rw [h1, h2, h3, Nat.add_comm, h4]
The simp
tactic, instead, rewrites the goal by applying the given
identities repeatedly, in any order, anywhere they are applicable in a
term. It also uses other rules that have been previously declared to
the system, and applies commutativity wisely to avoid looping. As a
result, we can also prove the theorem as follows:
variable (a b c d e : Nat)
variable (h1 : a = b)
variable (h2 : b = c + 1)
variable (h3 : c = d)
variable (h4 : e = 1 + d)
theorem T : a = e :=
by simp [h1, h2, h3, Nat.add_comm, h4]
We will discuss variations of rw
and simp
in the next chapter.
The calc
command can be configured for any relation that supports
some form of transitivity. It can even combine different relations.
example (a b c d : Nat) (h1 : a = b) (h2 : b ≤ c) (h3 : c + 1 < d) : a < d :=
calc
a = b := h1
_ < b + 1 := Nat.lt_succ_self b
_ ≤ c + 1 := Nat.succ_le_succ h2
_ < d := h3
You can "teach" calc
new transitivity theorems by adding new instances
of the Trans
type class. Type classes are introduced later, but the following
small example demonstrates how to extend the calc
notation using new Trans
instances.
def divides (x y : Nat) : Prop :=
∃ k, k*x = y
def divides_trans (h₁ : divides x y) (h₂ : divides y z) : divides x z :=
let ⟨k₁, d₁⟩ := h₁
let ⟨k₂, d₂⟩ := h₂
⟨k₁ * k₂, by rw [Nat.mul_comm k₁ k₂, Nat.mul_assoc, d₁, d₂]⟩
def divides_mul (x : Nat) (k : Nat) : divides x (k*x) :=
⟨k, rfl⟩
instance : Trans divides divides divides where
trans := divides_trans
example (h₁ : divides x y) (h₂ : y = z) : divides x (2*z) :=
calc
divides x y := h₁
_ = z := h₂
divides _ (2*z) := divides_mul ..
infix:50 " ∣ " => divides
example (h₁ : divides x y) (h₂ : y = z) : divides x (2*z) :=
calc
x ∣ y := h₁
_ = z := h₂
_ ∣ 2*z := divides_mul ..
The example above also makes it clear that you can use calc
even if you
do not have an infix notation for your relation. Finally we remark that
the vertical bar ∣
in the example above is the unicode one. We use
unicode to make sure we do not overload the ASCII 
used in the
match .. with
expression.
With calc
, we can write the proof in the last section in a more
natural and perspicuous way.
example (x y : Nat) : (x + y) * (x + y) = x * x + y * x + x * y + y * y :=
calc
(x + y) * (x + y) = (x + y) * x + (x + y) * y := by rw [Nat.mul_add]
_ = x * x + y * x + (x + y) * y := by rw [Nat.add_mul]
_ = x * x + y * x + (x * y + y * y) := by rw [Nat.add_mul]
_ = x * x + y * x + x * y + y * y := by rw [←Nat.add_assoc]
The alternative calc
notation is worth considering here. When the
first expression is taking this much space, using _
in the first
relation naturally aligns all relations:
example (x y : Nat) : (x + y) * (x + y) = x * x + y * x + x * y + y * y :=
calc (x + y) * (x + y)
_ = (x + y) * x + (x + y) * y := by rw [Nat.mul_add]
_ = x * x + y * x + (x + y) * y := by rw [Nat.add_mul]
_ = x * x + y * x + (x * y + y * y) := by rw [Nat.add_mul]
_ = x * x + y * x + x * y + y * y := by rw [←Nat.add_assoc]
Here the left arrow before Nat.add_assoc
tells rewrite to use the
identity in the opposite direction. (You can enter it with \l
or
use the ascii equivalent, <
.) If brevity is what we are after,
both rw
and simp
can do the job on their own:
example (x y : Nat) : (x + y) * (x + y) = x * x + y * x + x * y + y * y :=
by rw [Nat.mul_add, Nat.add_mul, Nat.add_mul, ←Nat.add_assoc]
example (x y : Nat) : (x + y) * (x + y) = x * x + y * x + x * y + y * y :=
by simp [Nat.mul_add, Nat.add_mul, Nat.add_assoc]
The Existential Quantifier
Finally, consider the existential quantifier, which can be written as
either exists x : α, p x
or ∃ x : α, p x
. Both versions are
actually notationally convenient abbreviations for a more longwinded
expression, Exists (fun x : α => p x)
, defined in Lean's library.
As you should by now expect, the library includes both an introduction
rule and an elimination rule. The introduction rule is
straightforward: to prove ∃ x : α, p x
, it suffices to provide a
suitable term t
and a proof of p t
. Here are some examples:
example : ∃ x : Nat, x > 0 :=
have h : 1 > 0 := Nat.zero_lt_succ 0
Exists.intro 1 h
example (x : Nat) (h : x > 0) : ∃ y, y < x :=
Exists.intro 0 h
example (x y z : Nat) (hxy : x < y) (hyz : y < z) : ∃ w, x < w ∧ w < z :=
Exists.intro y (And.intro hxy hyz)
#check @Exists.intro  ∀ {α : Sort u_1} {p : α → Prop} (w : α), p w → Exists p
We can use the anonymous constructor notation ⟨t, h⟩
for
Exists.intro t h
, when the type is clear from the context.
example : ∃ x : Nat, x > 0 :=
have h : 1 > 0 := Nat.zero_lt_succ 0
⟨1, h⟩
example (x : Nat) (h : x > 0) : ∃ y, y < x :=
⟨0, h⟩
example (x y z : Nat) (hxy : x < y) (hyz : y < z) : ∃ w, x < w ∧ w < z :=
⟨y, hxy, hyz⟩
Note that Exists.intro
has implicit arguments: Lean has to infer
the predicate p : α → Prop
in the conclusion ∃ x, p x
. This
is not a trivial affair. For example, if we have
hg : g 0 0 = 0
and write Exists.intro 0 hg
, there are many possible values
for the predicate p
, corresponding to the theorems ∃ x, g x x = x
,
∃ x, g x x = 0
, ∃ x, g x 0 = x
, etc. Lean uses the
context to infer which one is appropriate. This is illustrated in the
following example, in which we set the option pp.explicit
to true
to ask Lean's prettyprinter to show the implicit arguments.
variable (g : Nat → Nat → Nat)
variable (hg : g 0 0 = 0)
theorem gex1 : ∃ x, g x x = x := ⟨0, hg⟩
theorem gex2 : ∃ x, g x 0 = x := ⟨0, hg⟩
theorem gex3 : ∃ x, g 0 0 = x := ⟨0, hg⟩
theorem gex4 : ∃ x, g x x = 0 := ⟨0, hg⟩
set_option pp.explicit true  display implicit arguments
#print gex1
#print gex2
#print gex3
#print gex4
We can view Exists.intro
as an informationhiding operation, since
it hides the witness to the body of the assertion. The existential
elimination rule, Exists.elim
, performs the opposite operation. It
allows us to prove a proposition q
from ∃ x : α, p x
, by
showing that q
follows from p w
for an arbitrary value
w
. Roughly speaking, since we know there is an x
satisfying
p x
, we can give it a name, say, w
. If q
does not mention
w
, then showing that q
follows from p w
is tantamount to
showing that q
follows from the existence of any such x
. Here
is an example:
variable (α : Type) (p q : α → Prop)
example (h : ∃ x, p x ∧ q x) : ∃ x, q x ∧ p x :=
Exists.elim h
(fun w =>
fun hw : p w ∧ q w =>
show ∃ x, q x ∧ p x from ⟨w, hw.right, hw.left⟩)
It may be helpful to compare the existselimination rule to the
orelimination rule: the assertion ∃ x : α, p x
can be thought of
as a big disjunction of the propositions p a
, as a
ranges over
all the elements of α
. Note that the anonymous constructor
notation ⟨w, hw.right, hw.left⟩
abbreviates a nested constructor
application; we could equally well have written ⟨w, ⟨hw.right, hw.left⟩⟩
.
Notice that an existential proposition is very similar to a sigma
type, as described in dependent types section. The difference is that
given a : α
and h : p a
, the term Exists.intro a h
has
type (∃ x : α, p x) : Prop
and Sigma.mk a h
has type
(Σ x : α, p x) : Type
. The similarity between ∃
and Σ
is another
instance of the CurryHoward isomorphism.
Lean provides a more convenient way to eliminate from an existential
quantifier with the match
expression:
variable (α : Type) (p q : α → Prop)
example (h : ∃ x, p x ∧ q x) : ∃ x, q x ∧ p x :=
match h with
 ⟨w, hw⟩ => ⟨w, hw.right, hw.left⟩
The match
expression is part of Lean's function definition system,
which provides convenient and expressive ways of defining complex
functions. Once again, it is the CurryHoward isomorphism that allows
us to coopt this mechanism for writing proofs as well. The match
statement "destructs" the existential assertion into the components
w
and hw
, which can then be used in the body of the statement
to prove the proposition. We can annotate the types used in the match
for greater clarity:
variable (α : Type) (p q : α → Prop)
example (h : ∃ x, p x ∧ q x) : ∃ x, q x ∧ p x :=
match h with
 ⟨(w : α), (hw : p w ∧ q w)⟩ => ⟨w, hw.right, hw.left⟩
We can even use the match statement to decompose the conjunction at the same time:
variable (α : Type) (p q : α → Prop)
example (h : ∃ x, p x ∧ q x) : ∃ x, q x ∧ p x :=
match h with
 ⟨w, hpw, hqw⟩ => ⟨w, hqw, hpw⟩
Lean also provides a patternmatching let
expression:
variable (α : Type) (p q : α → Prop)
example (h : ∃ x, p x ∧ q x) : ∃ x, q x ∧ p x :=
let ⟨w, hpw, hqw⟩ := h
⟨w, hqw, hpw⟩
This is essentially just alternative notation for the match
construct above. Lean will even allow us to use an implicit match
in the fun
expression:
variable (α : Type) (p q : α → Prop)
example : (∃ x, p x ∧ q x) → ∃ x, q x ∧ p x :=
fun ⟨w, hpw, hqw⟩ => ⟨w, hqw, hpw⟩
We will see in Chapter Induction and Recursion that all these variations are instances of a more general patternmatching construct.
In the following example, we define is_even a
as ∃ b, a = 2 * b
,
and then we show that the sum of two even numbers is an even number.
def is_even (a : Nat) := ∃ b, a = 2 * b
theorem even_plus_even (h1 : is_even a) (h2 : is_even b) : is_even (a + b) :=
Exists.elim h1 (fun w1 (hw1 : a = 2 * w1) =>
Exists.elim h2 (fun w2 (hw2 : b = 2 * w2) =>
Exists.intro (w1 + w2)
(calc a + b
_ = 2 * w1 + 2 * w2 := by rw [hw1, hw2]
_ = 2 * (w1 + w2) := by rw [Nat.mul_add])))
Using the various gadgets described in this chapter  the match
statement, anonymous constructors, and the rewrite
tactic, we can
write this proof concisely as follows:
def is_even (a : Nat) := ∃ b, a = 2 * b
theorem even_plus_even (h1 : is_even a) (h2 : is_even b) : is_even (a + b) :=
match h1, h2 with
 ⟨w1, hw1⟩, ⟨w2, hw2⟩ => ⟨w1 + w2, by rw [hw1, hw2, Nat.mul_add]⟩
Just as the constructive "or" is stronger than the classical "or," so,
too, is the constructive "exists" stronger than the classical
"exists". For example, the following implication requires classical
reasoning because, from a constructive standpoint, knowing that it is
not the case that every x
satisfies ¬ p
is not the same as
having a particular x
that satisfies p
.
open Classical
variable (p : α → Prop)
example (h : ¬ ∀ x, ¬ p x) : ∃ x, p x :=
byContradiction
(fun h1 : ¬ ∃ x, p x =>
have h2 : ∀ x, ¬ p x :=
fun x =>
fun h3 : p x =>
have h4 : ∃ x, p x := ⟨x, h3⟩
show False from h1 h4
show False from h h2)
What follows are some common identities involving the existential quantifier. In the exercises below, we encourage you to prove as many as you can. We also leave it to you to determine which are nonconstructive, and hence require some form of classical reasoning.
open Classical
variable (α : Type) (p q : α → Prop)
variable (r : Prop)
example : (∃ x : α, r) → r := sorry
example (a : α) : r → (∃ x : α, r) := sorry
example : (∃ x, p x ∧ r) ↔ (∃ x, p x) ∧ r := sorry
example : (∃ x, p x ∨ q x) ↔ (∃ x, p x) ∨ (∃ x, q x) := sorry
example : (∀ x, p x) ↔ ¬ (∃ x, ¬ p x) := sorry
example : (∃ x, p x) ↔ ¬ (∀ x, ¬ p x) := sorry
example : (¬ ∃ x, p x) ↔ (∀ x, ¬ p x) := sorry
example : (¬ ∀ x, p x) ↔ (∃ x, ¬ p x) := sorry
example : (∀ x, p x → r) ↔ (∃ x, p x) → r := sorry
example (a : α) : (∃ x, p x → r) ↔ (∀ x, p x) → r := sorry
example (a : α) : (∃ x, r → p x) ↔ (r → ∃ x, p x) := sorry
Notice that the second example and the last two examples require the
assumption that there is at least one element a
of type α
.
Here are solutions to two of the more difficult ones:
open Classical
variable (α : Type) (p q : α → Prop)
variable (a : α)
variable (r : Prop)
example : (∃ x, p x ∨ q x) ↔ (∃ x, p x) ∨ (∃ x, q x) :=
Iff.intro
(fun ⟨a, (h1 : p a ∨ q a)⟩ =>
Or.elim h1
(fun hpa : p a => Or.inl ⟨a, hpa⟩)
(fun hqa : q a => Or.inr ⟨a, hqa⟩))
(fun h : (∃ x, p x) ∨ (∃ x, q x) =>
Or.elim h
(fun ⟨a, hpa⟩ => ⟨a, (Or.inl hpa)⟩)
(fun ⟨a, hqa⟩ => ⟨a, (Or.inr hqa)⟩))
example : (∃ x, p x → r) ↔ (∀ x, p x) → r :=
Iff.intro
(fun ⟨b, (hb : p b → r)⟩ =>
fun h2 : ∀ x, p x =>
show r from hb (h2 b))
(fun h1 : (∀ x, p x) → r =>
show ∃ x, p x → r from
byCases
(fun hap : ∀ x, p x => ⟨a, λ h' => h1 hap⟩)
(fun hnap : ¬ ∀ x, p x =>
byContradiction
(fun hnex : ¬ ∃ x, p x → r =>
have hap : ∀ x, p x :=
fun x =>
byContradiction
(fun hnp : ¬ p x =>
have hex : ∃ x, p x → r := ⟨x, (fun hp => absurd hp hnp)⟩
show False from hnex hex)
show False from hnap hap)))
More on the Proof Language
We have seen that keywords like fun
, have
, and show
make
it possible to write formal proof terms that mirror the structure of
informal mathematical proofs. In this section, we discuss some
additional features of the proof language that are often convenient.
To start with, we can use anonymous "have" expressions to introduce an
auxiliary goal without having to label it. We can refer to the last
expression introduced in this way using the keyword this
:
variable (f : Nat → Nat)
variable (h : ∀ x : Nat, f x ≤ f (x + 1))
example : f 0 ≤ f 3 :=
have : f 0 ≤ f 1 := h 0
have : f 0 ≤ f 2 := Nat.le_trans this (h 1)
show f 0 ≤ f 3 from Nat.le_trans this (h 2)
Often proofs move from one fact to the next, so this can be effective in eliminating the clutter of lots of labels.
When the goal can be inferred, we can also ask Lean instead to fill in
the proof by writing by assumption
:
variable (f : Nat → Nat)
variable (h : ∀ x : Nat, f x ≤ f (x + 1))
example : f 0 ≤ f 3 :=
have : f 0 ≤ f 1 := h 0
have : f 0 ≤ f 2 := Nat.le_trans (by assumption) (h 1)
show f 0 ≤ f 3 from Nat.le_trans (by assumption) (h 2)
This tells Lean to use the assumption
tactic, which, in turn,
proves the goal by finding a suitable hypothesis in the local
context. We will learn more about the assumption
tactic in the
next chapter.
We can also ask Lean to fill in the proof by writing ‹p›
, where
p
is the proposition whose proof we want Lean to find in the
context. You can type these corner quotes using \f<
and \f>
,
respectively. The letter "f" is for "French," since the unicode
symbols can also be used as French quotation marks. In fact, the
notation is defined in Lean as follows:
notation "‹" p "›" => show p by assumption
This approach is more robust than using by assumption
, because the
type of the assumption that needs to be inferred is given
explicitly. It also makes proofs more readable. Here is a more
elaborate example:
variable (f : Nat → Nat)
variable (h : ∀ x : Nat, f x ≤ f (x + 1))
example : f 0 ≥ f 1 → f 1 ≥ f 2 → f 0 = f 2 :=
fun _ : f 0 ≥ f 1 =>
fun _ : f 1 ≥ f 2 =>
have : f 0 ≥ f 2 := Nat.le_trans ‹f 1 ≥ f 2› ‹f 0 ≥ f 1›
have : f 0 ≤ f 2 := Nat.le_trans (h 0) (h 1)
show f 0 = f 2 from Nat.le_antisymm this ‹f 0 ≥ f 2›
Keep in mind that you can use the French quotation marks in this way to refer to anything in the context, not just things that were introduced anonymously. Its use is also not limited to propositions, though using it for data is somewhat odd:
example (n : Nat) : Nat := ‹Nat›
Later, we show how you can extend the proof language using the Lean macro system.
Exercises
 Prove these equivalences:
variable (α : Type) (p q : α → Prop)
example : (∀ x, p x ∧ q x) ↔ (∀ x, p x) ∧ (∀ x, q x) := sorry
example : (∀ x, p x → q x) → (∀ x, p x) → (∀ x, q x) := sorry
example : (∀ x, p x) ∨ (∀ x, q x) → ∀ x, p x ∨ q x := sorry
You should also try to understand why the reverse implication is not derivable in the last example.
 It is often possible to bring a component of a formula outside a universal quantifier, when it does not depend on the quantified variable. Try proving these (one direction of the second of these requires classical logic):
variable (α : Type) (p q : α → Prop)
variable (r : Prop)
example : α → ((∀ x : α, r) ↔ r) := sorry
example : (∀ x, p x ∨ r) ↔ (∀ x, p x) ∨ r := sorry
example : (∀ x, r → p x) ↔ (r → ∀ x, p x) := sorry
 Consider the "barber paradox," that is, the claim that in a certain town there is a (male) barber that shaves all and only the men who do not shave themselves. Prove that this is a contradiction:
variable (men : Type) (barber : men)
variable (shaves : men → men → Prop)
example (h : ∀ x : men, shaves barber x ↔ ¬ shaves x x) : False := sorry
 Remember that, without any parameters, an expression of type
Prop
is just an assertion. Fill in the definitions ofprime
andFermat_prime
below, and construct each of the given assertions. For example, you can say that there are infinitely many primes by asserting that for every natural numbern
, there is a prime number greater thann
. Goldbach's weak conjecture states that every odd number greater than 5 is the sum of three primes. Look up the definition of a Fermat prime or any of the other statements, if necessary.
def even (n : Nat) : Prop := sorry
def prime (n : Nat) : Prop := sorry
def infinitely_many_primes : Prop := sorry
def Fermat_prime (n : Nat) : Prop := sorry
def infinitely_many_Fermat_primes : Prop := sorry
def goldbach_conjecture : Prop := sorry
def Goldbach's_weak_conjecture : Prop := sorry
def Fermat's_last_theorem : Prop := sorry
 Prove as many of the identities listed in the Existential Quantifier section as you can.
Tactics
In this chapter, we describe an alternative approach to constructing proofs, using tactics. A proof term is a representation of a mathematical proof; tactics are commands, or instructions, that describe how to build such a proof. Informally, you might begin a mathematical proof by saying "to prove the forward direction, unfold the definition, apply the previous lemma, and simplify." Just as these are instructions that tell the reader how to find the relevant proof, tactics are instructions that tell Lean how to construct a proof term. They naturally support an incremental style of writing proofs, in which you decompose a proof and work on goals one step at a time.
We will describe proofs that consist of sequences of tactics as "tacticstyle" proofs, to contrast with the ways of writing proof terms we have seen so far, which we will call "termstyle" proofs. Each style has its own advantages and disadvantages. For example, tacticstyle proofs can be harder to read, because they require the reader to predict or guess the results of each instruction. But they can also be shorter and easier to write. Moreover, tactics offer a gateway to using Lean's automation, since automated procedures are themselves tactics.
Entering Tactic Mode
Conceptually, stating a theorem or introducing a have
statement
creates a goal, namely, the goal of constructing a term with the
expected type. For example, the following creates the goal of
constructing a term of type p ∧ q ∧ p
, in a context with constants
p q : Prop
, hp : p
and hq : q
:
theorem test (p q : Prop) (hp : p) (hq : q) : p ∧ q ∧ p :=
sorry
You can write this goal as follows:
p : Prop, q : Prop, hp : p, hq : q ⊢ p ∧ q ∧ p
Indeed, if you replace the "sorry" by an underscore in the example above, Lean will report that it is exactly this goal that has been left unsolved.
Ordinarily, you meet such a goal by writing an explicit term. But
wherever a term is expected, Lean allows us to insert instead a by <tactics>
block, where <tactics>
is a sequence of commands,
separated by semicolons or line breaks. You can prove the theorem above
in that way:
theorem test (p q : Prop) (hp : p) (hq : q) : p ∧ q ∧ p :=
by apply And.intro
exact hp
apply And.intro
exact hq
exact hp
We often put the by
keyword on the preceding line, and write the
example above as:
theorem test (p q : Prop) (hp : p) (hq : q) : p ∧ q ∧ p := by
apply And.intro
exact hp
apply And.intro
exact hq
exact hp
The apply
tactic applies an expression, viewed as denoting a
function with zero or more arguments. It unifies the conclusion with
the expression in the current goal, and creates new goals for the
remaining arguments, provided that no later arguments depend on
them. In the example above, the command apply And.intro
yields two
subgoals:
case left
p q : Prop
hp : p
hq : q
⊢ p
case right
p q : Prop
hp : p
hq : q
⊢ q ∧ p
The first goal is met with the command exact hp
. The exact
command is just a variant of apply
which signals that the
expression given should fill the goal exactly. It is good form to use
it in a tactic proof, since its failure signals that something has
gone wrong. It is also more robust than apply
, since the
elaborator takes the expected type, given by the target of the goal,
into account when processing the expression that is being applied. In
this case, however, apply
would work just as well.
You can see the resulting proof term with the #print
command:
theorem test (p q : Prop) (hp : p) (hq : q) : p ∧ q ∧ p := by
apply And.intro
exact hp
apply And.intro
exact hq
exact hp
#print test
You can write a tactic script incrementally. In VS Code, you can open
a window to display messages by pressing CtrlShiftEnter
, and
that window will then show you the current goal whenever the cursor is
in a tactic block. In Emacs, you can see the goal at the end of any
line by pressing Cc Cg
, or see the remaining goal in an
incomplete proof by putting the cursor after the first character of
the last tactic. If the proof is incomplete, the token by
is
decorated with a red squiggly line, and the error message contains the
remaining goals.
Tactic commands can take compound expressions, not just single identifiers. The following is a shorter version of the preceding proof:
theorem test (p q : Prop) (hp : p) (hq : q) : p ∧ q ∧ p := by
apply And.intro hp
exact And.intro hq hp
Unsurprisingly, it produces exactly the same proof term.
theorem test (p q : Prop) (hp : p) (hq : q) : p ∧ q ∧ p := by
apply And.intro hp
exact And.intro hq hp
#print test
Multiple tactic applications can be written in a single line by concatenating with a semicolon.
theorem test (p q : Prop) (hp : p) (hq : q) : p ∧ q ∧ p := by
apply And.intro hp; exact And.intro hq hp
Tactics that may produce multiple subgoals often tag them. For
example, the tactic apply And.intro
tagged the first subgoal as
left
, and the second as right
. In the case of the apply
tactic, the tags are inferred from the parameters' names used in the
And.intro
declaration. You can structure your tactics using the
notation case <tag> => <tactics>
. The following is a structured
version of our first tactic proof in this chapter.
theorem test (p q : Prop) (hp : p) (hq : q) : p ∧ q ∧ p := by
apply And.intro
case left => exact hp
case right =>
apply And.intro
case left => exact hq
case right => exact hp
You can solve the subgoal right
before left
using the case
notation:
theorem test (p q : Prop) (hp : p) (hq : q) : p ∧ q ∧ p := by
apply And.intro
case right =>
apply And.intro
case left => exact hq
case right => exact hp
case left => exact hp
Note that Lean hides the other goals inside the case
block. We say
it is "focusing" on the selected goal. Moreover, Lean flags an error
if the selected goal is not fully solved at the end of the case
block.
For simple subgoals, it may not be worth selecting a subgoal using its
tag, but you may still want to structure the proof. Lean also provides
the "bullet" notation . <tactics>
(or · <tactics>
) for
structuring proof.
theorem test (p q : Prop) (hp : p) (hq : q) : p ∧ q ∧ p := by
apply And.intro
. exact hp
. apply And.intro
. exact hq
. exact hp
Basic Tactics
In addition to apply
and exact
, another useful tactic is
intro
, which introduces a hypothesis. What follows is an example
of an identity from propositional logic that we proved in a previous
chapter, now proved using tactics.
example (p q r : Prop) : p ∧ (q ∨ r) ↔ (p ∧ q) ∨ (p ∧ r) := by
apply Iff.intro
. intro h
apply Or.elim (And.right h)
. intro hq
apply Or.inl
apply And.intro
. exact And.left h
. exact hq
. intro hr
apply Or.inr
apply And.intro
. exact And.left h
. exact hr
. intro h
apply Or.elim h
. intro hpq
apply And.intro
. exact And.left hpq
. apply Or.inl
exact And.right hpq
. intro hpr
apply And.intro
. exact And.left hpr
. apply Or.inr
exact And.right hpr
The intro
command can more generally be used to introduce a variable of any type:
example (α : Type) : α → α := by
intro a
exact a
example (α : Type) : ∀ x : α, x = x := by
intro x
exact Eq.refl x
You can use it to introduce several variables:
example : ∀ a b c : Nat, a = b → a = c → c = b := by
intro a b c h₁ h₂
exact Eq.trans (Eq.symm h₂) h₁
As the apply
tactic is a command for constructing function
applications interactively, the intro
tactic is a command for
constructing function abstractions interactively (i.e., terms of the
form fun x => e
). As with lambda abstraction notation, the
intro
tactic allows us to use an implicit match
.
example (α : Type) (p q : α → Prop) : (∃ x, p x ∧ q x) → ∃ x, q x ∧ p x := by
intro ⟨w, hpw, hqw⟩
exact ⟨w, hqw, hpw⟩
You can also provide multiple alternatives like in the match
expression.
example (α : Type) (p q : α → Prop) : (∃ x, p x ∨ q x) → ∃ x, q x ∨ p x := by
intro
 ⟨w, Or.inl h⟩ => exact ⟨w, Or.inr h⟩
 ⟨w, Or.inr h⟩ => exact ⟨w, Or.inl h⟩
The intros
tactic can be used without any arguments, in which
case, it chooses names and introduces as many variables as it can. You
will see an example of this in a moment.
The assumption
tactic looks through the assumptions in context of
the current goal, and if there is one matching the conclusion, it
applies it.
example (x y z w : Nat) (h₁ : x = y) (h₂ : y = z) (h₃ : z = w) : x = w := by
apply Eq.trans h₁
apply Eq.trans h₂
assumption  applied h₃
It will unify metavariables in the conclusion if necessary:
example (x y z w : Nat) (h₁ : x = y) (h₂ : y = z) (h₃ : z = w) : x = w := by
apply Eq.trans
assumption  solves x = ?b with h₁
apply Eq.trans
assumption  solves y = ?h₂.b with h₂
assumption  solves z = w with h₃
The following example uses the intros
command to introduce the three variables and two hypotheses automatically:
example : ∀ a b c : Nat, a = b → a = c → c = b := by
intros
apply Eq.trans
apply Eq.symm
assumption
assumption
Note that names automatically generated by Lean are inaccessible by default. The motivation is to
ensure your tactic proofs do not rely on automatically generated names, and are consequently more robust.
However, you can use the combinator unhygienic
to disable this restriction.
example : ∀ a b c : Nat, a = b → a = c → c = b := by unhygienic
intros
apply Eq.trans
apply Eq.symm
exact a_2
exact a_1
You can also use the rename_i
tactic to rename the most recent inaccessible names in your context.
In the following example, the tactic rename_i h1 _ h2
renames two of the last three hypotheses in
your context.
example : ∀ a b c d : Nat, a = b → a = d → a = c → c = b := by
intros
rename_i h1 _ h2
apply Eq.trans
apply Eq.symm
exact h2
exact h1
The rfl
tactic is syntactic sugar for exact rfl
.
example (y : Nat) : (fun x : Nat => 0) y = 0 :=
by rfl
The repeat
combinator can be used to apply a tactic several times.
example : ∀ a b c : Nat, a = b → a = c → c = b := by
intros
apply Eq.trans
apply Eq.symm
repeat assumption
Another tactic that is sometimes useful is the revert
tactic,
which is, in a sense, an inverse to intro
.
example (x : Nat) : x = x := by
revert x
 goal is ⊢ ∀ (x : Nat), x = x
intro y
 goal is y : Nat ⊢ y = y
rfl
Moving a hypothesis into the goal yields an implication:
example (x y : Nat) (h : x = y) : y = x := by
revert h
 goal is x y : Nat ⊢ x = y → y = x
intro h₁
 goal is x y : Nat, h₁ : x = y ⊢ y = x
apply Eq.symm
assumption
But revert
is even more clever, in that it will revert not only an
element of the context but also all the subsequent elements of the
context that depend on it. For example, reverting x
in the example
above brings h
along with it:
example (x y : Nat) (h : x = y) : y = x := by
revert x
 goal is y : Nat ⊢ ∀ (x : Nat), x = y → y = x
intros
apply Eq.symm
assumption
You can also revert multiple elements of the context at once:
example (x y : Nat) (h : x = y) : y = x := by
revert x y
 goal is ⊢ ∀ (x y : Nat), x = y → y = x
intros
apply Eq.symm
assumption
You can only revert
an element of the local context, that is, a
local variable or hypothesis. But you can replace an arbitrary
expression in the goal by a fresh variable using the generalize
tactic.
example : 3 = 3 := by
generalize 3 = x
 goal is x : Nat ⊢ x = x
revert x
 goal is ⊢ ∀ (x : Nat), x = x
intro y
 goal is y : Nat ⊢ y = y
rfl
The mnemonic in the notation above is that you are generalizing the
goal by setting 3
to an arbitrary variable x
. Be careful: not
every generalization preserves the validity of the goal. Here,
generalize
replaces a goal that could be proved using
rfl
with one that is not provable:
example : 2 + 3 = 5 := by
generalize 3 = x
 goal is x : Nat ⊢ 2 + x = 5
admit
In this example, the admit
tactic is the analogue of the sorry
proof term. It closes the current goal, producing the usual warning
that sorry
has been used. To preserve the validity of the previous
goal, the generalize
tactic allows us to record the fact that
3
has been replaced by x
. All you need to do is to provide a
label, and generalize
uses it to store the assignment in the local
context:
example : 2 + 3 = 5 := by
generalize h : 3 = x
 goal is x : Nat, h : 3 = x ⊢ 2 + x = 5
rw [← h]
Here the rewrite
tactic, abbreviated rw
, uses h
to replace
x
by 3
again. The rewrite
tactic will be discussed below.
More Tactics
Some additional tactics are useful for constructing and destructing
propositions and data. For example, when applied to a goal of the form
p ∨ q
, you use tactics such as apply Or.inl
and apply Or.inr
. Conversely, the cases
tactic can be used to decompose a
disjunction.
example (p q : Prop) : p ∨ q → q ∨ p := by
intro h
cases h with
 inl hp => apply Or.inr; exact hp
 inr hq => apply Or.inl; exact hq
Note that the syntax is similar to the one used in match
expressions.
The new subgoals can be solved in any order.
example (p q : Prop) : p ∨ q → q ∨ p := by
intro h
cases h with
 inr hq => apply Or.inl; exact hq
 inl hp => apply Or.inr; exact hp
You can also use a (unstructured) cases
without the with
and a tactic
for each alternative.
example (p q : Prop) : p ∨ q → q ∨ p := by
intro h
cases h
apply Or.inr
assumption
apply Or.inl
assumption
The (unstructured) cases
is particularly useful when you can close several
subgoals using the same tactic.
example (p : Prop) : p ∨ p → p := by
intro h
cases h
repeat assumption
You can also use the combinator tac1 <;> tac2
to apply tac2
to each
subgoal produced by tactic tac1
.
example (p : Prop) : p ∨ p → p := by
intro h
cases h <;> assumption
You can combine the unstructured cases
tactic with the case
and .
notation.
example (p q : Prop) : p ∨ q → q ∨ p := by
intro h
cases h
. apply Or.inr
assumption
. apply Or.inl
assumption
example (p q : Prop) : p ∨ q → q ∨ p := by
intro h
cases h
case inr h =>
apply Or.inl
assumption
case inl h =>
apply Or.inr
assumption
example (p q : Prop) : p ∨ q → q ∨ p := by
intro h
cases h
case inr h =>
apply Or.inl
assumption
. apply Or.inr
assumption
The cases
tactic can also be used to
decompose a conjunction.
example (p q : Prop) : p ∧ q → q ∧ p := by
intro h
cases h with
 intro hp hq => constructor; exact hq; exact hp
In this example, there is only one goal after the cases
tactic is
applied, with h : p ∧ q
replaced by a pair of assumptions,
hp : p
and hq : q
. The constructor
tactic applies the unique
constructor for conjunction, And.intro
. With these tactics, an
example from the previous section can be rewritten as follows:
example (p q r : Prop) : p ∧ (q ∨ r) ↔ (p ∧ q) ∨ (p ∧ r) := by
apply Iff.intro
. intro h
cases h with
 intro hp hqr =>
cases hqr
. apply Or.inl; constructor <;> assumption
. apply Or.inr; constructor <;> assumption
. intro h
cases h with
 inl hpq =>
cases hpq with
 intro hp hq => constructor; exact hp; apply Or.inl; exact hq
 inr hpr =>
cases hpr with
 intro hp hr => constructor; exact hp; apply Or.inr; exact hr
You will see in Chapter Inductive Types that
these tactics are quite general. The cases
tactic can be used to
decompose any element of an inductively defined type; constructor
always applies the first applicable constructor of an inductively defined type.
For example, you can use cases
and constructor
with an existential quantifier:
example (p q : Nat → Prop) : (∃ x, p x) → ∃ x, p x ∨ q x := by
intro h
cases h with
 intro x px => constructor; apply Or.inl; exact px
Here, the constructor
tactic leaves the first component of the
existential assertion, the value of x
, implicit. It is represented
by a metavariable, which should be instantiated later on. In the
previous example, the proper value of the metavariable is determined
by the tactic exact px
, since px
has type p x
. If you want
to specify a witness to the existential quantifier explicitly, you can
use the exists
tactic instead:
example (p q : Nat → Prop) : (∃ x, p x) → ∃ x, p x ∨ q x := by
intro h
cases h with
 intro x px => exists x; apply Or.inl; exact px
Here is another example:
example (p q : Nat → Prop) : (∃ x, p x ∧ q x) → ∃ x, q x ∧ p x := by
intro h
cases h with
 intro x hpq =>
cases hpq with
 intro hp hq =>
exists x
These tactics can be used on data just as well as propositions. In the next example, they are used to define functions which swap the components of the product and sum types:
def swap_pair : α × β → β × α := by
intro p
cases p
constructor <;> assumption
def swap_sum : Sum α β → Sum β α := by
intro p
cases p
. apply Sum.inr; assumption
. apply Sum.inl; assumption
Note that up to the names we have chosen for the variables, the
definitions are identical to the proofs of the analogous propositions
for conjunction and disjunction. The cases
tactic will also do a
case distinction on a natural number:
open Nat
example (P : Nat → Prop) (h₀ : P 0) (h₁ : ∀ n, P (succ n)) (m : Nat) : P m := by
cases m with
 zero => exact h₀
 succ m' => exact h₁ m'
The cases
tactic, and its companion, the induction
tactic, are discussed in greater detail in
the Tactics for Inductive Types section.
The contradiction
tactic searches for a contradiction among the hypotheses of the current goal:
example (p q : Prop) : p ∧ ¬ p → q := by
intro h
cases h
contradiction
You can also use match
in tactic blocks.
example (p q r : Prop) : p ∧ (q ∨ r) ↔ (p ∧ q) ∨ (p ∧ r) := by
apply Iff.intro
. intro h
match h with
 ⟨_, Or.inl _⟩ => apply Or.inl; constructor <;> assumption
 ⟨_, Or.inr _⟩ => apply Or.inr; constructor <;> assumption
. intro h
match h with
 Or.inl ⟨hp, hq⟩ => constructor; exact hp; apply Or.inl; exact hq
 Or.inr ⟨hp, hr⟩ => constructor; exact hp; apply Or.inr; exact hr
You can "combine" intro h
with match h ...
and write the previous examples as follows
example (p q r : Prop) : p ∧ (q ∨ r) ↔ (p ∧ q) ∨ (p ∧ r) := by
apply Iff.intro
. intro
 ⟨hp, Or.inl hq⟩ => apply Or.inl; constructor <;> assumption
 ⟨hp, Or.inr hr⟩ => apply Or.inr; constructor <;> assumption
. intro
 Or.inl ⟨hp, hq⟩ => constructor; assumption; apply Or.inl; assumption
 Or.inr ⟨hp, hr⟩ => constructor; assumption; apply Or.inr; assumption
Structuring Tactic Proofs
Tactics often provide an efficient way of building a proof, but long sequences of instructions can obscure the structure of the argument. In this section, we describe some means that help provide structure to a tacticstyle proof, making such proofs more readable and robust.
One thing that is nice about Lean's proofwriting syntax is that it is
possible to mix termstyle and tacticstyle proofs, and pass between
the two freely. For example, the tactics apply
and exact
expect arbitrary terms, which you can write using have
, show
,
and so on. Conversely, when writing an arbitrary Lean term, you can
always invoke the tactic mode by inserting a by
block. The following is a somewhat toy example:
example (p q r : Prop) : p ∧ (q ∨ r) → (p ∧ q) ∨ (p ∧ r) := by
intro h
exact
have hp : p := h.left
have hqr : q ∨ r := h.right
show (p ∧ q) ∨ (p ∧ r) by
cases hqr with
 inl hq => exact Or.inl ⟨hp, hq⟩
 inr hr => exact Or.inr ⟨hp, hr⟩
The following is a more natural example:
example (p q r : Prop) : p ∧ (q ∨ r) ↔ (p ∧ q) ∨ (p ∧ r) := by
apply Iff.intro
. intro h
cases h.right with
 inl hq => exact Or.inl ⟨h.left, hq⟩
 inr hr => exact Or.inr ⟨h.left, hr⟩
. intro h
cases h with
 inl hpq => exact ⟨hpq.left, Or.inl hpq.right⟩
 inr hpr => exact ⟨hpr.left, Or.inr hpr.right⟩
In fact, there is a show
tactic, which is similar to the
show
expression in a proof term. It simply declares the type of the
goal that is about to be solved, while remaining in tactic
mode.
example (p q r : Prop) : p ∧ (q ∨ r) ↔ (p ∧ q) ∨ (p ∧ r) := by
apply Iff.intro
. intro h
cases h.right with
 inl hq =>
show (p ∧ q) ∨ (p ∧ r)
exact Or.inl ⟨h.left, hq⟩
 inr hr =>
show (p ∧ q) ∨ (p ∧ r)
exact Or.inr ⟨h.left, hr⟩
. intro h
cases h with
 inl hpq =>
show p ∧ (q ∨ r)
exact ⟨hpq.left, Or.inl hpq.right⟩
 inr hpr =>
show p ∧ (q ∨ r)
exact ⟨hpr.left, Or.inr hpr.right⟩
The show
tactic can actually be used to rewrite a goal to something definitionally equivalent:
example (n : Nat) : n + 1 = Nat.succ n := by
show Nat.succ n = Nat.succ n
rfl
There is also a have
tactic, which introduces a new subgoal, just as when writing proof terms:
example (p q r : Prop) : p ∧ (q ∨ r) → (p ∧ q) ∨ (p ∧ r) := by
intro ⟨hp, hqr⟩
show (p ∧ q) ∨ (p ∧ r)
cases hqr with
 inl hq =>
have hpq : p ∧ q := And.intro hp hq
apply Or.inl
exact hpq
 inr hr =>
have hpr : p ∧ r := And.intro hp hr
apply Or.inr
exact hpr
As with proof terms, you can omit the label in the have
tactic, in
which case, the default label this
is used:
example (p q r : Prop) : p ∧ (q ∨ r) → (p ∧ q) ∨ (p ∧ r) := by
intro ⟨hp, hqr⟩
show (p ∧ q) ∨ (p ∧ r)
cases hqr with
 inl hq =>
have : p ∧ q := And.intro hp hq
apply Or.inl
exact this
 inr hr =>
have : p ∧ r := And.intro hp hr
apply Or.inr
exact this
The types in a have
tactic can be omitted, so you can write have hp := h.left
and have hqr := h.right
. In fact, with this
notation, you can even omit both the type and the label, in which case
the new fact is introduced with the label this
.
example (p q r : Prop) : p ∧ (q ∨ r) → (p ∧ q) ∨ (p ∧ r) := by
intro ⟨hp, hqr⟩
cases hqr with
 inl hq =>
have := And.intro hp hq
apply Or.inl; exact this
 inr hr =>
have := And.intro hp hr
apply Or.inr; exact this
Lean also has a let
tactic, which is similar to the have
tactic, but is used to introduce local definitions instead of
auxiliary facts. It is the tactic analogue of a let
in a proof
term.
example : ∃ x, x + 2 = 8 := by
let a : Nat := 3 * 2
exists a
As with have
, you can leave the type implicit by writing let a := 3 * 2
. The difference between let
and have
is that
let
introduces a local definition in the context, so that the
definition of the local declaration can be unfolded in the proof.
We have used .
to create nested tactic blocks. In a nested block,
Lean focuses on the first goal, and generates an error if it has not
been fully solved at the end of the block. This can be helpful in
indicating the separate proofs of multiple subgoals introduced by a
tactic. The notation .
is whitespace sensitive and relies on the indentation
to detect whether the tactic block ends. Alternatively, you can
define tactic blocks using curly braces and semicolons.
example (p q r : Prop) : p ∧ (q ∨ r) ↔ (p ∧ q) ∨ (p ∧ r) := by
apply Iff.intro
{ intro h;
cases h.right;
{ show (p ∧ q) ∨ (p ∧ r);
exact Or.inl ⟨h.left, ‹q›⟩ }
{ show (p ∧ q) ∨ (p ∧ r);
exact Or.inr ⟨h.left, ‹r›⟩ } }
{ intro h;
cases h;
{ show p ∧ (q ∨ r);
rename_i hpq;
exact ⟨hpq.left, Or.inl hpq.right⟩ }
{ show p ∧ (q ∨ r);
rename_i hpr;
exact ⟨hpr.left, Or.inr hpr.right⟩ } }
It is useful to use indentation to structure proof: every time a tactic
leaves more than one subgoal, we separate the remaining subgoals by
enclosing them in blocks and indenting. Thus if the application of
theorem foo
to a single goal produces four subgoals, one would
expect the proof to look like this:
apply foo
. <proof of first goal>
. <proof of second goal>
. <proof of third goal>
. <proof of final goal>
or
apply foo
case <tag of first goal> => <proof of first goal>
case <tag of second goal> => <proof of second goal>
case <tag of third goal> => <proof of third goal>
case <tag of final goal> => <proof of final goal>
or
apply foo
{ <proof of first goal> }
{ <proof of second goal> }
{ <proof of third goal> }
{ <proof of final goal> }
Tactic Combinators
Tactic combinators are operations that form new tactics from old
ones. A sequencing combinator is already implicit in the by
block:
example (p q : Prop) (hp : p) : p ∨ q :=
by apply Or.inl; assumption
Here, apply Or.inl; assumption
is functionally equivalent to a
single tactic which first applies apply Or.inl
and then applies
assumption
.
In t₁ <;> t₂
, the <;>
operator provides a parallel version of the sequencing operation:
t₁
is applied to the current goal, and then t₂
is applied to all the resulting subgoals:
example (p q : Prop) (hp : p) (hq : q) : p ∧ q :=
by constructor <;> assumption
This is especially useful when the resulting goals can be finished off in a uniform way, or, at least, when it is possible to make progress on all of them uniformly.
The first  t₁  t₂  ...  tₙ
applies each tᵢ
until one succeeds, or else fails:
example (p q : Prop) (hp : p) : p ∨ q := by
first  apply Or.inl; assumption  apply Or.inr; assumption
example (p q : Prop) (hq : q) : p ∨ q := by
first  apply Or.inl; assumption  apply Or.inr; assumption
In the first example, the left branch succeeds, whereas in the second one, it is the right one that succeeds. In the next three examples, the same compound tactic succeeds in each case.
example (p q r : Prop) (hp : p) : p ∨ q ∨ r :=
by repeat (first  apply Or.inl; assumption  apply Or.inr  assumption)
example (p q r : Prop) (hq : q) : p ∨ q ∨ r :=
by repeat (first  apply Or.inl; assumption  apply Or.inr  assumption)
example (p q r : Prop) (hr : r) : p ∨ q ∨ r :=
by repeat (first  apply Or.inl; assumption  apply Or.inr  assumption)
The tactic tries to solve the left disjunct immediately by assumption; if that fails, it tries to focus on the right disjunct; and if that doesn't work, it invokes the assumption tactic.
You will have no doubt noticed by now that tactics can fail. Indeed,
it is the "failure" state that causes the first combinator to
backtrack and try the next tactic. The try
combinator builds a
tactic that always succeeds, though possibly in a trivial way:
try t
executes t
and reports success, even if t
fails. It is
equivalent to first  t  skip
, where skip
is a tactic that does
nothing (and succeeds in doing so). In the next example, the second
constructor
succeeds on the right conjunct q ∧ r
(remember that
disjunction and conjunction associate to the right) but fails on the
first. The try
tactic ensures that the sequential composition
succeeds.
example (p q r : Prop) (hp : p) (hq : q) (hr : r) : p ∧ q ∧ r := by
constructor <;> (try constructor) <;> assumption
Be careful: repeat (try t)
will loop forever, because the inner tactic never fails.
In a proof, there are often multiple goals outstanding. Parallel
sequencing is one way to arrange it so that a single tactic is applied
to multiple goals, but there are other ways to do this. For example,
all_goals t
applies t
to all open goals:
example (p q r : Prop) (hp : p) (hq : q) (hr : r) : p ∧ q ∧ r := by
constructor
all_goals (try constructor)
all_goals assumption
In this case, the any_goals
tactic provides a more robust solution.
It is similar to all_goals
, except it succeeds if its argument
succeeds on at least one goal.
example (p q r : Prop) (hp : p) (hq : q) (hr : r) : p ∧ q ∧ r := by
constructor
any_goals constructor
any_goals assumption
The first tactic in the by
block below repeatedly splits
conjunctions:
example (p q r : Prop) (hp : p) (hq : q) (hr : r) :
p ∧ ((p ∧ q) ∧ r) ∧ (q ∧ r ∧ p) := by
repeat (any_goals constructor)
all_goals assumption
In fact, we can compress the full tactic down to one line:
example (p q r : Prop) (hp : p) (hq : q) (hr : r) :
p ∧ ((p ∧ q) ∧ r) ∧ (q ∧ r ∧ p) := by
repeat (any_goals (first  constructor  assumption))
The combinator focus t
ensures that t
only effects the current
goal, temporarily hiding the others from the scope. So, if t
ordinarily only effects the current goal, focus (all_goals t)
has
the same effect as t
.
Rewriting
The rewrite
tactic (abbreviated rw
) and the simp
tactic
were introduced briefly in Calculational Proofs. In this
section and the next, we discuss them in greater detail.
The rewrite
tactic provides a basic mechanism for applying
substitutions to goals and hypotheses, providing a convenient and
efficient way of working with equality. The most basic form of the
tactic is rewrite [t]
, where t
is a term whose type asserts an
equality. For example, t
can be a hypothesis h : x = y
in the
context; it can be a general lemma, like
add_comm : ∀ x y, x + y = y + x
, in which the rewrite tactic tries to find suitable
instantiations of x
and y
; or it can be any compound term
asserting a concrete or general equation. In the following example, we
use this basic form to rewrite the goal using a hypothesis.
example (f : Nat → Nat) (k : Nat) (h₁ : f 0 = 0) (h₂ : k = 0) : f k = 0 := by
rw [h₂]  replace k with 0
rw [h₁]  replace f 0 with 0
In the example above, the first use of rw
replaces k
with
0
in the goal f k = 0
. Then, the second one replaces f 0
with 0
. The tactic automatically closes any goal of the form
t = t
. Here is an example of rewriting using a compound expression:
example (x y : Nat) (p : Nat → Prop) (q : Prop) (h : q → x = y)
(h' : p y) (hq : q) : p x := by
rw [h hq]; assumption
Here, h hq
establishes the equation x = y
.
Multiple rewrites can be combined using the notation rw [t_1, ..., t_n]
,
which is just shorthand for rw [t_1]; ...; rw [t_n]
. The previous example can be written as follows:
example (f : Nat → Nat) (k : Nat) (h₁ : f 0 = 0) (h₂ : k = 0) : f k = 0 := by
rw [h₂, h₁]
By default, rw
uses an equation in the forward direction, matching
the lefthand side with an expression, and replacing it with the
righthand side. The notation ←t
can be used to instruct the
tactic to use the equality t
in the reverse direction.
example (f : Nat → Nat) (a b : Nat) (h₁ : a = b) (h₂ : f a = 0) : f b = 0 := by
rw [←h₁, h₂]
In this example, the term ←h₁
instructs the rewriter to replace
b
with a
. In the editors, you can type the backwards arrow as
\l
. You can also use the ascii equivalent, <
.
Sometimes the lefthand side of an identity can match more than one
subterm in the pattern, in which case the rw
tactic chooses the
first match it finds when traversing the term. If that is not the one
you want, you can use additional arguments to specify the appropriate
subterm.
example (a b c : Nat) : a + b + c = a + c + b := by
rw [Nat.add_assoc, Nat.add_comm b, ← Nat.add_assoc]
example (a b c : Nat) : a + b + c = a + c + b := by
rw [Nat.add_assoc, Nat.add_assoc, Nat.add_comm b]
example (a b c : Nat) : a + b + c = a + c + b := by
rw [Nat.add_assoc, Nat.add_assoc, Nat.add_comm _ b]
In the first example above, the first step rewrites a + b + c
to
a + (b + c)
. The next step applies commutativity to the term
b + c
; without specifying the argument, the tactic would instead rewrite
a + (b + c)
to (b + c) + a
. Finally, the last step applies
associativity in the reverse direction, rewriting a + (c + b)
to
a + c + b
. The next two examples instead apply associativity to
move the parenthesis to the right on both sides, and then switch b
and c
. Notice that the last example specifies that the rewrite
should take place on the righthand side by specifying the second
argument to Nat.add_comm
.
By default, the rewrite
tactic affects only the goal. The notation
rw [t] at h
applies the rewrite t
at hypothesis h
.
example (f : Nat → Nat) (a : Nat) (h : a + 0 = 0) : f a = f 0 := by
rw [Nat.add_zero] at h
rw [h]
The first step, rw [Nat.add_zero] at h
, rewrites the hypothesis a + 0 = 0
to a = 0
.
Then the new hypothesis a = 0
is used to rewrite the goal to f 0 = f 0
.
The rewrite
tactic is not restricted to propositions.
In the following example, we use rw [h] at t
to rewrite the hypothesis t : Tuple α n
to t : Tuple α 0
.
def Tuple (α : Type) (n : Nat) :=
{ as : List α // as.length = n }
example (n : Nat) (h : n = 0) (t : Tuple α n) : Tuple α 0 := by
rw [h] at t
exact t
Using the Simplifier
Whereas rewrite
is designed as a surgical tool for manipulating a
goal, the simplifier offers a more powerful form of automation. A
number of identities in Lean's library have been tagged with the
[simp]
attribute, and the simp
tactic uses them to iteratively
rewrite subterms in an expression.
example (x y z : Nat) : (x + 0) * (0 + y * 1 + z * 0) = x * y := by
simp
example (x y z : Nat) (p : Nat → Prop) (h : p (x * y))
: p ((x + 0) * (0 + y * 1 + z * 0)) := by
simp; assumption
In the first example, the lefthand side of the equality in the goal
is simplified using the usual identities involving 0 and 1, reducing
the goal to x * y = x * y
. At that point, simp
applies
reflexivity to finish it off. In the second example, simp
reduces
the goal to p (x * y)
, at which point the assumption h
finishes it off. Here are some more examples
with lists:
open List
example (xs : List Nat)
: reverse (xs ++ [1, 2, 3]) = [3, 2, 1] ++ reverse xs := by
simp
example (xs ys : List α)
: length (reverse (xs ++ ys)) = length xs + length ys := by
simp [Nat.add_comm]
As with rw
, you can use the keyword at
to simplify a hypothesis:
example (x y z : Nat) (p : Nat → Prop)
(h : p ((x + 0) * (0 + y * 1 + z * 0))) : p (x * y) := by
simp at h; assumption
Moreover, you can use a "wildcard" asterisk to simplify all the hypotheses and the goal:
attribute [local simp] Nat.mul_comm Nat.mul_assoc Nat.mul_left_comm
attribute [local simp] Nat.add_assoc Nat.add_comm Nat.add_left_comm
example (w x y z : Nat) (p : Nat → Prop)
(h : p (x * y + z * w * x)) : p (x * w * z + y * x) := by
simp at *; assumption
example (x y z : Nat) (p : Nat → Prop)
(h₁ : p (1 * x + y)) (h₂ : p (x * z * 1))
: p (y + 0 + x) ∧ p (z * x) := by
simp at * <;> constructor <;> assumption
For operations that are commutative and associative, like
multiplication on the natural numbers, the simplifier uses these two
facts to rewrite an expression, as well as left commutativity. In
the case of multiplication the latter is expressed as follows:
x * (y * z) = y * (x * z)
. The local
modifier tells the simplifier
to use these rules in the current file (or section or namespace, as
the case may be). It may seem that commutativity and
leftcommutativity are problematic, in that repeated application of
either causes looping. But the simplifier detects identities that
permute their arguments, and uses a technique known as ordered
rewriting. This means that the system maintains an internal ordering
of terms, and only applies the identity if doing so decreases the
order. With the three identities mentioned above, this has the effect
that all the parentheses in an expression are associated to the right,
and the expressions are ordered in a canonical (though somewhat
arbitrary) way. Two expressions that are equivalent up to
associativity and commutativity are then rewritten to the same
canonical form.
attribute [local simp] Nat.mul_comm Nat.mul_assoc Nat.mul_left_comm
attribute [local simp] Nat.add_assoc Nat.add_comm Nat.add_left_comm
example (w x y z : Nat) (p : Nat → Prop)
: x * y + z * w * x = x * w * z + y * x := by
simp
example (w x y z : Nat) (p : Nat → Prop)
(h : p (x * y + z * w * x)) : p (x * w * z + y * x) := by
simp; simp at h; assumption
As with rewrite
, you can send simp
a list of facts to use,
including general lemmas, local hypotheses, definitions to unfold, and
compound expressions. The simp
tactic also recognizes the ←t
syntax that rewrite
does. In any case, the additional rules are
added to the collection of identities that are used to simplify a
term.
def f (m n : Nat) : Nat :=
m + n + m
example {m n : Nat} (h : n = 1) (h' : 0 = m) : (f m n) = n := by
simp [h, ←h', f]
A common idiom is to simplify a goal using local hypotheses:
example (f : Nat → Nat) (k : Nat) (h₁ : f 0 = 0) (h₂ : k = 0) : f k = 0 := by
simp [h₁, h₂]
To use all the hypotheses present in the local context when
simplifying, we can use the wildcard symbol, *
:
example (f : Nat → Nat) (k : Nat) (h₁ : f 0 = 0) (h₂ : k = 0) : f k = 0 := by
simp [*]
Here is another example:
example (u w x y z : Nat) (h₁ : x = y + z) (h₂ : w = u + x)
: w = z + y + u := by
simp [*, Nat.add_assoc, Nat.add_comm, Nat.add_left_comm]
The simplifier will also do propositional rewriting. For example,
using the hypothesis p
, it rewrites p ∧ q
to q
and p ∨ q
to true
, which it then proves trivially. Iterating such
rewrites produces nontrivial propositional reasoning.
example (p q : Prop) (hp : p) : p ∧ q ↔ q := by
simp [*]
example (p q : Prop) (hp : p) : p ∨ q := by
simp [*]
example (p q r : Prop) (hp : p) (hq : q) : p ∧ (q ∨ r) := by
simp [*]
The next example simplifies all the hypotheses, and then uses them to prove the goal.
example (u w x x' y y' z : Nat) (p : Nat → Prop)
(h₁ : x + 0 = x') (h₂ : y + 0 = y')
: x + y + 0 = x' + y' := by
simp at *
simp [*]
One thing that makes the simplifier especially useful is that its capabilities can grow as a library develops. For example, suppose we define a list operation that symmetrizes its input by appending its reversal:
def mk_symm (xs : List α) :=
xs ++ xs.reverse
Then for any list xs
, reverse (mk_symm xs)
is equal to mk_symm xs
,
which can easily be proved by unfolding the definition:
def mk_symm (xs : List α) :=
xs ++ xs.reverse
theorem reverse_mk_symm (xs : List α)
: (mk_symm xs).reverse = mk_symm xs := by
simp [mk_symm]
We can now use this theorem to prove new results:
def mk_symm (xs : List α) :=
xs ++ xs.reverse
theorem reverse_mk_symm (xs : List α)
: (mk_symm xs).reverse = mk_symm xs := by
simp [mk_symm]
example (xs ys : List Nat)
: (xs ++ mk_symm ys).reverse = mk_symm ys ++ xs.reverse := by
simp [reverse_mk_symm]
example (xs ys : List Nat) (p : List Nat → Prop)
(h : p (xs ++ mk_symm ys).reverse)
: p (mk_symm ys ++ xs.reverse) := by
simp [reverse_mk_symm] at h; assumption
But using reverse_mk_symm
is generally the right thing to do, and
it would be nice if users did not have to invoke it explicitly. You can
achieve that by marking it as a simplification rule when the theorem
is defined:
def mk_symm (xs : List α) :=
xs ++ xs.reverse
@[simp] theorem reverse_mk_symm (xs : List α)
: (mk_symm xs).reverse = mk_symm xs := by
simp [mk_symm]
example (xs ys : List Nat)
: (xs ++ mk_symm ys).reverse = mk_symm ys ++ xs.reverse := by
simp
example (xs ys : List Nat) (p : List Nat → Prop)
(h : p (xs ++ mk_symm ys).reverse)
: p (mk_symm ys ++ xs.reverse) := by
simp at h; assumption
The notation @[simp]
declares reverse_mk_symm
to have the
[simp]
attribute, and can be spelled out more explicitly:
def mk_symm (xs : List α) :=
xs ++ xs.reverse
theorem reverse_mk_symm (xs : List α)
: (mk_symm xs).reverse = mk_symm xs := by
simp [mk_symm]
attribute [simp] reverse_mk_symm
example (xs ys : List Nat)
: (xs ++ mk_symm ys).reverse = mk_symm ys ++ xs.reverse := by
simp
example (xs ys : List Nat) (p : List Nat → Prop)
(h : p (xs ++ mk_symm ys).reverse)
: p (mk_symm ys ++ xs.reverse) := by
simp at h; assumption
The attribute can also be applied any time after the theorem is declared:
def mk_symm (xs : List α) :=
xs ++ xs.reverse
theorem reverse_mk_symm (xs : List α)
: (mk_symm xs).reverse = mk_symm xs := by
simp [mk_symm]
example (xs ys : List Nat)
: (xs ++ mk_symm ys).reverse = mk_symm ys ++ xs.reverse := by
simp [reverse_mk_symm]
attribute [simp] reverse_mk_symm
example (xs ys : List Nat) (p : List Nat → Prop)
(h : p (xs ++ mk_symm ys).reverse)
: p (mk_symm ys ++ xs.reverse) := by
simp at h; assumption
Once the attribute is applied, however, there is no way to permanently
remove it; it persists in any file that imports the one where the
attribute is assigned. As we will discuss further in
Attributes, one can limit the scope of an attribute to the
current file or section using the local
modifier:
def mk_symm (xs : List α) :=
xs ++ xs.reverse
theorem reverse_mk_symm (xs : List α)
: (mk_symm xs).reverse = mk_symm xs := by
simp [mk_symm]
section
attribute [local simp] reverse_mk_symm
example (xs ys : List Nat)
: (xs ++ mk_symm ys).reverse = mk_symm ys ++ xs.reverse := by
simp
example (xs ys : List Nat) (p : List Nat → Prop)
(h : p (xs ++ mk_symm ys).reverse)
: p (mk_symm ys ++ xs.reverse) := by
simp at h; assumption
end
Outside the section, the simplifier will no longer use
reverse_mk_symm
by default.
Note that the various simp
options we have discussed  giving an
explicit list of rules, and using at
to specify the location  can be combined,
but the order they are listed is rigid. You can see the correct order
in an editor by placing the cursor on the simp
identifier to see
the documentation string that is associated with it.
There are two additional modifiers that are useful. By default,
simp
includes all theorems that have been marked with the
attribute [simp]
. Writing simp only
excludes these defaults,
allowing you to use a more explicitly crafted list of
rules. In the examples below, the minus sign and
only
are used to block the application of reverse_mk_symm
.
def mk_symm (xs : List α) :=
xs ++ xs.reverse
@[simp] theorem reverse_mk_symm (xs : List α)
: (mk_symm xs).reverse = mk_symm xs := by
simp [mk_symm]
example (xs ys : List Nat) (p : List Nat → Prop)
(h : p (xs ++ mk_symm ys).reverse)
: p (mk_symm ys ++ xs.reverse) := by
simp at h; assumption
example (xs ys : List Nat) (p : List Nat → Prop)
(h : p (xs ++ mk_symm ys).reverse)
: p ((mk_symm ys).reverse ++ xs.reverse) := by
simp [reverse_mk_symm] at h; assumption
example (xs ys : List Nat) (p : List Nat → Prop)
(h : p (xs ++ mk_symm ys).reverse)
: p ((mk_symm ys).reverse ++ xs.reverse) := by
simp only [List.reverse_append] at h; assumption
The simp
tactic has many configuration options. For example, we can enable contextual simplifications as follows.
example : if x = 0 then y + x = y else x ≠ 0 := by
simp (config := { contextual := true })
when contextual := true
, simp
uses the fact that x = 0
when simplifying y + x = y
, and
x ≠ 0
when simplifying the other branch. Here is another example.
example : ∀ (x : Nat) (h : x = 0), y + x = y := by
simp (config := { contextual := true })
Another useful configuration option is arith := true
which enables arithmetical simplifications. It is so useful
that simp_arith
is a shorthand for simp (config := { arith := true })
.
example : 0 < 1 + x ∧ x + y + 2 ≥ y + 1 := by
simp_arith
Split Tactic
The split
tactic is useful for breaking nested ifthenelse
and match
expressions in cases.
For a match
expression with n
cases, the split
tactic generates at most n
subgoals. Here is an example.
def f (x y z : Nat) : Nat :=
match x, y, z with
 5, _, _ => y
 _, 5, _ => y
 _, _, 5 => y
 _, _, _ => 1
example (x y z : Nat) : x ≠ 5 → y ≠ 5 → z ≠ 5 → z = w → f x y w = 1 := by
intros
simp [f]
split
. contradiction
. contradiction
. contradiction
. rfl
We can compress the tactic proof above as follows.
def f (x y z : Nat) : Nat :=
match x, y, z with
 5, _, _ => y
 _, 5, _ => y
 _, _, 5 => y
 _, _, _ => 1
example (x y z : Nat) : x ≠ 5 → y ≠ 5 → z ≠ 5 → z = w → f x y w = 1 := by
intros; simp [f]; split <;> first  contradiction  rfl
The tactic split <;> first  contradiction  rfl
first applies the split
tactic,
and then for each generated goal it tries contradiction
, and then rfl
if contradiction
fails.
Like simp
, we can apply split
to a particular hypothesis.
def g (xs ys : List Nat) : Nat :=
match xs, ys with
 [a, b], _ => a+b+1
 _, [b, c] => b+1
 _, _ => 1
example (xs ys : List Nat) (h : g xs ys = 0) : False := by
simp [g] at h; split at h <;> simp_arith at h
Extensible Tactics
In the following example, we define the notation triv
using the command syntax
.
Then, we use the command macro_rules
to specify what should
be done when triv
is used. You can provide different expansions, and the tactic
interpreter will try all of them until one succeeds.
 Define a new tactic notation
syntax "triv" : tactic
macro_rules
 `(tactic triv) => `(tactic assumption)
example (h : p) : p := by
triv
 You cannot prove the following theorem using `triv`
 example (x : α) : x = x := by
 triv
 Let's extend `triv`. The tactic interpreter
 tries all possible macro extensions for `triv` until one succeeds
macro_rules
 `(tactic triv) => `(tactic rfl)
example (x : α) : x = x := by
triv
example (x : α) (h : p) : x = x ∧ p := by
apply And.intro <;> triv
 We now add a (recursive) extension
macro_rules  `(tactic triv) => `(tactic apply And.intro <;> triv)
example (x : α) (h : p) : x = x ∧ p := by
triv
Exercises

Go back to the exercises in Chapter Propositions and Proofs and Chapter Quantifiers and Equality and redo as many as you can now with tactic proofs, using also
rw
andsimp
as appropriate. 
Use tactic combinators to obtain a one line proof of the following:
example (p q r : Prop) (hp : p)
: (p ∨ q ∨ r) ∧ (q ∨ p ∨ r) ∧ (q ∨ r ∨ p) := by
admit
Interacting with Lean
You are now familiar with the fundamentals of dependent type theory, both as a language for defining mathematical objects and a language for constructing proofs. The one thing you are missing is a mechanism for defining new data types. We will fill this gap in the next chapter, which introduces the notion of an inductive data type. But first, in this chapter, we take a break from the mechanics of type theory to explore some pragmatic aspects of interacting with Lean.
Not all of the information found here will be useful to you right away. We recommend skimming this section to get a sense of Lean's features, and then returning to it as necessary.
Importing Files
The goal of Lean's front end is to interpret user input, construct formal expressions, and check that they are well formed and type correct. Lean also supports the use of various editors, which provide continuous checking and feedback. More information can be found on the Lean documentation pages.
The definitions and theorems in Lean's standard library are spread
across multiple files. Users may also wish to make use of additional
libraries, or develop their own projects across multiple files. When
Lean starts, it automatically imports the contents of the library
Init
folder, which includes a number of fundamental definitions
and constructions. As a result, most of the examples we present here
work "out of the box."
If you want to use additional files, however, they need to be imported
manually, via an import
statement at the beginning of a file. The
command
import Bar.Baz.Blah
imports the file Bar/Baz/Blah.olean
, where the descriptions are
interpreted relative to the Lean search path. Information as to how
the search path is determined can be found on the
documentation pages.
By default, it includes the standard library directory, and (in some contexts)
the root of the user's local project.
Importing is transitive. In other words, if you import Foo
and Foo
imports Bar
,
then you also have access to the contents of Bar
, and do not need to import it explicitly.
More on Sections
Lean provides various sectioning mechanisms to help structure a
theory. You saw in Variables and Sections that the
section
command makes it possible not only to group together
elements of a theory that go together, but also to declare variables
that are inserted as arguments to theorems and definitions, as
necessary. Remember that the point of the variable
command is to
declare variables for use in theorems, as in the following example:
section
variable (x y : Nat)
def double := x + x
#check double y
#check double (2 * x)
attribute [local simp] Nat.add_assoc Nat.add_comm Nat.add_left_comm
theorem t1 : double (x + y) = double x + double y := by
simp [double]
#check t1 y
#check t1 (2 * x)
theorem t2 : double (x * y) = double x * y := by
simp [double, Nat.add_mul]
end
The definition of double
does not have to declare x
as an
argument; Lean detects the dependence and inserts it
automatically. Similarly, Lean detects the occurrence of x
in
t1
and t2
, and inserts it automatically there, too.
Note that double
does not have y
as argument. Variables are only
included in declarations where they are actually used.
More on Namespaces
In Lean, identifiers are given by hierarchical names like
Foo.Bar.baz
. We saw in Namespaces that Lean provides
mechanisms for working with hierarchical names. The command
namespace foo
causes foo
to be prepended to the name of each
definition and theorem until end foo
is encountered. The command
open foo
then creates temporary aliases to definitions and
theorems that begin with prefix foo
.
namespace Foo
def bar : Nat := 1
end Foo
open Foo
#check bar
#check Foo.bar
The following definition
def Foo.bar : Nat := 1
is treated as a macro, and expands to
namespace Foo
def bar : Nat := 1
end Foo
Although the names of theorems and definitions have to be unique, the
aliases that identify them do not. When we open a namespace, an
identifier may be ambiguous. Lean tries to use type information to
disambiguate the meaning in context, but you can always disambiguate
by giving the full name. To that end, the string _root_
is an
explicit description of the empty prefix.
def String.add (a b : String) : String :=
a ++ b
def Bool.add (a b : Bool) : Bool :=
a != b
def add (α β : Type) : Type := Sum α β
open Bool
open String
 #check add  ambiguous
#check String.add  String → String → String
#check Bool.add  Bool → Bool → Bool
#check _root_.add  Type → Type → Type
#check add "hello" "world"  String
#check add true false  Bool
#check add Nat Nat  Type
We can prevent the shorter alias from being created by using the protected
keyword:
protected def Foo.bar : Nat := 1
open Foo
 #check bar  error
#check Foo.bar
This is often used for names like Nat.rec
and Nat.recOn
, to prevent
overloading of common names.
The open
command admits variations. The command
open Nat (succ zero gcd)
#check zero  Nat
#eval gcd 15 6  3
creates aliases for only the identifiers listed. The command
open Nat hiding succ gcd
#check zero  Nat
 #eval gcd 15 6  error
#eval Nat.gcd 15 6  3
creates aliases for everything in the Nat
namespace except the identifiers listed.
open Nat renaming mul → times, add → plus
#eval plus (times 2 2) 3  7
creates aliases renaming Nat.mul
to times
and Nat.add
to plus
.
It is sometimes useful to export
aliases from one namespace to another, or to the top level. The command
export Nat (succ add sub)
creates aliases for succ
, add
, and sub
in the current
namespace, so that whenever the namespace is open, these aliases are
available. If this command is used outside a namespace, the aliases
are exported to the top level.
Attributes
The main function of Lean is to translate user input to formal
expressions that are checked by the kernel for correctness and then
stored in the environment for later use. But some commands have other
effects on the environment, either assigning attributes to objects in
the environment, defining notation, or declaring instances of type
classes, as described in Chapter Type Classes. Most of
these commands have global effects, which is to say, that they remain
in effect not only in the current file, but also in any file that
imports it. However, such commands often support the local
modifier,
which indicates that they only have effect until
the current section
or namespace
is closed, or until the end
of the current file.
In Section Using the Simplifier,
we saw that theorems can be annotated with the [simp]
attribute,
which makes them available for use by the simplifier.
The following example defines the prefix relation on lists,
proves that this relation is reflexive, and assigns the [simp]
attribute to that theorem.
def isPrefix (l₁ : List α) (l₂ : List α) : Prop :=
∃ t, l₁ ++ t = l₂
@[simp] theorem List.isPrefix_self (as : List α) : isPrefix as as :=
⟨[], by simp⟩
example : isPrefix [1, 2, 3] [1, 2, 3] := by
simp
The simplifier then proves isPrefix [1, 2, 3] [1, 2, 3]
by rewriting it to True
.
One can also assign the attribute any time after the definition takes place:
def isPrefix (l₁ : List α) (l₂ : List α) : Prop :=
∃ t, l₁ ++ t = l₂
theorem List.isPrefix_self (as : List α) : isPrefix as as :=
⟨[], by simp⟩
attribute [simp] List.isPrefix_self
In all these cases, the attribute remains in effect in any file that
imports the one in which the declaration occurs. Adding the local
modifier restricts the scope:
def isPrefix (l₁ : List α) (l₂ : List α) : Prop :=
∃ t, l₁ ++ t = l₂
section
theorem List.isPrefix_self (as : List α) : isPrefix as as :=
⟨[], by simp⟩
attribute [local simp] List.isPrefix_self
example : isPrefix [1, 2, 3] [1, 2, 3] := by
simp
end
 Error:
 example : isPrefix [1, 2, 3] [1, 2, 3] := by
 simp
For another example, we can use the instance
command to assign the
notation ≤
to the isPrefix
relation. That command, which will
be explained in Chapter Type Classes, works by
assigning an [instance]
attribute to the associated definition.
def isPrefix (l₁ : List α) (l₂ : List α) : Prop :=
∃ t, l₁ ++ t = l₂
instance : LE (List α) where
le := isPrefix
theorem List.isPrefix_self (as : List α) : as ≤ as :=
⟨[], by simp⟩
That assignment can also be made local:
def isPrefix (l₁ : List α) (l₂ : List α) : Prop :=
∃ t, l₁ ++ t = l₂
def instLe : LE (List α) :=
{ le := isPrefix }
section
attribute [local instance] instLe
example (as : List α) : as ≤ as :=
⟨[], by simp⟩
end
 Error:
 example (as : List α) : as ≤ as :=
 ⟨[], by simp⟩
In Section Notation below, we will discuss Lean's
mechanisms for defining notation, and see that they also support the
local
modifier. However, in Section Setting Options, we will
discuss Lean's mechanisms for setting options, which does not follow
this pattern: options can only be set locally, which is to say,
their scope is always restricted to the current section or current
file.
More on Implicit Arguments
In Section Implicit Arguments,
we saw that if Lean displays the type
of a term t
as {x : α} → β x
, then the curly brackets
indicate that x
has been marked as an implicit argument to
t
. This means that whenever you write t
, a placeholder, or
"hole," is inserted, so that t
is replaced by @t _
. If you
don't want that to happen, you have to write @t
instead.
Notice that implicit arguments are inserted eagerly. Suppose we define
a function f (x : Nat) {y : Nat} (z : Nat)
with the arguments
shown. Then, when we write the expression f 7
without further
arguments, it is parsed as f 7 _
. Lean offers a weaker annotation,
{{y : Nat}}
, which specifies that a placeholder should only be added
before a subsequent explicit argument. This annotation can also be
written using as ⦃y : Nat⦄
, where the unicode brackets are entered
as \{{
and \}}
, respectively. With this annotation, the
expression f 7
would be parsed as is, whereas f 7 3
would be
parsed as f 7 _ 3
, just as it would be with the strong annotation.
To illustrate the difference, consider the following example, which shows that a reflexive euclidean relation is both symmetric and transitive.
def reflexive {α : Type u} (r : α → α → Prop) : Prop :=
∀ (a : α), r a a
def symmetric {α : Type u} (r : α → α → Prop) : Prop :=
∀ {a b : α}, r a b → r b a
def transitive {α : Type u} (r : α → α → Prop) : Prop :=
∀ {a b c : α}, r a b → r b c → r a c
def euclidean {α : Type u} (r : α → α → Prop) : Prop :=
∀ {a b c : α}, r a b → r a c → r b c
theorem th1 {α : Type u} {r : α → α → Prop}
(reflr : reflexive r) (euclr : euclidean r)
: symmetric r :=
fun {a b : α} =>
fun (h : r a b) =>
show r b a from euclr h (reflr _)
theorem th2 {α : Type u} {r : α → α → Prop}
(symmr : symmetric r) (euclr : euclidean r)
: transitive r :=
fun {a b c : α} =>
fun (rab : r a b) (rbc : r b c) =>
euclr (symmr rab) rbc
theorem th3 {α : Type u} {r : α → α → Prop}
(reflr : reflexive r) (euclr : euclidean r)
: transitive r :=
th2 (th1 reflr @euclr) @euclr
variable (r : α → α → Prop)
variable (euclr : euclidean r)
#check euclr  r ?m1 ?m2 → r ?m1 ?m3 → r ?m2 ?m3
The results are broken down into small steps: th1
shows that a
relation that is reflexive and euclidean is symmetric, and th2
shows that a relation that is symmetric and euclidean is
transitive. Then th3
combines the two results. But notice that we
have to manually disable the implicit arguments in euclr
, because
otherwise too many implicit arguments are inserted. The problem goes
away if we use weak implicit arguments:
def reflexive {α : Type u} (r : α → α → Prop) : Prop :=
∀ (a : α), r a a
def symmetric {α : Type u} (r : α → α → Prop) : Prop :=
∀ {{a b : α}}, r a b → r b a
def transitive {α : Type u} (r : α → α → Prop) : Prop :=
∀ {{a b c : α}}, r a b → r b c → r a c
def euclidean {α : Type u} (r : α → α → Prop) : Prop :=
∀ {{a b c : α}}, r a b → r a c → r b c
theorem th1 {α : Type u} {r : α → α → Prop}
(reflr : reflexive r) (euclr : euclidean r)
: symmetric r :=
fun {a b : α} =>
fun (h : r a b) =>
show r b a from euclr h (reflr _)
theorem th2 {α : Type u} {r : α → α → Prop}
(symmr : symmetric r) (euclr : euclidean r)
: transitive r :=
fun {a b c : α} =>
fun (rab : r a b) (rbc : r b c) =>
euclr (symmr rab) rbc
theorem th3 {α : Type u} {r : α → α → Prop}
(reflr : reflexive r) (euclr : euclidean r)
: transitive r :=
th2 (th1 reflr euclr) euclr
variable (r : α → α → Prop)
variable (euclr : euclidean r)
#check euclr  euclidean r
There is a third kind of implicit argument that is denoted with square
brackets, [
and ]
. These are used for type classes, as
explained in Chapter Type Classes.
Notation
Identifiers in Lean can include any alphanumeric characters, including
Greek characters (other than ∀ , Σ , and λ , which, as we have seen,
have a special meaning in the dependent type theory). They can also
include subscripts, which can be entered by typing \_
followed by
the desired subscripted character.
Lean's parser is extensible, which is to say, we can define new notation.
Lean's syntax can be extended and customized by users at every level, ranging from basic "mixfix" notations to custom elaborators. In fact, all builtin syntax is parsed and processed using the same mechanisms and APIs open to users. In this section, we will describe and explain the various extension points.
While introducing new notations is a relatively rare feature in programming languages and sometimes even frowned upon because of its potential to obscure code, it is an invaluable tool in formalization for expressing established conventions and notations of the respective field succinctly in code. Going beyond basic notations, Lean's ability to factor out common boilerplate code into (wellbehaved) macros and to embed entire custom domain specific languages (DSLs) to textually encode subproblems efficiently and readably can be of great benefit to both programmers and proof engineers alike.
Notations and Precedence
The most basic syntax extension commands allow introducing new (or overloading existing) prefix, infix, and postfix operators.
infixl:65 " + " => HAdd.hAdd  leftassociative
infix:50 " = " => Eq  nonassociative
infixr:80 " ^ " => HPow.hPow  rightassociative
prefix:100 "" => Neg.neg
set_option quotPrecheck false
postfix:max "⁻¹" => Inv.inv
After the initial command name describing the operator kind (its
"fixity"), we give the parsing precedence of the operator preceded
by a colon :
, then a new or existing token surrounded by double
quotes (the whitespace is used for pretty printing), then the function
this operator should be translated to after the arrow =>
.
The precedence is a natural number describing how "tightly" an operator binds to its arguments, encoding the order of operations. We can make this more precise by looking at the commands the above unfold to:
notation:65 lhs:65 " + " rhs:66 => HAdd.hAdd lhs rhs
notation:50 lhs:51 " = " rhs:51 => Eq lhs rhs
notation:80 lhs:81 " ^ " rhs:80 => HPow.hPow lhs rhs
notation:100 "" arg:100 => Neg.neg arg
set_option quotPrecheck false
notation:1024 arg:1024 "⁻¹" => Inv.inv arg  `max` is a shorthand for precedence 1024
It turns out that all commands from the first code block are in fact
command macros translating to the more general notation
command.
We will learn about writing such macros below. Instead of a single
token, the notation
command accepts a mixed sequence of tokens and
named term placeholders with precedences, which can be referenced on
the righthand side of =>
and will be replaced by the respective
term parsed at that position. A placeholder with precedence p
accepts only notations with precedence at least p
in that place.
Thus the string a + b + c
cannot be parsed as the equivalent of
a + (b + c)
because the righthand side operand of an infixl
notation
has precedence one greater than the notation itself. In contrast,
infixr
reuses the notation's precedence for the righthand side
operand, so a ^ b ^ c
can be parsed as a ^ (b ^ c)
. Note that
if we used notation
directly to introduce an infix notation like
set_option quotPrecheck false
notation:65 lhs:65 " ~ " rhs:65 => wobble lhs rhs
where the precedences do not sufficiently determine associativity,
Lean's parser will default to right associativity. More precisely,
Lean's parser follows a local longest parse rule in the presence of
ambiguous grammars: when parsing the righthand side of a ~
in
a ~ b ~ c
, it will continue parsing as long as possible (as the current
precedence allows), not stopping after b
but parsing ~ c
as well.
Thus the term is equivalent to a ~ (b ~ c)
.
As mentioned above, the notation
command allows us to define
arbitrary mixfix syntax freely mixing tokens and placeholders.
set_option quotPrecheck false
notation:max "(" e ")" => e
notation:10 Γ " ⊢ " e " : " τ => Typing Γ e τ
Placeholders without precedence default to 0
, i.e. they accept notations of any precedence in their place.
If two notations overlap, we again apply the longest parse rule:
notation:65 a " + " b:66 " + " c:66 => a + b  c
#eval 1 + 2 + 3  0
The new notation is preferred to the binary notation since the latter,
before chaining, would stop parsing after 1 + 2
. If there are
multiple notations accepting the same longest parse, the choice will
be delayed until elaboration, which will fail unless exactly one
overload is type correct.
Coercions
In Lean, the type of natural numbers, Nat
, is different from the
type of integers, Int
. But there is a function Int.ofNat
that
embeds the natural numbers in the integers, meaning that we can view
any natural number as an integer, when needed. Lean has mechanisms to
detect and insert coercions of this sort.
variable (m n : Nat)
variable (i j : Int)
#check i + m  i + Int.ofNat m : Int
#check i + m + j  i + Int.ofNat m + j : Int
#check i + m + n  i + Int.ofNat m + Int.ofNat n : Int
Displaying Information
There are a number of ways in which you can query Lean for information
about its current state and the objects and theorems that are
available in the current context. You have already seen two of the
most common ones, #check
and #eval
. Remember that #check
is often used in conjunction with the @
operator, which makes all
of the arguments to a theorem or definition explicit. In addition, you
can use the #print
command to get information about any
identifier. If the identifier denotes a definition or theorem, Lean
prints the type of the symbol, and its definition. If it is a constant
or an axiom, Lean indicates that fact, and shows the type.
 examples with equality
#check Eq
#check @Eq
#check Eq.symm
#check @Eq.symm
#print Eq.symm
 examples with And
#check And
#check And.intro
#check @And.intro
 a userdefined function
def foo {α : Type u} (x : α) : α := x
#check foo
#check @foo
#print foo
Setting Options
Lean maintains a number of internal variables that can be set by users to control its behavior. The syntax for doing so is as follows:
set_option <name> <value>
One very useful family of options controls the way Lean's pretty printer displays terms. The following options take an input of true or false:
pp.explicit : display implicit arguments
pp.universes : display hidden universe parameters
pp.notation : display output using defined notations
As an example, the following settings yield much longer output:
set_option pp.explicit true
set_option pp.universes true
set_option pp.notation false
#check 2 + 2 = 4
#reduce (fun x => x + 2) = (fun x => x + 3)
#check (fun x => x + 1) 1
The command set_option pp.all true
carries out these settings all
at once, whereas set_option pp.all false
reverts to the previous
values. Pretty printing additional information is often very useful
when you are debugging a proof, or trying to understand a cryptic
error message. Too much information can be overwhelming, though, and
Lean's defaults are generally sufficient for ordinary interactions.
Using the Library
To use Lean effectively you will inevitably need to make use of
definitions and theorems in the library. Recall that the import
command at the beginning of a file imports previously compiled results
from other files, and that importing is transitive; if you import
Foo
and Foo
imports Bar
, then the definitions and theorems
from Bar
are available to you as well. But the act of opening a
namespace, which provides shorter names, does not carry over. In each
file, you need to open the namespaces you wish to use.
In general, it is important for you to be familiar with the library and its contents, so you know what theorems, definitions, notations, and resources are available to you. Below we will see that Lean's editor modes can also help you find things you need, but studying the contents of the library directly is often unavoidable. Lean's standard library can be found online, on GitHub:
You can see the contents of these directories and files using GitHub's
browser interface. If you have installed Lean on your own computer,
you can find the library in the lean
folder, and explore it with
your file manager. Comment headers at the top of each file provide
additional information.
Lean's library developers follow general naming guidelines to make it
easier to guess the name of a theorem you need, or to find it using
tab completion in editors with a Lean mode that supports this, which
is discussed in the next section. Identifiers are generally
camelCase
, and types are CamelCase
. For theorem names,
we rely on descriptive names where the different components are separated
by _
s. Often the name of theorem simply describes the conclusion:
#check Nat.succ_ne_zero
#check Nat.zero_add
#check Nat.mul_one
#check Nat.le_of_succ_le_succ
Remember that identifiers in Lean can be organized into hierarchical
namespaces. For example, the theorem named le_of_succ_le_succ
in the
namespace Nat
has full name Nat.le_of_succ_le_succ
, but the shorter
name is made available by the command open Nat
(for names not marked as
protected
). We will see in Chapter Inductive Types
and Chapter Structures and Records
that defining structures and inductive data types in Lean generates
associated operations, and these are stored in
a namespace with the same name as the type under definition. For
example, the product type comes with the following operations:
#check @Prod.mk
#check @Prod.fst
#check @Prod.snd
#check @Prod.rec
The first is used to construct a pair, whereas the next two,
Prod.fst
and Prod.snd
, project the two elements. The last,
Prod.rec
, provides another mechanism for defining functions on a
product in terms of a function on the two components. Names like
Prod.rec
are protected, which means that one has to use the full
name even when the Prod
namespace is open.
With the propositions as types correspondence, logical connectives are also instances of inductive types, and so we tend to use dot notation for them as well:
#check @And.intro
#check @And.casesOn
#check @And.left
#check @And.right
#check @Or.inl
#check @Or.inr
#check @Or.elim
#check @Exists.intro
#check @Exists.elim
#check @Eq.refl
#check @Eq.subst
Auto Bound Implicit Arguments
In the previous section, we have shown how implicit arguments make functions more convenient to use.
However, functions such as compose
are still quite verbose to define. Note that the universe
polymorphic compose
is even more verbose than the one previously defined.
universe u v w
def compose {α : Type u} {β : Type v} {γ : Type w}
(g : β → γ) (f : α → β) (x : α) : γ :=
g (f x)
You can avoid the universe
command by providing the universe parameters when defining compose
.
def compose.{u, v, w}
{α : Type u} {β : Type v} {γ : Type w}
(g : β → γ) (f : α → β) (x : α) : γ :=
g (f x)
Lean 4 supports a new feature called auto bound implicit arguments. It makes functions such as
compose
much more convenient to write. When Lean processes the header of a declaration,
any unbound identifier is automatically added as an implicit argument if it is a single lower case or
greek letter. With this feature we can write compose
as
def compose (g : β → γ) (f : α → β) (x : α) : γ :=
g (f x)
#check @compose
 {β : Sort u_1} → {γ : Sort u_2} → {α : Sort u_3} → (β → γ) → (α → β) → α → γ
Note that Lean inferred a more general type using Sort
instead of Type
.
Although we love this feature and use it extensively when implementing Lean,
we realize some users may feel uncomfortable with it. Thus, you can disable it using
the command set_option autoImplicit false
.
set_option autoImplicit false
/ The following definition produces `unknown identifier` errors /
 def compose (g : β → γ) (f : α → β) (x : α) : γ :=
 g (f x)
Implicit Lambdas
In Lean 3 stdlib, we find many
instances of the dreadful @
+_
idiom.
It is often used when the expected type is a function type with implicit arguments,
and we have a constant (reader_t.pure
in the example) which also takes implicit arguments. In Lean 4, the elaborator automatically introduces lambdas
for consuming implicit arguments. We are still exploring this feature and analyzing its impact, but the experience so far has been very positive. Here is the example from the link above using Lean 4 implicit lambdas.
variable (ρ : Type) (m : Type → Type) [Monad m]
instance : Monad (ReaderT ρ m) where
pure := ReaderT.pure
bind := ReaderT.bind
Users can disable the implicit lambda feature by using @
or writing
a lambda expression with {}
or []
binder annotations. Here are
few examples
namespace ex2
def id1 : {α : Type} → α → α :=
fun x => x
def listId : List ({α : Type} → α → α) :=
(fun x => x) :: []
 In this example, implicit lambda introduction has been disabled because
 we use `@` before `fun`
def id2 : {α : Type} → α → α :=
@fun α (x : α) => id1 x
def id3 : {α : Type} → α → α :=
@fun α x => id1 x
def id4 : {α : Type} → α → α :=
fun x => id1 x
 In this example, implicit lambda introduction has been disabled
 because we used the binder annotation `{...}`
def id5 : {α : Type} → α → α :=
fun {α} x => id1 x
end ex2
Sugar for Simple Functions
In Lean 3, we can create simple functions from infix operators by
using parentheses. For example, (+1)
is sugar for fun x, x + 1
. In
Lean 4, we generalize this notation using ·
as a placeholder. Here
are a few examples:
namespace ex3
#check (· + 1)
 fun a => a + 1
#check (2  ·)
 fun a => 2  a
#eval [1, 2, 3, 4, 5].foldl (·*·) 1
 120
def f (x y z : Nat) :=
x + y + z
#check (f · 1 ·)
 fun a b => f a 1 b
#eval [(1, 2), (3, 4), (5, 6)].map (·.1)
 [1, 3, 5]
end ex3
As in Lean 3, the notation is activated using parentheses, and the lambda abstraction is created by collecting the nested ·
s.
The collection is interrupted by nested parentheses. In the following example, two different lambda expressions are created.
#check (Prod.mk · (· + 1))
 fun a => (a, fun b => b + 1)
Named Arguments
Named arguments enable you to specify an argument for a parameter by matching the argument with its name rather than with its position in the parameter list. If you don't remember the order of the parameters but know their names, you can send the arguments in any order. You may also provide the value for an implicit parameter when Lean failed to infer it. Named arguments also improve the readability of your code by identifying what each argument represents.
def sum (xs : List Nat) :=
xs.foldl (init := 0) (·+·)
#eval sum [1, 2, 3, 4]
 10
example {a b : Nat} {p : Nat → Nat → Nat → Prop} (h₁ : p a b b) (h₂ : b = a)
: p a a b :=
Eq.subst (motive := fun x => p a x b) h₂ h₁
In the following examples, we illustrate the interaction between named and default arguments.
def f (x : Nat) (y : Nat := 1) (w : Nat := 2) (z : Nat) :=
x + y + w  z
example (x z : Nat) : f (z := z) x = x + 1 + 2  z := rfl
example (x z : Nat) : f x (z := z) = x + 1 + 2  z := rfl
example (x y : Nat) : f x y = fun z => x + y + 2  z := rfl
example : f = (fun x z => x + 1 + 2  z) := rfl
example (x : Nat) : f x = fun z => x + 1 + 2  z := rfl
example (y : Nat) : f (y := 5) = fun x z => x + 5 + 2  z := rfl
def g {α} [Add α] (a : α) (b? : Option α := none) (c : α) : α :=
match b? with
 none => a + c
 some b => a + b + c
variable {α} [Add α]
example : g = fun (a c : α) => a + c := rfl
example (x : α) : g (c := x) = fun (a : α) => a + x := rfl
example (x : α) : g (b? := some x) = fun (a c : α) => a + x + c := rfl
example (x : α) : g x = fun (c : α) => x + c := rfl
example (x y : α) : g x y = fun (c : α) => x + y + c := rfl
You can use ..
to provide missing explicit arguments as _
.
This feature combined with named arguments is useful for writing patterns. Here is an example:
inductive Term where
 var (name : String)
 num (val : Nat)
 app (fn : Term) (arg : Term)
 lambda (name : String) (type : Term) (body : Term)
def getBinderName : Term → Option String
 Term.lambda (name := n) .. => some n
 _ => none
def getBinderType : Term → Option Term
 Term.lambda (type := t) .. => some t
 _ => none
Ellipses are also useful when explicit arguments can be automatically
inferred by Lean, and we want to avoid a sequence of _
s.
example (f : Nat → Nat) (a b c : Nat) : f (a + b + c) = f (a + (b + c)) :=
congrArg f (Nat.add_assoc ..)
Inductive Types
We have seen that Lean's formal foundation includes basic types,
Prop, Type 0, Type 1, Type 2, ...
, and allows for the formation of
dependent function types, (x : α) → β
. In the examples, we have
also made use of additional types like Bool
, Nat
, and Int
,
and type constructors, like List
, and product, ×
. In fact, in
Lean's library, every concrete type other than the universes and every
type constructor other than dependent arrows is an instance of a general family of
type constructions known as inductive types. It is remarkable that
it is possible to construct a substantial edifice of mathematics based
on nothing more than the type universes, dependent arrow types, and inductive
types; everything else follows from those.
Intuitively, an inductive type is built up from a specified list of constructors. In Lean, the syntax for specifying such a type is as follows:
inductive Foo where
 constructor₁ : ... → Foo
 constructor₂ : ... → Foo
...
 constructorₙ : ... → Foo
The intuition is that each constructor specifies a way of building new
objects of Foo
, possibly from previously constructed values. The
type Foo
consists of nothing more than the objects that are
constructed in this way. The first character 
in an inductive
declaration is optional. We can also separate constructors using a
comma instead of 
.
We will see below that the arguments of the constructors can include
objects of type Foo
, subject to a certain "positivity" constraint,
which guarantees that elements of Foo
are built from the bottom
up. Roughly speaking, each ...
can be any arrow type constructed from
Foo
and previously defined types, in which Foo
appears, if at
all, only as the "target" of the dependent arrow type.
We will provide a number of examples of inductive types. We will also consider slight generalizations of the scheme above, to mutually defined inductive types, and socalled inductive families.
As with the logical connectives, every inductive type comes with introduction rules, which show how to construct an element of the type, and elimination rules, which show how to "use" an element of the type in another construction. The analogy to the logical connectives should not come as a surprise; as we will see below, they, too, are examples of inductive type constructions. You have already seen the introduction rules for an inductive type: they are just the constructors that are specified in the definition of the type. The elimination rules provide for a principle of recursion on the type, which includes, as a special case, a principle of induction as well.
In the next chapter, we will describe Lean's function definition package, which provides even more convenient ways to define functions on inductive types and carry out inductive proofs. But because the notion of an inductive type is so fundamental, we feel it is important to start with a lowlevel, handson understanding. We will start with some basic examples of inductive types, and work our way up to more elaborate and complex examples.
Enumerated Types
The simplest kind of inductive type is a type with a finite, enumerated list of elements.
inductive Weekday where
 sunday : Weekday
 monday : Weekday
 tuesday : Weekday
 wednesday : Weekday
 thursday : Weekday
 friday : Weekday
 saturday : Weekday
The inductive
command creates a new type, Weekday
. The
constructors all live in the Weekday
namespace.
inductive Weekday where
 sunday : Weekday
 monday : Weekday
 tuesday : Weekday
 wednesday : Weekday
 thursday : Weekday
 friday : Weekday
 saturday : Weekday
#check Weekday.sunday
#check Weekday.monday
open Weekday
#check sunday
#check monday
You can omit : Weekday
when declaring the Weekday
inductive type.
inductive Weekday where
 sunday
 monday
 tuesday
 wednesday
 thursday
 friday
 saturday
Think of sunday
, monday
, ... , saturday
as
being distinct elements of Weekday
, with no other distinguishing
properties. The elimination principle, Weekday.rec
, is defined
along with the type Weekday
and its constructors. It is also known
as a recursor, and it is what makes the type "inductive": it allows
us to define a function on Weekday
by assigning values
corresponding to each constructor. The intuition is that an inductive
type is exhaustively generated by the constructors, and has no
elements beyond those they construct.
We will use the match
expression to define a function from Weekday
to the natural numbers:
inductive Weekday where
 sunday : Weekday
 monday : Weekday
 tuesday : Weekday
 wednesday : Weekday
 thursday : Weekday
 friday : Weekday
 saturday : Weekday
open Weekday
def numberOfDay (d : Weekday) : Nat :=
match d with
 sunday => 1
 monday => 2
 tuesday => 3
 wednesday => 4
 thursday => 5
 friday => 6
 saturday => 7
#eval numberOfDay Weekday.sunday  1
#eval numberOfDay Weekday.monday  2
#eval numberOfDay Weekday.tuesday  3
Note that the match
expression is compiled using the recursor Weekday.rec
generated when
you declare the inductive type.
inductive Weekday where
 sunday : Weekday
 monday : Weekday
 tuesday : Weekday
 wednesday : Weekday
 thursday : Weekday
 friday : Weekday
 saturday : Weekday
open Weekday
def numberOfDay (d : Weekday) : Nat :=
match d with
 sunday => 1
 monday => 2
 tuesday => 3
 wednesday => 4
 thursday => 5
 friday => 6
 saturday => 7
set_option pp.all true
#print numberOfDay
 ... numberOfDay.match_1
#print numberOfDay.match_1
 ... Weekday.casesOn ...
#print Weekday.casesOn
 ... Weekday.rec ...
#check @Weekday.rec
/
@Weekday.rec.{u}
: {motive : Weekday → Sort u} →
motive Weekday.sunday →
motive Weekday.monday →
motive Weekday.tuesday →
motive Weekday.wednesday →
motive Weekday.thursday →
motive Weekday.friday →
motive Weekday.saturday →
(t : Weekday) → motive t
/
When declaring an inductive datatype, you can use deriving Repr
to instruct
Lean to generate a function that converts Weekday
objects into text.
This function is used by the #eval
command to display Weekday
objects.
inductive Weekday where
 sunday
 monday
 tuesday
 wednesday
 thursday
 friday
 saturday
deriving Repr
open Weekday
#eval tuesday  Weekday.tuesday
It is often useful to group definitions and theorems related to a
structure in a namespace with the same name. For example, we can put
the numberOfDay
function in the Weekday
namespace. We are
then allowed to use the shorter name when we open the namespace.
We can define functions from Weekday
to Weekday
:
inductive Weekday where
 sunday : Weekday
 monday : Weekday
 tuesday : Weekday
 wednesday : Weekday
 thursday : Weekday
 friday : Weekday
 saturday : Weekday
deriving Repr
namespace Weekday
def next (d : Weekday) : Weekday :=
match d with
 sunday => monday
 monday => tuesday
 tuesday => wednesday
 wednesday => thursday
 thursday => friday
 friday => saturday
 saturday => sunday
def previous (d : Weekday) : Weekday :=
match d with
 sunday => saturday
 monday => sunday
 tuesday => monday
 wednesday => tuesday
 thursday => wednesday
 friday => thursday
 saturday => friday
#eval next (next tuesday)  Weekday.thursday
#eval next (previous tuesday)  Weekday.tuesday
example : next (previous tuesday) = tuesday :=
rfl
end Weekday
How can we prove the general theorem that next (previous d) = d
for any Weekday d
? You can use match
to provide a proof of the claim for each
constructor:
inductive Weekday where
 sunday : Weekday
 monday : Weekday
 tuesday : Weekday
 wednesday : Weekday
 thursday : Weekday
 friday : Weekday
 saturday : Weekday
deriving Repr
namespace Weekday
def next (d : Weekday) : Weekday :=
match d with
 sunday => monday
 monday => tuesday
 tuesday => wednesday
 wednesday => thursday
 thursday => friday
 friday => saturday
 saturday => sunday
def previous (d : Weekday) : Weekday :=
match d with
 sunday => saturday
 monday => sunday
 tuesday => monday
 wednesday => tuesday
 thursday => wednesday
 friday => thursday
 saturday => friday
def next_previous (d : Weekday) : next (previous d) = d :=
match d with
 sunday => rfl
 monday => rfl
 tuesday => rfl
 wednesday => rfl
 thursday => rfl
 friday => rfl
 saturday => rfl
Using a tactic proof, we can be even more concise:
inductive Weekday where
 sunday : Weekday
 monday : Weekday
 tuesday : Weekday
 wednesday : Weekday
 thursday : Weekday
 friday : Weekday
 saturday : Weekday
deriving Repr
namespace Weekday
def next (d : Weekday) : Weekday :=
match d with
 sunday => monday
 monday => tuesday
 tuesday => wednesday
 wednesday => thursday
 thursday => friday
 friday => saturday
 saturday => sunday
def previous (d : Weekday) : Weekday :=
match d with
 sunday => saturday
 monday => sunday
 tuesday => monday
 wednesday => tuesday
 thursday => wednesday
 friday => thursday
 saturday => friday
def next_previous (d : Weekday) : next (previous d) = d := by
cases d <;> rfl
Tactics for Inductive Types below will introduce additional tactics that are specifically designed to make use of inductive types.
Notice that, under the propositionsastypes correspondence, we can
use match
to prove theorems as well as define functions. In other
words, under the propositionsastypes correspondence, the proof by
cases is a kind of definition by cases, where what is being "defined"
is a proof instead of a piece of data.
The Bool
type in the Lean library is an instance of
enumerated type.
namespace Hidden
inductive Bool where
 false : Bool
 true : Bool
end Hidden
(To run these examples, we put them in a namespace called Hidden
,
so that a name like Bool
does not conflict with the Bool
in
the standard library. This is necessary because these types are part
of the Lean "prelude" that is automatically imported when the system
is started.)
As an exercise, you should think about what the introduction and
elimination rules for these types do. As a further exercise, we
suggest defining boolean operations and
, or
, not
on the
Bool
type, and verifying common identities. Note that you can define a
binary operation like and
using match
:
namespace Hidden
def and (a b : Bool) : Bool :=
match a with
 true => b
 false => false
end Hidden
Similarly, most identities can be proved by introducing suitable match
, and then using rfl
.
Constructors with Arguments
Enumerated types are a very special case of inductive types, in which the constructors take no arguments at all. In general, a "construction" can depend on data, which is then represented in the constructed argument. Consider the definitions of the product type and sum type in the library:
namespace Hidden
inductive Prod (α : Type u) (β : Type v)
 mk : α → β → Prod α β
inductive Sum (α : Type u) (β : Type v) where
 inl : α → Sum α β
 inr : β → Sum α β
end Hidden
Consider what is going on in these examples.
The product type has one constructor, Prod.mk
,
which takes two arguments. To define a function on Prod α β
, we
can assume the input is of the form Prod.mk a b
, and we have to
specify the output, in terms of a
and b
. We can use this to
define the two projections for Prod
. Remember that the standard
library defines notation α × β
for Prod α β
and (a, b)
for
Prod.mk a b
.
namespace Hidden
inductive Prod (α : Type u) (β : Type v)
 mk : α → β → Prod α β
def fst {α : Type u} {β : Type v} (p : Prod α β) : α :=
match p with
 Prod.mk a b => a
def snd {α : Type u} {β : Type v} (p : Prod α β) : β :=
match p with
 Prod.mk a b => b
end Hidden
The function fst
takes a pair, p
. The match
interprets
p
as a pair, Prod.mk a b
. Recall also from Dependent Type Theory
that to give these definitions the greatest generality possible, we allow
the types α
and β
to belong to any universe.
Here is another example where we use the recursor Prod.casesOn
instead
of match
.
def prod_example (p : Bool × Nat) : Nat :=
Prod.casesOn (motive := fun _ => Nat) p (fun b n => cond b (2 * n) (2 * n + 1))
#eval prod_example (true, 3)
#eval prod_example (false, 3)
The argument motive
is used to specify the type of the object you want to
construct, and it is a function because it may depend on the pair.
The cond
function is a boolean conditional: cond b t1 t2
returns t1
if b
is true, and t2
otherwise.
The function prod_example
takes a pair consisting of a boolean,
b
, and a number, n
, and returns either 2 * n
or 2 * n + 1
according to whether b
is true or false.
In contrast, the sum type has two constructors, inl
and inr
(for "insert left" and "insert right"), each of which takes one
(explicit) argument. To define a function on Sum α β
, we have to
handle two cases: either the input is of the form inl a
, in which
case we have to specify an output value in terms of a
, or the
input is of the form inr b
, in which case we have to specify an
output value in terms of b
.
def sum_example (s : Sum Nat Nat) : Nat :=
Sum.casesOn (motive := fun _ => Nat) s
(fun n => 2 * n)
(fun n => 2 * n + 1)
#eval sum_example (Sum.inl 3)
#eval sum_example (Sum.inr 3)
This example is similar to the previous one, but now an input to
sum_example
is implicitly either of the form inl n
or inr n
.
In the first case, the function returns 2 * n
, and the second
case, it returns 2 * n + 1
.
Notice that the product type depends on parameters α β : Type
which are arguments to the constructors as well as Prod
. Lean
detects when these arguments can be inferred from later arguments to a
constructor or the return type, and makes them implicit in that case.
In Section Defining the Natural Numbers we will see what happens when the constructor of an inductive type takes arguments from the inductive type itself. What characterizes the examples we consider in this section is that each constructor relies only on previously specified types.
Notice that a type with multiple constructors is disjunctive: an
element of Sum α β
is either of the form inl a
or of the
form inl b
. A constructor with multiple arguments introduces
conjunctive information: from an element Prod.mk a b
of
Prod α β
we can extract a
and b
. An arbitrary inductive type can
include both features, by having any number of constructors, each of
which takes any number of arguments.
As with function definitions, Lean's inductive definition syntax will let you put named arguments to the constructors before the colon:
namespace Hidden
inductive Prod (α : Type u) (β : Type v) where
 mk (fst : α) (snd : β) : Prod α β
inductive Sum (α : Type u) (β : Type v) where
 inl (a : α) : Sum α β
 inr (b : β) : Sum α β
end Hidden
The results of these definitions are essentially the same as the ones given earlier in this section.
A type, like Prod
, that has only one constructor is purely
conjunctive: the constructor simply packs the list of arguments into a
single piece of data, essentially a tuple where the type of subsequent
arguments can depend on the type of the initial argument. We can also
think of such a type as a "record" or a "structure". In Lean, the
keyword structure
can be used to define such an inductive type as
well as its projections, at the same time.
namespace Hidden
structure Prod (α : Type u) (β : Type v) where
mk :: (fst : α) (snd : β)
end Hidden
This example simultaneously introduces the inductive type, Prod
,
its constructor, mk
, the usual eliminators (rec
and
recOn
), as well as the projections, fst
and snd
, as
defined above.
If you do not name the constructor, Lean uses mk
as a default. For
example, the following defines a record to store a color as a triple
of RGB values:
structure Color where
(red : Nat) (green : Nat) (blue : Nat)
deriving Repr
def yellow := Color.mk 255 255 0
#eval Color.red yellow
The definition of yellow
forms the record with the three values
shown, and the projection Color.red
returns the red component.
You can avoid the parentheses if you add a line break between each field.
structure Color where
red : Nat
green : Nat
blue : Nat
deriving Repr
The structure
command is especially useful for defining algebraic
structures, and Lean provides substantial infrastructure to support
working with them. Here, for example, is the definition of a
semigroup:
structure Semigroup where
carrier : Type u
mul : carrier → carrier → carrier
mul_assoc : ∀ a b c, mul (mul a b) c = mul a (mul b c)
We will see more examples in Chapter Structures and Records.
We have already discussed the dependent product type Sigma
:
namespace Hidden
inductive Sigma {α : Type u} (β : α → Type v) where
 mk : (a : α) → β a → Sigma β
end Hidden
Two more examples of inductive types in the library are the following:
namespace Hidden
inductive Option (α : Type u) where
 none : Option α
 some : α → Option α
inductive Inhabited (α : Type u) where
 mk : α → Inhabited α
end Hidden
In the semantics of dependent type theory, there is no builtin notion
of a partial function. Every element of a function type α → β
or a
dependent function type (a : α) → β
is assumed to have a value
at every input. The Option
type provides a way of representing partial functions. An
element of Option β
is either none
or of the form some b
,
for some value b : β
. Thus we can think of an element f
of the
type α → Option β
as being a partial function from α
to β
:
for every a : α
, f a
either returns none
, indicating
f a
is "undefined", or some b
.
An element of Inhabited α
is simply a witness to the fact that
there is an element of α
. Later, we will see that Inhabited
is
an example of a type class in Lean: Lean can be instructed that
suitable base types are inhabited, and can automatically infer that
other constructed types are inhabited on that basis.
As exercises, we encourage you to develop a notion of composition for
partial functions from α
to β
and β
to γ
, and show
that it behaves as expected. We also encourage you to show that
Bool
and Nat
are inhabited, that the product of two inhabited
types is inhabited, and that the type of functions to an inhabited
type is inhabited.
Inductively Defined Propositions
Inductively defined types can live in any type universe, including the
bottommost one, Prop
. In fact, this is exactly how the logical
connectives are defined.
namespace Hidden
inductive False : Prop
inductive True : Prop where
 intro : True
inductive And (a b : Prop) : Prop where
 intro : a → b → And a b
inductive Or (a b : Prop) : Prop where
 inl : a → Or a b
 inr : b → Or a b
end Hidden
You should think about how these give rise to the introduction and
elimination rules that you have already seen. There are rules that
govern what the eliminator of an inductive type can eliminate to,
that is, what kinds of types can be the target of a recursor. Roughly
speaking, what characterizes inductive types in Prop
is that one
can only eliminate to other types in Prop
. This is consistent with
the understanding that if p : Prop
, an element hp : p
carries
no data. There is a small exception to this rule, however, which we
will discuss below, in Section Inductive Families.
Even the existential quantifier is inductively defined:
namespace Hidden
inductive Exists {α : Sort u} (p : α → Prop) : Prop where
 intro (w : α) (h : p w) : Exists p
end Hidden
Keep in mind that the notation ∃ x : α, p
is syntactic sugar for Exists (fun x : α => p)
.
The definitions of False
, True
, And
, and Or
are
perfectly analogous to the definitions of Empty
, Unit
,
Prod
, and Sum
. The difference is that the first group yields
elements of Prop
, and the second yields elements of Type u
for
some u
. In a similar way, ∃ x : α, p
is a Prop
valued
variant of Σ x : α, p
.
This is a good place to mention another inductive type, denoted
{x : α // p}
, which is sort of a hybrid between
∃ x : α, P
and Σ x : α, P
.
namespace Hidden
inductive Subtype {α : Type u} (p : α → Prop) where
 mk : (x : α) → p x → Subtype p
end Hidden
In fact, in Lean, Subtype
is defined using the structure command:
namespace Hidden
structure Subtype {α : Sort u} (p : α → Prop) where
val : α
property : p val
end Hidden
The notation {x : α // p x}
is syntactic sugar for Subtype (fun x : α => p x)
.
It is modeled after subset notation in set theory: the idea is that {x : α // p x}
denotes the collection of elements of α
that have property p
.
Defining the Natural Numbers
The inductively defined types we have seen so far are "flat":
constructors wrap data and insert it into a type, and the
corresponding recursor unpacks the data and acts on it. Things get
much more interesting when the constructors act on elements of the
very type being defined. A canonical example is the type Nat
of
natural numbers:
namespace Hidden
inductive Nat where
 zero : Nat
 succ : Nat → Nat
end Hidden
There are two constructors. We start with zero : Nat
; it takes
no arguments, so we have it from the start. In contrast, the
constructor succ
can only be applied to a previously constructed
Nat
. Applying it to zero
yields succ zero : Nat
. Applying
it again yields succ (succ zero) : Nat
, and so on. Intuitively,
Nat
is the "smallest" type with these constructors, meaning that
it is exhaustively (and freely) generated by starting with zero
and applying succ
repeatedly.
As before, the recursor for Nat
is designed to define a dependent
function f
from Nat
to any domain, that is, an element f
of (n : Nat) → motive n
for some motive : Nat → Sort u
.
It has to handle two cases: the case where the input is zero
, and the case where
the input is of the form succ n
for some n : Nat
. In the first
case, we simply specify a target value with the appropriate type, as
before. In the second case, however, the recursor can assume that a
value of f
at n
has already been computed. As a result, the
next argument to the recursor specifies a value for f (succ n)
in
terms of n
and f n
. If we check the type of the recursor,
namespace Hidden
inductive Nat where
 zero : Nat
 succ : Nat → Nat
#check @Nat.rec
end Hidden
you find the following:
{motive : Nat → Sort u}
→ motive Nat.zero
→ ((n : Nat) → motive n → motive (Nat.succ n))
→ (t : Nat) → motive t
The implicit argument, motive
, is the codomain of the function being defined.
In type theory it is common to say motive
is the motive for the elimination/recursion,
since it describes the kind of object we wish to construct.
The next two arguments specify how to compute the zero and successor cases, as described above.
They are also known as the minor premises.
Finally, the t : Nat
, is the input to the function. It is also known as the major premise.
The Nat.recOn
is similar to Nat.rec
but the major premise occurs before the minor premises.
@Nat.recOn :
{motive : Nat → Sort u}
→ (t : Nat)
→ motive Nat.zero
→ ((n : Nat) → motive n → motive (Nat.succ n))
→ motive t
Consider, for example, the addition function add m n
on the
natural numbers. Fixing m
, we can define addition by recursion on
n
. In the base case, we set add m zero
to m
. In the
successor step, assuming the value add m n
is already determined,
we define add m (succ n)
to be succ (add m n)
.
namespace Hidden
inductive Nat where
 zero : Nat
 succ : Nat → Nat
deriving Repr
def add (m n : Nat) : Nat :=
match n with
 Nat.zero => m
 Nat.succ n => Nat.succ (add m n)
open Nat
#eval add (succ (succ zero)) (succ zero)
end Hidden
It is useful to put such definitions into a namespace, Nat
. We can
then go on to define familiar notation in that namespace. The two
defining equations for addition now hold definitionally:
namespace Hidden
inductive Nat where
 zero : Nat
 succ : Nat → Nat
deriving Repr
namespace Nat
def add (m n : Nat) : Nat :=
match n with
 Nat.zero => m
 Nat.succ n => Nat.succ (add m n)
instance : Add Nat where
add := add
theorem add_zero (m : Nat) : m + zero = m := rfl
theorem add_succ (m n : Nat) : m + succ n = succ (m + n) := rfl
end Nat
end Hidden
We will explain how the instance
command works in
Chapter Type Classes. In the examples below, we will use
Lean's version of the natural numbers.
Proving a fact like zero + m = m
, however, requires a proof by induction.
As observed above, the induction principle is just a special case of the recursion principle,
when the codomain motive n
is an element of Prop
. It represents the familiar
pattern of an inductive proof: to prove ∀ n, motive n
, first prove motive 0
,
and then, for arbitrary n
, assume ih : motive n
and prove motive (succ n)
.
namespace Hidden
open Nat
theorem zero_add (n : Nat) : 0 + n = n :=
Nat.recOn (motive := fun x => 0 + x = x)
n
(show 0 + 0 = 0 from rfl)
(fun (n : Nat) (ih : 0 + n = n) =>
show 0 + succ n = succ n from
calc 0 + succ n
_ = succ (0 + n) := rfl
_ = succ n := by rw [ih])
end Hidden
Notice that, once again, when Nat.recOn
is used in the context of
a proof, it is really the induction principle in disguise. The
rewrite
and simp
tactics tend to be very effective in proofs
like these. In this case, each can be used to reduce the proof to:
namespace Hidden
open Nat
theorem zero_add (n : Nat) : 0 + n = n :=
Nat.recOn (motive := fun x => 0 + x = x) n
rfl
(fun n ih => by simp [add_succ, ih])
end Hidden
As another example, let us prove the associativity of addition,
∀ m n k, m + n + k = m + (n + k)
.
(The notation +
, as we have defined it, associates to the left, so m + n + k
is really (m + n) + k
.)
The hardest part is figuring out which variable to do the induction on. Since addition is defined by recursion on the second argument,
k
is a good guess, and once we make that choice the proof almost writes itself:
namespace Hidden
open Nat
theorem add_assoc (m n k : Nat) : m + n + k = m + (n + k) :=
Nat.recOn (motive := fun k => m + n + k = m + (n + k)) k
(show m + n + 0 = m + (n + 0) from rfl)
(fun k (ih : m + n + k = m + (n + k)) =>
show m + n + succ k = m + (n + succ k) from
calc m + n + succ k
_ = succ (m + n + k) := rfl
_ = succ (m + (n + k)) := by rw [ih]
_ = m + succ (n + k) := rfl
_ = m + (n + succ k) := rfl)
end Hidden
Once again, you can reduce the proof to:
open Nat
theorem add_assoc (m n k : Nat) : m + n + k = m + (n + k) :=
Nat.recOn (motive := fun k => m + n + k = m + (n + k)) k
rfl
(fun k ih => by simp [Nat.add_succ, ih])
Suppose we try to prove the commutativity of addition. Choosing induction on the second argument, we might begin as follows:
open Nat
theorem add_comm (m n : Nat) : m + n = n + m :=
Nat.recOn (motive := fun x => m + x = x + m) n
(show m + 0 = 0 + m by rw [Nat.zero_add, Nat.add_zero])
(fun (n : Nat) (ih : m + n = n + m) =>
show m + succ n = succ n + m from
calc m + succ n
_ = succ (m + n) := rfl
_ = succ (n + m) := by rw [ih]
_ = succ n + m := sorry)
At this point, we see that we need another supporting fact, namely, that succ (n + m) = succ n + m
.
You can prove this by induction on m
:
open Nat
theorem succ_add (n m : Nat) : succ n + m = succ (n + m) :=
Nat.recOn (motive := fun x => succ n + x = succ (n + x)) m
(show succ n + 0 = succ (n + 0) from rfl)
(fun (m : Nat) (ih : succ n + m = succ (n + m)) =>
show succ n + succ m = succ (n + succ m) from
calc succ n + succ m
_ = succ (succ n + m) := rfl
_ = succ (succ (n + m)) := by rw [ih]
_ = succ (n + succ m) := rfl)
You can then replace the sorry
in the previous proof with succ_add
. Yet again, the proofs can be compressed:
namespace Hidden
open Nat
theorem succ_add (n m : Nat) : succ n + m = succ (n + m) :=
Nat.recOn (motive := fun x => succ n + x = succ (n + x)) m
rfl
(fun m ih => by simp only [add_succ, ih])
theorem add_comm (m n : Nat) : m + n = n + m :=
Nat.recOn (motive := fun x => m + x = x + m) n
(by simp)
(fun m ih => by simp [add_succ, succ_add, ih])
end Hidden
Other Recursive Data Types
Let us consider some more examples of inductively defined types. For
any type, α
, the type List α
of lists of elements of α
is
defined in the library.
namespace Hidden
inductive List (α : Type u) where
 nil : List α
 cons : α → List α → List α
namespace List
def append (as bs : List α) : List α :=
match as with
 nil => bs
 cons a as => cons a (append as bs)
theorem nil_append (as : List α) : append nil as = as :=
rfl
theorem cons_append (a : α) (as bs : List α)
: append (cons a as) bs = cons a (append as bs) :=
rfl
end List
end Hidden
A list of elements of type α
is either the empty list, nil
, or
an element h : α
followed by a list t : List α
.
The first element, h
, is commonly known as the "head" of the list,
and the remainder, t
, is known as the "tail."
As an exercise, prove the following:
namespace Hidden
inductive List (α : Type u) where
 nil : List α
 cons : α → List α → List α
namespace List
def append (as bs : List α) : List α :=
match as with
 nil => bs
 cons a as => cons a (append as bs)
theorem nil_append (as : List α) : append nil as = as :=
rfl
theorem cons_append (a : α) (as bs : List α)
: append (cons a as) bs = cons a (append as bs) :=
rfl
theorem append_nil (as : List α) : append as nil = as :=
sorry
theorem append_assoc (as bs cs : List α)
: append (append as bs) cs = append as (append bs cs) :=
sorry
end List
end Hidden
Try also defining the function length : {α : Type u} → List α → Nat
that returns the length of a list,
and prove that it behaves as expected (for example, length (append as bs) = length as + length bs
).
For another example, we can define the type of binary trees:
inductive BinaryTree where
 leaf : BinaryTree
 node : BinaryTree → BinaryTree → BinaryTree
In fact, we can even define the type of countably branching trees:
inductive CBTree where
 leaf : CBTree
 sup : (Nat → CBTree) → CBTree
namespace CBTree
def succ (t : CBTree) : CBTree :=
sup (fun _ => t)
def toCBTree : Nat → CBTree
 0 => leaf
 n+1 => succ (toCBTree n)
def omega : CBTree :=
sup toCBTree
end CBTree
Tactics for Inductive Types
Given the fundamental importance of inductive types in Lean, it should not be surprising that there are a number of tactics designed to work with them effectively. We describe some of them here.
The cases
tactic works on elements of an inductively defined type,
and does what the name suggests: it decomposes the element according
to each of the possible constructors. In its most basic form, it is
applied to an element x
in the local context. It then reduces the
goal to cases in which x
is replaced by each of the constructions.
example (p : Nat → Prop) (hz : p 0) (hs : ∀ n, p (Nat.succ n)) : ∀ n, p n := by
intro n
cases n
. exact hz  goal is p 0
. apply hs  goal is a : Nat ⊢ p (succ a)
There are extra bells and whistles. For one thing, cases
allows
you to choose the names for each alternative using a
with
clause. In the next example, for example, we choose the name
m
for the argument to succ
, so that the second case refers to
succ m
. More importantly, the cases tactic will detect any items
in the local context that depend on the target variable. It reverts
these elements, does the split, and reintroduces them. In the example
below, notice that the hypothesis h : n ≠ 0
becomes h : 0 ≠ 0
in the first branch, and h : succ m ≠ 0
in the second.
open Nat
example (n : Nat) (h : n ≠ 0) : succ (pred n) = n := by
cases n with
 zero =>
 goal: h : 0 ≠ 0 ⊢ succ (pred 0) = 0
apply absurd rfl h
 succ m =>
 second goal: h : succ m ≠ 0 ⊢ succ (pred (succ m)) = succ m
rfl
Notice that cases
can be used to produce data as well as prove propositions.
def f (n : Nat) : Nat := by
cases n; exact 3; exact 7
example : f 0 = 3 := rfl
example : f 5 = 7 := rfl
Once again, cases will revert, split, and then reintroduce dependencies in the context.
def Tuple (α : Type) (n : Nat) :=
{ as : List α // as.length = n }
def f {n : Nat} (t : Tuple α n) : Nat := by
cases n; exact 3; exact 7
def myTuple : Tuple Nat 3 :=
⟨[0, 1, 2], rfl⟩
example : f myTuple = 7 :=
rfl
Here is an example of multiple constructors with arguments.
inductive Foo where
 bar1 : Nat → Nat → Foo
 bar2 : Nat → Nat → Nat → Foo
def silly (x : Foo) : Nat := by
cases x with
 bar1 a b => exact b
 bar2 c d e => exact e
The alternatives for each constructor don't need to be solved in the order the constructors were declared.
inductive Foo where
 bar1 : Nat → Nat → Foo
 bar2 : Nat → Nat → Nat → Foo
def silly (x : Foo) : Nat := by
cases x with
 bar2 c d e => exact e
 bar1 a b => exact b
The syntax of the with
is convenient for writing structured proofs.
Lean also provides a complementary case
tactic, which allows you to focus on goal
assign variable names.
inductive Foo where
 bar1 : Nat → Nat → Foo
 bar2 : Nat → Nat → Nat → Foo
def silly (x : Foo) : Nat := by
cases x
case bar1 a b => exact b
case bar2 c d e => exact e
The case
tactic is clever, in that it will match the constructor to the appropriate goal. For example, we can fill the goals above in the opposite order:
inductive Foo where
 bar1 : Nat → Nat → Foo
 bar2 : Nat → Nat → Nat → Foo
def silly (x : Foo) : Nat := by
cases x
case bar2 c d e => exact e
case bar1 a b => exact b
You can also use cases
with an arbitrary expression. Assuming that
expression occurs in the goal, the cases tactic will generalize over
the expression, introduce the resulting universally quantified
variable, and case on that.
open Nat
example (p : Nat → Prop) (hz : p 0) (hs : ∀ n, p (succ n)) (m k : Nat)
: p (m + 3 * k) := by
cases m + 3 * k
exact hz  goal is p 0
apply hs  goal is a : Nat ⊢ p (succ a)
Think of this as saying "split on cases as to whether m + 3 * k
is
zero or the successor of some number." The result is functionally
equivalent to the following:
open Nat
example (p : Nat → Prop) (hz : p 0) (hs : ∀ n, p (succ n)) (m k : Nat)
: p (m + 3 * k) := by
generalize m + 3 * k = n
cases n
exact hz  goal is p 0
apply hs  goal is a : Nat ⊢ p (succ a)
Notice that the expression m + 3 * k
is erased by generalize
; all
that matters is whether it is of the form 0
or succ a
. This
form of cases
will not revert any hypotheses that also mention
the expression in the equation (in this case, m + 3 * k
). If such a
term appears in a hypothesis and you want to generalize over that as
well, you need to revert
it explicitly.
If the expression you case on does not appear in the goal, the
cases
tactic uses have
to put the type of the expression into
the context. Here is an example:
example (p : Prop) (m n : Nat)
(h₁ : m < n → p) (h₂ : m ≥ n → p) : p := by
cases Nat.lt_or_ge m n
case inl hlt => exact h₁ hlt
case inr hge => exact h₂ hge
The theorem Nat.lt_or_ge m n
says m < n ∨ m ≥ n
, and it is
natural to think of the proof above as splitting on these two
cases. In the first branch, we have the hypothesis hlt : m < n
, and
in the second we have the hypothesis hge : m ≥ n
. The proof above
is functionally equivalent to the following:
example (p : Prop) (m n : Nat)
(h₁ : m < n → p) (h₂ : m ≥ n → p) : p := by
have h : m < n ∨ m ≥ n := Nat.lt_or_ge m n
cases h
case inl hlt => exact h₁ hlt
case inr hge => exact h₂ hge
After the first two lines, we have h : m < n ∨ m ≥ n
as a
hypothesis, and we simply do cases on that.
Here is another example, where we use the decidability of equality on
the natural numbers to split on the cases m = n
and m ≠ n
.
#check Nat.sub_self
example (m n : Nat) : m  n = 0 ∨ m ≠ n := by
cases Decidable.em (m = n) with
 inl heq => rw [heq]; apply Or.inl; exact Nat.sub_self n
 inr hne => apply Or.inr; exact hne
Remember that if you open Classical
, you can use the law of the
excluded middle for any proposition at all. But using type class
inference (see Chapter Type Classes), Lean can actually
find the relevant decision procedure, which means that you can use the
case split in a computable function.
Just as the cases
tactic can be used to carry out proof by cases,
the induction
tactic can be used to carry out proofs by
induction. The syntax is similar to that of cases
, except that the
argument can only be a term in the local context. Here is an example:
namespace Hidden
theorem zero_add (n : Nat) : 0 + n = n := by
induction n with
 zero => rfl
 succ n ih => rw [Nat.add_succ, ih]
end Hidden
As with cases
, we can use the case
tactic instead of with
.
namespace Hidden
theorem zero_add (n : Nat) : 0 + n = n := by
induction n
case zero => rfl
case succ n ih => rw [Nat.add_succ, ih]
end Hidden
Here are some additional examples:
namespace Hidden
theorem add_zero (n : Nat) : n + 0 = n := Nat.add_zero n
open Nat
theorem zero_add (n : Nat) : 0 + n = n := by
induction n <;> simp [*, add_zero, add_succ]
theorem succ_add (m n : Nat) : succ m + n = succ (m + n) := by
induction n <;> simp [*, add_zero, add_succ]
theorem add_comm (m n : Nat) : m + n = n + m := by
induction n <;> simp [*, add_zero, add_succ, succ_add, zero_add]
theorem add_assoc (m n k : Nat) : m + n + k = m + (n + k) := by
induction k <;> simp [*, add_zero, add_succ]
end Hidden
The induction
tactic also supports userdefined induction principles with
multiple targets (aka major premises).
/
theorem Nat.mod.inductionOn
{motive : Nat → Nat → Sort u}
(x y : Nat)
(ind : ∀ x y, 0 < y ∧ y ≤ x → motive (x  y) y → motive x y)
(base : ∀ x y, ¬(0 < y ∧ y ≤ x) → motive x y)
: motive x y :=
/
example (x : Nat) {y : Nat} (h : y > 0) : x % y < y := by
induction x, y using Nat.mod.inductionOn with
 ind x y h₁ ih =>
rw [Nat.mod_eq_sub_mod h₁.2]
exact ih h
 base x y h₁ =>
have : ¬ 0 < y ∨ ¬ y ≤ x := Iff.mp (Decidable.not_and_iff_or_not ..) h₁
match this with
 Or.inl h₁ => exact absurd h h₁
 Or.inr h₁ =>
have hgt : y > x := Nat.gt_of_not_le h₁
rw [← Nat.mod_eq_of_lt hgt] at hgt
assumption
You can use the match
notation in tactics too:
example : p ∨ q → q ∨ p := by
intro h
match h with
 Or.inl _ => apply Or.inr; assumption
 Or.inr h2 => apply Or.inl; exact h2
As a convenience, patternmatching has been integrated into tactics such as intro
and funext
.
example : s ∧ q ∧ r → p ∧ r → q ∧ p := by
intro ⟨_, ⟨hq, _⟩⟩ ⟨hp, _⟩
exact ⟨hq, hp⟩
example :
(fun (x : Nat × Nat) (y : Nat × Nat) => x.1 + y.2)
=
(fun (x : Nat × Nat) (z : Nat × Nat) => z.2 + x.1) := by
funext (a, b) (c, d)
show a + d = d + a
rw [Nat.add_comm]
We close this section with one last tactic that is designed to
facilitate working with inductive types, namely, the injection
tactic. By design, the elements of an inductive type are freely
generated, which is to say, the constructors are injective and have
disjoint ranges. The injection
tactic is designed to make use of
this fact:
open Nat
example (m n k : Nat) (h : succ (succ m) = succ (succ n))
: n + k = m + k := by
injection h with h'
injection h' with h''
rw [h'']
The first instance of the tactic adds h' : succ m = succ n
to the
context, and the second adds h'' : m = n
.
The injection
tactic also detects contradictions that arise when different constructors
are set equal to one another, and uses them to close the goal.
open Nat
example (m n : Nat) (h : succ m = 0) : n = n + 7 := by
injection h
example (m n : Nat) (h : succ m = 0) : n = n + 7 := by
contradiction
example (h : 7 = 4) : False := by
contradiction
As the second example shows, the contradiction
tactic also detects contradictions of this form.
Inductive Families
We are almost done describing the full range of inductive definitions accepted by Lean. So far, you have seen that Lean allows you to introduce inductive types with any number of recursive constructors. In fact, a single inductive definition can introduce an indexed family of inductive types, in a manner we now describe.
An inductive family is an indexed family of types defined by a simultaneous induction of the following form:
inductive foo : ... → Sort u where
 constructor₁ : ... → foo ...
 constructor₂ : ... → foo ...
...
 constructorₙ : ... → foo ...
In contrast to an ordinary inductive definition, which constructs an
element of some Sort u
, the more general version constructs a
function ... → Sort u
, where "...
" denotes a sequence of
argument types, also known as indices. Each constructor then
constructs an element of some member of the family. One example is the
definition of Vector α n
, the type of vectors of elements of α
of length n
:
namespace Hidden
inductive Vector (α : Type u) : Nat → Type u where
 nil : Vector α 0
 cons : α → {n : Nat} → Vector α n → Vector α (n+1)
end Hidden
Notice that the cons
constructor takes an element of
Vector α n
and returns an element of Vector α (n+1)
, thereby using an
element of one member of the family to build an element of another.
A more exotic example is given by the definition of the equality type in Lean:
namespace Hidden
inductive Eq {α : Sort u} (a : α) : α → Prop where
 refl : Eq a a
end Hidden
For each fixed α : Sort u
and a : α
, this definition
constructs a family of types Eq a x
, indexed by x : α
.
Notably, however, there is only one constructor, refl
, which
is an element of Eq a a
.
Intuitively, the only way to construct a proof of Eq a x
is to use reflexivity, in the case where x
is a
.
Note that Eq a a
is the only inhabited type in the family of types
Eq a x
. The elimination principle generated by Lean is as follows:
universe u v
#check (@Eq.rec : {α : Sort u} → {a : α} → {motive : (x : α) → a = x → Sort v}
→ motive a rfl → {b : α} → (h : a = b) → motive b h)
It is a remarkable fact that all the basic axioms for equality follow
from the constructor, refl
, and the eliminator, Eq.rec
. The
definition of equality is atypical, however; see the discussion in Section Axiomatic Details.
The recursor Eq.rec
is also used to define substitution:
namespace Hidden
theorem subst {α : Type u} {a b : α} {p : α → Prop} (h₁ : Eq a b) (h₂ : p a) : p b :=
Eq.rec (motive := fun x _ => p x) h₂ h₁
end Hidden
You can also define subst
using match
.
namespace Hidden
theorem subst {α : Type u} {a b : α} {p : α → Prop} (h₁ : Eq a b) (h₂ : p a) : p b :=
match h₁ with
 rfl => h₂
end Hidden
Actually, Lean compiles the match
expressions using a definition based on
Eq.rec
.
namespace Hidden
theorem subst {α : Type u} {a b : α} {p : α → Prop} (h₁ : Eq a b) (h₂ : p a) : p b :=
match h₁ with
 rfl => h₂
set_option pp.all true
#print subst
 ... subst.match_1 ...
#print subst.match_1
 ... Eq.casesOn ...
#print Eq.casesOn
 ... Eq.rec ...
end Hidden
Using the recursor or match
with h₁ : a = b
, we may assume a
and b
are the same,
in which case, p b
and p a
are the same.
It is not hard to prove that Eq
is symmetric and transitive.
In the following example, we prove symm
and leave as exercises the theorems trans
and congr
(congruence).
namespace Hidden
theorem symm {α : Type u} {a b : α} (h : Eq a b) : Eq b a :=
match h with
 rfl => rfl
theorem trans {α : Type u} {a b c : α} (h₁ : Eq a b) (h₂ : Eq b c) : Eq a c :=
sorry
theorem congr {α β : Type u} {a b : α} (f : α → β) (h : Eq a b) : Eq (f a) (f b) :=
sorry
end Hidden
In the type theory literature, there are further generalizations of inductive definitions, for example, the principles of inductionrecursion and inductioninduction. These are not supported by Lean.
Axiomatic Details
We have described inductive types and their syntax through examples. This section provides additional information for those interested in the axiomatic foundations.
We have seen that the constructor to an inductive type takes parameters  intuitively, the arguments that remain fixed throughout the inductive construction  and indices, the arguments parameterizing the family of types that is simultaneously under construction. Each constructor should have a type, where the argument types are built up from previously defined types, the parameter and index types, and the inductive family currently being defined. The requirement is that if the latter is present at all, it occurs only strictly positively. This means simply that any argument to the constructor in which it occurs is a dependent arrow type in which the inductive type under definition occurs only as the resulting type, where the indices are given in terms of constants and previous arguments.
Since an inductive type lives in Sort u
for some u
, it is
reasonable to ask which universe levels u
can be instantiated
to. Each constructor c
in the definition of a family C
of
inductive types is of the form
c : (a : α) → (b : β[a]) → C a p[a,b]
where a
is a sequence of data type parameters, b
is the
sequence of arguments to the constructors, and p[a, b]
are the
indices, which determine which element of the inductive family the
construction inhabits. (Note that this description is somewhat
misleading, in that the arguments to the constructor can appear in any
order as long as the dependencies make sense.) The constraints on the
universe level of C
fall into two cases, depending on whether or
not the inductive type is specified to land in Prop
(that is,
Sort 0
).
Let us first consider the case where the inductive type is not
specified to land in Prop
. Then the universe level u
is
constrained to satisfy the following:
For each constructor
c
as above, and eachβk[a]
in the sequenceβ[a]
, ifβk[a] : Sort v
, we haveu
≥v
.
In other words, the universe level u
is required to be at least as
large as the universe level of each type that represents an argument
to a constructor.
When the inductive type is specified to land in Prop
, there are no
constraints on the universe levels of the constructor arguments. But
these universe levels do have a bearing on the elimination
rule. Generally speaking, for an inductive type in Prop
, the
motive of the elimination rule is required to be in Prop
.
There is an exception to this last rule: we are allowed to eliminate
from an inductively defined Prop
to an arbitrary Sort
when
there is only one constructor and each constructor argument is either
in Prop
or an index. The intuition is that in this case the
elimination does not make use of any information that is not already
given by the mere fact that the type of argument is inhabited. This
special case is known as singleton elimination.
We have already seen singleton elimination at play in applications of
Eq.rec
, the eliminator for the inductively defined equality
type. We can use an element h : Eq a b
to cast an element
t' : p a
to p b
even when p a
and p b
are arbitrary types,
because the cast does not produce new data; it only reinterprets the
data we already have. Singleton elimination is also used with
heterogeneous equality and wellfounded recursion, which will be
discussed in a Chapter Induction and Recursion.
Mutual and Nested Inductive Types
We now consider two generalizations of inductive types that are often useful, which Lean supports by "compiling" them down to the more primitive kinds of inductive types described above. In other words, Lean parses the more general definitions, defines auxiliary inductive types based on them, and then uses the auxiliary types to define the ones we really want. Lean's equation compiler, described in the next chapter, is needed to make use of these types effectively. Nonetheless, it makes sense to describe the declarations here, because they are straightforward variations on ordinary inductive definitions.
First, Lean supports mutually defined inductive types. The idea is that we can define two (or more) inductive types at the same time, where each one refers to the other(s).
mutual
inductive Even : Nat → Prop where
 even_zero : Even 0
 even_succ : (n : Nat) → Odd n → Even (n + 1)
inductive Odd : Nat → Prop where
 odd_succ : (n : Nat) → Even n → Odd (n + 1)
end
In this example, two types are defined simultaneously: a natural
number n
is Even
if it is 0
or one more than an Odd
number, and Odd
if it is one more than an Even
number.
In the exercises below, you are asked to spell out the details.
A mutual inductive definition can also be used to define the notation
of a finite tree with nodes labelled by elements of α
:
mutual
inductive Tree (α : Type u) where
 node : α → TreeList α → Tree α
inductive TreeList (α : Type u) where
 nil : TreeList α
 cons : Tree α → TreeList α → TreeList α
end
With this definition, one can construct an element of Tree α
by
giving an element of α
together with a list of subtrees, possibly
empty. The list of subtrees is represented by the type TreeList α
,
which is defined to be either the empty list, nil
, or the
cons
of a tree and an element of TreeList α
.
This definition is inconvenient to work with, however. It would be
much nicer if the list of subtrees were given by the type
List (Tree α)
, especially since Lean's library contains a number of functions
and theorems for working with lists. One can show that the type
TreeList α
is isomorphic to List (Tree α)
, but translating
results back and forth along this isomorphism is tedious.
In fact, Lean allows us to define the inductive type we really want:
inductive Tree (α : Type u) where
 mk : α → List (Tree α) → Tree α
This is known as a nested inductive type. It falls outside the
strict specification of an inductive type given in the last section
because Tree
does not occur strictly positively among the
arguments to mk
, but, rather, nested inside the List
type
constructor. Lean then automatically builds the
isomorphism between TreeList α
and List (Tree α)
in its kernel,
and defines the constructors for Tree
in terms of the isomorphism.
Exercises

Try defining other operations on the natural numbers, such as multiplication, the predecessor function (with
pred 0 = 0
), truncated subtraction (withn  m = 0
whenm
is greater than or equal ton
), and exponentiation. Then try proving some of their basic properties, building on the theorems we have already proved.Since many of these are already defined in Lean's core library, you should work within a namespace named
Hidden
, or something like that, in order to avoid name clashes. 
Define some operations on lists, like a
length
function or thereverse
function. Prove some properties, such as the following:a.
length (s ++ t) = length s + length t
b.
length (reverse t) = length t
c.
reverse (reverse t) = t

Define an inductive data type consisting of terms built up from the following constructors:
const n
, a constant denoting the natural numbern
var n
, a variable, numberedn
plus s t
, denoting the sum ofs
andt
times s t
, denoting the product ofs
andt
Recursively define a function that evaluates any such term with respect to an assignment of values to the variables.

Similarly, define the type of propositional formulas, as well as functions on the type of such formulas: an evaluation function, functions that measure the complexity of a formula, and a function that substitutes another formula for a given variable.
Induction and Recursion
In the previous chapter, we saw that inductive definitions provide a powerful means of introducing new types in Lean. Moreover, the constructors and the recursors provide the only means of defining functions on these types. By the propositionsastypes correspondence, this means that induction is the fundamental method of proof.
Lean provides natural ways of defining recursive functions, performing pattern matching, and writing inductive proofs. It allows you to define a function by specifying equations that it should satisfy, and it allows you to prove a theorem by specifying how to handle various cases that can arise. Behind the scenes, these descriptions are "compiled" down to primitive recursors, using a procedure that we refer to as the "equation compiler." The equation compiler is not part of the trusted code base; its output consists of terms that are checked independently by the kernel.
Pattern Matching
The interpretation of schematic patterns is the first step of the
compilation process. We have seen that the casesOn
recursor can
be used to define functions and prove theorems by cases, according to
the constructors involved in an inductively defined type. But
complicated definitions may use several nested casesOn
applications, and may be hard to read and understand. Pattern matching
provides an approach that is more convenient, and familiar to users of
functional programming languages.
Consider the inductively defined type of natural numbers. Every
natural number is either zero
or succ x
, and so you can define
a function from the natural numbers to an arbitrary type by specifying
a value in each of those cases:
open Nat
def sub1 : Nat → Nat
 zero => zero
 succ x => x
def isZero : Nat → Bool
 zero => true
 succ x => false
The equations used to define these functions hold definitionally:
open Nat
def sub1 : Nat → Nat
 zero => zero
 succ x => x
def isZero : Nat → Bool
 zero => true
 succ x => false
example : sub1 0 = 0 := rfl
example (x : Nat) : sub1 (succ x) = x := rfl
example : isZero 0 = true := rfl
example (x : Nat) : isZero (succ x) = false := rfl
example : sub1 7 = 6 := rfl
example (x : Nat) : isZero (x + 3) = false := rfl
Instead of zero
and succ
, we can use more familiar notation:
def sub1 : Nat → Nat
 0 => 0
 x+1 => x
def isZero : Nat → Bool
 0 => true
 x+1 => false
Because addition and the zero notation have been assigned the
[match_pattern]
attribute, they can be used in pattern matching. Lean
simply normalizes these expressions until the constructors zero
and succ
are exposed.
Pattern matching works with any inductive type, such as products and option types:
def swap : α × β → β × α
 (a, b) => (b, a)
def foo : Nat × Nat → Nat
 (m, n) => m + n
def bar : Option Nat → Nat
 some n => n + 1
 none => 0
Here we use it not only to define a function, but also to carry out a proof by cases:
namespace Hidden
def not : Bool → Bool
 true => false
 false => true
theorem not_not : ∀ (b : Bool), not (not b) = b
 true => rfl  proof that not (not true) = true
 false => rfl  proof that not (not false) = false
end Hidden
Pattern matching can also be used to destruct inductively defined propositions:
example (p q : Prop) : p ∧ q → q ∧ p
 And.intro h₁ h₂ => And.intro h₂ h₁
example (p q : Prop) : p ∨ q → q ∨ p
 Or.inl hp => Or.inr hp
 Or.inr hq => Or.inl hq
This provides a compact way of unpacking hypotheses that make use of logical connectives.
In all these examples, pattern matching was used to carry out a single case distinction. More interestingly, patterns can involve nested constructors, as in the following examples.
def sub2 : Nat → Nat
 0 => 0
 1 => 0
 x+2 => x
The equation compiler first splits on cases as to whether the input is
zero
or of the form succ x
. It then does a case split on
whether x
is of the form zero
or succ x
. It determines
the necessary case splits from the patterns that are presented to it,
and raises an error if the patterns fail to exhaust the cases. Once
again, we can use arithmetic notation, as in the version below. In
either case, the defining equations hold definitionally.
def sub2 : Nat → Nat
 0 => 0
 1 => 0
 x+2 => x
example : sub2 0 = 0 := rfl
example : sub2 1 = 0 := rfl
example : sub2 (x+2) = x := rfl
example : sub2 5 = 3 := rfl
You can write #print sub2
to see how the function was compiled to
recursors. (Lean will tell you that sub2
has been defined in terms
of an internal auxiliary function, sub2.match_1
, but you can print
that out too.) Lean uses these auxiliary functions to compile match
expressions.
Actually, the definition above is expanded to
def sub2 : Nat → Nat :=
fun x =>
match x with
 0 => 0
 1 => 0
 x+2 => x
Here are some more examples of nested pattern matching:
example (p q : α → Prop)
: (∃ x, p x ∨ q x) → (∃ x, p x) ∨ (∃ x, q x)
 Exists.intro x (Or.inl px) => Or.inl (Exists.intro x px)
 Exists.intro x (Or.inr qx) => Or.inr (Exists.intro x qx)
def foo : Nat × Nat → Nat
 (0, n) => 0
 (m+1, 0) => 1
 (m+1, n+1) => 2
The equation compiler can process multiple arguments sequentially. For example, it would be more natural to define the previous example as a function of two arguments:
def foo : Nat → Nat → Nat
 0, n => 0
 m+1, 0 => 1
 m+1, n+1 => 2
Here is another example:
def bar : List Nat → List Nat → Nat
 [], [] => 0
 a :: as, [] => a
 [], b :: bs => b
 a :: as, b :: bs => a + b
Note that the patterns are separated by commas.
In each of the following examples, splitting occurs on only the first argument, even though the others are included among the list of patterns.
namespace Hidden
def and : Bool → Bool → Bool
 true, a => a
 false, _ => false
def or : Bool → Bool → Bool
 true, _ => true
 false, a => a
def cond : Bool → α → α → α
 true, x, y => x
 false, x, y => y
end Hidden
Notice also that, when the value of an argument is not needed in the definition, you can use an underscore instead. This underscore is known as a wildcard pattern, or an anonymous variable. In contrast to usage outside the equation compiler, here the underscore does not indicate an implicit argument. The use of underscores for wildcards is common in functional programming languages, and so Lean adopts that notation. Section Wildcards and Overlapping Patterns expands on the notion of a wildcard, and Section Inaccessible Patterns explains how you can use implicit arguments in patterns as well.
As described in Chapter Inductive Types,
inductive data types can depend on parameters. The following example defines
the tail
function using pattern matching. The argument α : Type u
is a parameter and occurs before the colon to indicate it does not participate in the pattern matching.
Lean also allows parameters to occur after :
, but it cannot pattern match on them.
def tail1 {α : Type u} : List α → List α
 [] => []
 a :: as => as
def tail2 : {α : Type u} → List α → List α
 α, [] => []
 α, a :: as => as
Despite the different placement of the parameter α
in these two
examples, in both cases it is treated in the same way, in that it does
not participate in a case split.
Lean can also handle more complex forms of pattern matching, in which arguments to dependent types pose additional constraints on the various cases. Such examples of dependent pattern matching are considered in the Section Dependent Pattern Matching.
Wildcards and Overlapping Patterns
Consider one of the examples from the last section:
def foo : Nat → Nat → Nat
 0, n => 0
 m+1, 0 => 1
 m+1, n+1 => 2
An alternative presentation is:
def foo : Nat → Nat → Nat
 0, n => 0
 m, 0 => 1
 m, n => 2
In the second presentation, the patterns overlap; for example, the
pair of arguments 0 0
matches all three cases. But Lean handles
the ambiguity by using the first applicable equation, so in this example
the net result is the same. In particular, the following equations hold
definitionally:
def foo : Nat → Nat → Nat
 0, n => 0
 m, 0 => 1
 m, n => 2
example : foo 0 0 = 0 := rfl
example : foo 0 (n+1) = 0 := rfl
example : foo (m+1) 0 = 1 := rfl
example : foo (m+1) (n+1) = 2 := rfl
Since the values of m
and n
are not needed, we can just as well use wildcard patterns instead.
def foo : Nat → Nat → Nat
 0, _ => 0
 _, 0 => 1
 _, _ => 2
You can check that this definition of foo
satisfies the same
definitional identities as before.
Some functional programming languages support incomplete
patterns. In these languages, the interpreter produces an exception
or returns an arbitrary value for incomplete cases. We can simulate
the arbitrary value approach using the Inhabited
type
class. Roughly, an element of Inhabited α
is a witness to the fact
that there is an element of α
; in the Chapter Type Classes
we will see that Lean can be instructed that suitable
base types are inhabited, and can automatically infer that other
constructed types are inhabited. On this basis, the
standard library provides a default element, default
, of
any inhabited type.
We can also use the type Option α
to simulate incomplete patterns.
The idea is to return some a
for the provided patterns, and use
none
for the incomplete cases. The following example demonstrates
both approaches.
def f1 : Nat → Nat → Nat
 0, _ => 1
 _, 0 => 2
 _, _ => default  the "incomplete" case
example : f1 0 0 = 1 := rfl
example : f1 0 (a+1) = 1 := rfl
example : f1 (a+1) 0 = 2 := rfl
example : f1 (a+1) (b+1) = default := rfl
def f2 : Nat → Nat → Option Nat
 0, _ => some 1
 _, 0 => some 2
 _, _ => none  the "incomplete" case
example : f2 0 0 = some 1 := rfl
example : f2 0 (a+1) = some 1 := rfl
example : f2 (a+1) 0 = some 2 := rfl
example : f2 (a+1) (b+1) = none := rfl
The equation compiler is clever. If you leave out any of the cases in the following definition, the error message will let you know what has not been covered.
def bar : Nat → List Nat → Bool → Nat
 0, _, false => 0
 0, b :: _, _ => b
 0, [], true => 7
 a+1, [], false => a
 a+1, [], true => a + 1
 a+1, b :: _, _ => a + b
It will also use an "if ... then ... else" instead of a casesOn
in appropriate situations.
def foo : Char → Nat
 'A' => 1
 'B' => 2
 _ => 3
#print foo.match_1
Structural Recursion and Induction
What makes the equation compiler powerful is that it also supports recursive definitions. In the next three sections, we will describe, respectively:
 structurally recursive definitions
 wellfounded recursive definitions
 mutually recursive definitions
Generally speaking, the equation compiler processes input of the following form:
def foo (a : α) : (b : β) → γ
 [patterns₁] => t₁
...
 [patternsₙ] => tₙ
Here (a : α)
is a sequence of parameters, (b : β)
is the
sequence of arguments on which pattern matching takes place, and γ
is any type, which can depend on a
and b
. Each line should
contain the same number of patterns, one for each element of β
. As we
have seen, a pattern is either a variable, a constructor applied to
other patterns, or an expression that normalizes to something of that
form (where the nonconstructors are marked with the [match_pattern]
attribute). The appearances of constructors prompt case splits, with
the arguments to the constructors represented by the given
variables. In Section Dependent Pattern Matching,
we will see that it is sometimes necessary to include explicit terms in patterns that
are needed to make an expression type check, though they do not play a
role in pattern matching. These are called "inaccessible patterns" for
that reason. But we will not need to use such inaccessible patterns
before Section Dependent Pattern Matching.
As we saw in the last section, the terms t₁, ..., tₙ
can make use
of any of the parameters a
, as well as any of the variables that
are introduced in the corresponding patterns. What makes recursion and
induction possible is that they can also involve recursive calls to
foo
. In this section, we will deal with structural recursion, in
which the arguments to foo
occurring on the righthand side of the
=>
are subterms of the patterns on the lefthand side. The idea is
that they are structurally smaller, and hence appear in the inductive
type at an earlier stage. Here are some examples of structural
recursion from the last chapter, now defined using the equation
compiler:
open Nat
def add : Nat → Nat → Nat
 m, zero => m
 m, succ n => succ (add m n)
theorem add_zero (m : Nat) : add m zero = m := rfl
theorem add_succ (m n : Nat) : add m (succ n) = succ (add m n) := rfl
theorem zero_add : ∀ n, add zero n = n
 zero => rfl
 succ n => congrArg succ (zero_add n)
def mul : Nat → Nat → Nat
 n, zero => zero
 n, succ m => add (mul n m) n
The proof of zero_add
makes it clear that proof by induction is
really a form of recursion in Lean.
The example above shows that the defining equations for add
hold
definitionally, and the same is true of mul
. The equation compiler
tries to ensure that this holds whenever possible, as is the case with
straightforward structural induction. In other situations, however,
reductions hold only propositionally, which is to say, they are
equational theorems that must be applied explicitly. The equation
compiler generates such theorems internally. They are not meant to be
used directly by the user; rather, the simp
tactic
is configured to use them when necessary. Thus both of the following
proofs of zero_add
work:
open Nat
def add : Nat → Nat → Nat
 m, zero => m
 m, succ n => succ (add m n)
theorem zero_add : ∀ n, add zero n = n
 zero => by simp [add]
 succ n => by simp [add, zero_add]
As with definition by pattern matching, parameters to a structural recursion or induction may appear before the colon. Such parameters are simply added to the local context before the definition is processed. For example, the definition of addition may also be written as follows:
open Nat
def add (m : Nat) : Nat → Nat
 zero => m
 succ n => succ (add m n)
You can also write the example above using match
.
open Nat
def add (m n : Nat) : Nat :=
match n with
 zero => m
 succ n => succ (add m n)
A more interesting example of structural recursion is given by the Fibonacci function fib
.
def fib : Nat → Nat
 0 => 1
 1 => 1
 n+2 => fib (n+1) + fib n
example : fib 0 = 1 := rfl
example : fib 1 = 1 := rfl
example : fib (n + 2) = fib (n + 1) + fib n := rfl
example : fib 7 = 21 := rfl
Here, the value of the fib
function at n + 2
(which is
definitionally equal to succ (succ n)
) is defined in terms of the
values at n + 1
(which is definitionally equivalent to succ n
)
and the value at n
. This is a notoriously inefficient way of
computing the Fibonacci function, however, with an execution time that
is exponential in n
. Here is a better way:
def fibFast (n : Nat) : Nat :=
(loop n).2
where
loop : Nat → Nat × Nat
 0 => (0, 1)
 n+1 => let p := loop n; (p.2, p.1 + p.2)
#eval fibFast 100
Here is the same definition using a let rec
instead of a where
.
def fibFast (n : Nat) : Nat :=
let rec loop : Nat → Nat × Nat
 0 => (0, 1)
 n+1 => let p := loop n; (p.2, p.1 + p.2)
(loop n).2
In both cases, Lean generates the auxiliary function fibFast.loop
.
To handle structural recursion, the equation compiler uses
courseofvalues recursion, using constants below
and brecOn
that are automatically generated with each inductively defined
type. You can get a sense of how it works by looking at the types of
Nat.below
and Nat.brecOn
:
variable (C : Nat → Type u)
#check (@Nat.below C : Nat → Type u)
#reduce @Nat.below C (3 : Nat)
#check (@Nat.brecOn C : (n : Nat) → ((n : Nat) → @Nat.below C n → C n) → C n)
The type @Nat.below C (3 : nat)
is a data structure that stores elements of C 0
, C 1
, and C 2
.
The courseofvalues recursion is implemented by Nat.brecOn
. It enables us to define the value of a dependent
function of type (n : Nat) → C n
at a particular input n
in terms of all the previous values of the function,
presented as an element of @Nat.below C n
.
The use of courseofvalues recursion is one of the techniques the equation compiler uses to justify to
the Lean kernel that a function terminates. It does not affect the code generator which compiles recursive
functions as other functional programming language compilers. Recall that #eval fib <n>
is exponential on <n>
.
On the other hand, #reduce fib <n>
is efficient because it uses the definition sent to the kernel that
is based on the brecOn
construction.
def fib : Nat → Nat
 0 => 1
 1 => 1
 n+2 => fib (n+1) + fib n
 #eval fib 50  slow
#reduce fib 50  fast
#print fib
Another good example of a recursive definition is the list append
function.
def append : List α → List α → List α
 [], bs => bs
 a::as, bs => a :: append as bs
example : append [1, 2, 3] [4, 5] = [1, 2, 3, 4, 5] := rfl
Here is another: it adds elements of the first list to elements of the second list, until one of the two lists runs out.
def listAdd [Add α] : List α → List α → List α
 [], _ => []
 _, [] => []
 a :: as, b :: bs => (a + b) :: listAdd as bs
#eval listAdd [1, 2, 3] [4, 5, 6, 6, 9, 10]
 [5, 7, 9]
You are encouraged to experiment with similar examples in the exercises below.
Local recursive declarations
You can define local recursive declarations using the let rec
keyword.
def replicate (n : Nat) (a : α) : List α :=
let rec loop : Nat → List α → List α
 0, as => as
 n+1, as => loop n (a::as)
loop n []
#check @replicate.loop
 {α : Type} → α → Nat → List α → List α
Lean creates an auxiliary declaration for each let rec
. In the example above,
it created the declaration replicate.loop
for the let rec loop
occurring at replicate
.
Note that, Lean "closes" the declaration by adding any local variable occurring in the
let rec
declaration as additional parameters. For example, the local variable a
occurs
at let rec loop
.
You can also use let rec
in tactic mode and for creating proofs by induction.
def replicate (n : Nat) (a : α) : List α :=
let rec loop : Nat → List α → List α
 0, as => as
 n+1, as => loop n (a::as)
loop n []
theorem length_replicate (n : Nat) (a : α) : (replicate n a).length = n := by
let rec aux (n : Nat) (as : List α)
: (replicate.loop a n as).length = n + as.length := by
match n with
 0 => simp [replicate.loop]
 n+1 => simp [replicate.loop, aux n, Nat.add_succ, Nat.succ_add]
exact aux n []
You can also introduce auxiliary recursive declarations using where
clause after your definition.
Lean converts them into a let rec
.
def replicate (n : Nat) (a : α) : List α :=
loop n []
where
loop : Nat → List α → List α
 0, as => as
 n+1, as => loop n (a::as)
theorem length_replicate (n : Nat) (a : α) : (replicate n a).length = n := by
exact aux n []
where
aux (n : Nat) (as : List α)
: (replicate.loop a n as).length = n + as.length := by
match n with
 0 => simp [replicate.loop]
 n+1 => simp [replicate.loop, aux n, Nat.add_succ, Nat.succ_add]
WellFounded Recursion and Induction
When structural recursion cannot be used, we can prove termination using wellfounded recursion. We need a wellfounded relation and a proof that each recursive application is decreasing with respect to this relation. Dependent type theory is powerful enough to encode and justify wellfounded recursion. Let us start with the logical background that is needed to understand how it works.
Lean's standard library defines two predicates, Acc r a
and
WellFounded r
, where r
is a binary relation on a type α
,
and a
is an element of type α
.
variable (α : Sort u)
variable (r : α → α → Prop)
#check (Acc r : α → Prop)
#check (WellFounded r : Prop)
The first, Acc
, is an inductively defined predicate. According to
its definition, Acc r x
is equivalent to
∀ y, r y x → Acc r y
. If you think of r y x
as denoting a kind of order relation
y ≺ x
, then Acc r x
says that x
is accessible from below,
in the sense that all its predecessors are accessible. In particular,
if x
has no predecessors, it is accessible. Given any type α
,
we should be able to assign a value to each accessible element of
α
, recursively, by assigning values to all its predecessors first.
The statement that r
is well founded, denoted WellFounded r
,
is exactly the statement that every element of the type is
accessible. By the above considerations, if r
is a wellfounded
relation on a type α
, we should have a principle of wellfounded
recursion on α
, with respect to the relation r
. And, indeed,
we do: the standard library defines WellFounded.fix
, which serves
exactly that purpose.
noncomputable def f {α : Sort u}
(r : α → α → Prop)
(h : WellFounded r)
(C : α → Sort v)
(F : (x : α) → ((y : α) → r y x → C y) → C x)
: (x : α) → C x := WellFounded.fix h F
There is a long cast of characters here, but the first block we have
already seen: the type, α
, the relation, r
, and the
assumption, h
, that r
is well founded. The variable C
represents the motive of the recursive definition: for each element
x : α
, we would like to construct an element of C x
. The
function F
provides the inductive recipe for doing that: it tells
us how to construct an element C x
, given elements of C y
for
each predecessor y
of x
.
Note that WellFounded.fix
works equally well as an induction
principle. It says that if ≺
is well founded and you want to prove
∀ x, C x
, it suffices to show that for an arbitrary x
, if we
have ∀ y ≺ x, C y
, then we have C x
.
In the example above we use the modifier noncomputable
because the code
generator currently does not support WellFounded.fix
. The function
WellFounded.fix
is another tool Lean uses to justify that a function
terminates.
Lean knows that the usual order <
on the natural numbers is well
founded. It also knows a number of ways of constructing new well
founded orders from others, for example, using lexicographic order.
Here is essentially the definition of division on the natural numbers that is found in the standard library.
open Nat
theorem div_lemma {x y : Nat} : 0 < y ∧ y ≤ x → x  y < x :=
fun h => sub_lt (Nat.lt_of_lt_of_le h.left h.right) h.left
def div.F (x : Nat) (f : (x₁ : Nat) → x₁ < x → Nat → Nat) (y : Nat) : Nat :=
if h : 0 < y ∧ y ≤ x then
f (x  y) (div_lemma h) y + 1
else
zero
noncomputable def div := WellFounded.fix (measure id).wf div.F
#reduce div 8 2  4
The definition is somewhat inscrutable. Here the recursion is on
x
, and div.F x f : Nat → Nat
returns the "divide by y
"
function for that fixed x
. You have to remember that the second
argument to div.F
, the recipe for the recursion, is a function
that is supposed to return the divide by y
function for all values
x₁
smaller than x
.
The elaborator is designed to make definitions like this more convenient. It accepts the following:
def div (x y : Nat) : Nat :=
if h : 0 < y ∧ y ≤ x then
have : x  y < x := Nat.sub_lt (Nat.lt_of_lt_of_le h.1 h.2) h.1
div (x  y) y + 1
else
0
When Lean encounters a recursive definition, it first
tries structural recursion, and only when that fails, does it fall
back on wellfounded recursion. Lean uses the tactic decreasing_tactic
to show that the recursive applications are smaller. The auxiliary
proposition x  y < x
in the example above should be viewed as a hint
for this tactic.
The defining equation for div
does not hold definitionally, but
we can unfold div
using the unfold
tactic. We use conv
to select which
div
application we want to unfold.
def div (x y : Nat) : Nat :=
if h : 0 < y ∧ y ≤ x then
have : x  y < x := Nat.sub_lt (Nat.lt_of_lt_of_le h.1 h.2) h.1
div (x  y) y + 1
else
0
example (x y : Nat) : div x y = if 0 < y ∧ y ≤ x then div (x  y) y + 1 else 0 := by
conv => lhs; unfold div  unfold occurrence in the lefthandside of the equation
example (x y : Nat) (h : 0 < y ∧ y ≤ x) : div x y = div (x  y) y + 1 := by
conv => lhs; unfold div
simp [h]
The following example is similar: it converts any natural number to a
binary expression, represented as a list of 0's and 1's. We have to
provide evidence that the recursive call is
decreasing, which we do here with a sorry
. The sorry
does not
prevent the interpreter from evaluating the function successfully.
def natToBin : Nat → List Nat
 0 => [0]
 1 => [1]
 n + 2 =>
have : (n + 2) / 2 < n + 2 := sorry
natToBin ((n + 2) / 2) ++ [n % 2]
#eval natToBin 1234567
As a final example, we observe that Ackermann's function can be
defined directly, because it is justified by the well foundedness of
the lexicographic order on the natural numbers. The termination_by
clause
instructs Lean to use a lexicographic order. This clause is actually mapping
the function arguments to elements of type Nat × Nat
. Then, Lean uses typeclass
resolution to synthesize an element of type WellFoundedRelation (Nat × Nat)
.
def ack : Nat → Nat → Nat
 0, y => y+1
 x+1, 0 => ack x 1
 x+1, y+1 => ack x (ack (x+1) y)
termination_by x y => (x, y)
Note that a lexicographic order is used in the example above because the instance
WellFoundedRelation (α × β)
uses a lexicographic order. Lean also defines the instance
instance (priority := low) [SizeOf α] : WellFoundedRelation α :=
sizeOfWFRel
In the following example, we prove termination by showing that as.size  i
is decreasing
in the recursive application.
def takeWhile (p : α → Bool) (as : Array α) : Array α :=
go 0 #[]
where
go (i : Nat) (r : Array α) : Array α :=
if h : i < as.size then
let a := as.get ⟨i, h⟩
if p a then
go (i+1) (r.push a)
else
r
else
r
termination_by as.size  i
Note that, auxiliary function go
is recursive in this example, but takeWhile
is not.
By default, Lean uses the tactic decreasing_tactic
to prove recursive applications are decreasing. The modifier decreasing_by
allows us to provide our own tactic. Here is an example.
theorem div_lemma {x y : Nat} : 0 < y ∧ y ≤ x → x  y < x :=
fun ⟨ypos, ylex⟩ => Nat.sub_lt (Nat.lt_of_lt_of_le ypos ylex) ypos
def div (x y : Nat) : Nat :=
if h : 0 < y ∧ y ≤ x then
div (x  y) y + 1
else
0
decreasing_by apply div_lemma; assumption
Note that decreasing_by
is not replacement for termination_by
, they complement each other. termination_by
is used to specify a wellfounded relation, and decreasing_by
for providing our own tactic for showing recursive applications are decreasing. In the following example, we use both of them.
def ack : Nat → Nat → Nat
 0, y => y+1
 x+1, 0 => ack x 1
 x+1, y+1 => ack x (ack (x+1) y)
termination_by x y => (x, y)
decreasing_by
all_goals simp_wf  unfolds wellfounded recursion auxiliary definitions
· apply Prod.Lex.left; simp_arith
· apply Prod.Lex.right; simp_arith
· apply Prod.Lex.left; simp_arith
We can use decreasing_by sorry
to instruct Lean to "trust" us that the function terminates.
def natToBin : Nat → List Nat
 0 => [0]
 1 => [1]
 n + 2 => natToBin ((n + 2) / 2) ++ [n % 2]
decreasing_by sorry
#eval natToBin 1234567
Recall that using sorry
is equivalent to using a new axiom, and should be avoided. In the following example, we used the sorry
to prove False
. The command #print axioms
shows that unsound
depends on the unsound axiom sorryAx
used to implement sorry
.
def unsound (x : Nat) : False :=
unsound (x + 1)
decreasing_by sorry
#check unsound 0
 `unsound 0` is a proof of `False`
#print axioms unsound
 'unsound' depends on axioms: [sorryAx]
Summary:

If there is no
termination_by
, a wellfounded relation is derived (if possible) by selecting an argument and then using typeclass resolution to synthesize a wellfounded relation for this argument's type. 
If
termination_by
is specified, it maps the arguments of the function to a typeα
and type class resolution is again used. Recall that, the default instance forβ × γ
is a lexicographic order based on the wellfounded relations forβ
andγ
. 
The default wellfounded relation instance for
Nat
is<
. 
By default, the tactic
decreasing_tactic
is used to show that recursive applications are smaller with respect to the selected wellfounded relation. Ifdecreasing_tactic
fails, the error message includes the remaining goal...  G
. Note that, thedecreasing_tactic
usesassumption
. So, you can include ahave
expression to prove goalG
. You can also provide your own tactic usingdecreasing_by
.
Mutual Recursion
Lean also supports mutual recursive definitions. The syntax is similar to that for mutual inductive types. Here is an example:
mutual
def even : Nat → Bool
 0 => true
 n+1 => odd n
def odd : Nat → Bool
 0 => false
 n+1 => even n
end
example : even (a + 1) = odd a := by
simp [even]
example : odd (a + 1) = even a := by
simp [odd]
theorem even_eq_not_odd : ∀ a, even a = not (odd a) := by
intro a; induction a
. simp [even, odd]
. simp [even, odd, *]
What makes this a mutual definition is that even
is defined recursively in terms of odd
, while odd
is defined recursively in terms of even
. Under the hood, this is compiled as a single recursive definition. The internally defined function takes, as argument, an element of a sum type, either an input to even
, or an input to odd
. It then returns an output appropriate to the input. To define that function, Lean uses a suitable wellfounded measure. The internals are meant to be hidden from users; the canonical way to make use of such definitions is to use simp
(or unfold
), as we did above.
Mutual recursive definitions also provide natural ways of working with mutual and nested inductive types. Recall the definition of Even
and Odd
as mutual inductive predicates as presented before.
mutual
inductive Even : Nat → Prop where
 even_zero : Even 0
 even_succ : ∀ n, Odd n → Even (n + 1)
inductive Odd : Nat → Prop where
 odd_succ : ∀ n, Even n → Odd (n + 1)
end
The constructors, even_zero
, even_succ
, and odd_succ
provide positive means for showing that a number is even or odd. We need to use the fact that the inductive type is generated by these constructors to know that zero is not odd, and that the latter two implications reverse. As usual, the constructors are kept in a namespace that is named after the type being defined, and the command open Even Odd
allows us to access them more conveniently.
mutual
inductive Even : Nat → Prop where
 even_zero : Even 0
 even_succ : ∀ n, Odd n → Even (n + 1)
inductive Odd : Nat → Prop where
 odd_succ : ∀ n, Even n → Odd (n + 1)
end
open Even Odd
theorem not_odd_zero : ¬ Odd 0 :=
fun h => nomatch h
theorem even_of_odd_succ : ∀ n, Odd (n + 1) → Even n
 _, odd_succ n h => h
theorem odd_of_even_succ : ∀ n, Even (n + 1) → Odd n
 _, even_succ n h => h
For another example, suppose we use a nested inductive type to define a set of terms inductively, so that a term is either a constant (with a name given by a string), or the result of applying a constant to a list of constants.
inductive Term where
 const : String → Term
 app : String → List Term → Term
We can then use a mutual recursive definition to count the number of constants occurring in a term, as well as the number occurring in a list of terms.
inductive Term where
 const : String → Term
 app : String → List Term → Term
namespace Term
mutual
def numConsts : Term → Nat
 const _ => 1
 app _ cs => numConstsLst cs
def numConstsLst : List Term → Nat
 [] => 0
 c :: cs => numConsts c + numConstsLst cs
end
def sample := app "f" [app "g" [const "x"], const "y"]
#eval numConsts sample
end Term
As a final example, we define a function replaceConst a b e
that replaces a constant a
with b
in a term e
, and then prove the number of constants is the same. Note that, our proof uses mutual recursion (aka induction).
inductive Term where
 const : String → Term
 app : String → List Term → Term
namespace Term
mutual
def numConsts : Term → Nat
 const _ => 1
 app _ cs => numConstsLst cs
def numConstsLst : List Term → Nat
 [] => 0
 c :: cs => numConsts c + numConstsLst cs
end
mutual
def replaceConst (a b : String) : Term → Term
 const c => if a == c then const b else const c
 app f cs => app f (replaceConstLst a b cs)
def replaceConstLst (a b : String) : List Term → List Term
 [] => []
 c :: cs => replaceConst a b c :: replaceConstLst a b cs
end
mutual
theorem numConsts_replaceConst (a b : String) (e : Term)
: numConsts (replaceConst a b e) = numConsts e := by
match e with
 const c => simp [replaceConst]; split <;> simp [numConsts]
 app f cs => simp [replaceConst, numConsts, numConsts_replaceConstLst a b cs]
theorem numConsts_replaceConstLst (a b : String) (es : List Term)
: numConstsLst (replaceConstLst a b es) = numConstsLst es := by
match es with
 [] => simp [replaceConstLst, numConstsLst]
 c :: cs =>
simp [replaceConstLst, numConstsLst, numConsts_replaceConst a b c,
numConsts_replaceConstLst a b cs]
end
Dependent Pattern Matching
All the examples of pattern matching we considered in
Section Pattern Matching can easily be written using casesOn
and recOn
. However, this is often not the case with indexed
inductive families such as Vector α n
, since case splits impose
constraints on the values of the indices. Without the equation
compiler, we would need a lot of boilerplate code to define very
simple functions such as map
, zip
, and unzip
using
recursors. To understand the difficulty, consider what it would take
to define a function tail
which takes a vector
v : Vector α (succ n)
and deletes the first element. A first thought might be to
use the casesOn
function:
inductive Vector (α : Type u) : Nat → Type u
 nil : Vector α 0
 cons : α → {n : Nat} → Vector α n → Vector α (n+1)
namespace Vector
#check @Vector.casesOn
/
{α : Type u}
→ {motive : (a : Nat) → Vector α a → Sort v} →
→ {a : Nat} → (t : Vector α a)
→ motive 0 nil
→ ((a : α) → {n : Nat} → (a_1 : Vector α n) → motive (n + 1) (cons a a_1))
→ motive a t
/
end Vector
But what value should we return in the nil
case? Something funny
is going on: if v
has type Vector α (succ n)
, it can't be
nil, but it is not clear how to tell that to casesOn
.
One solution is to define an auxiliary function:
inductive Vector (α : Type u) : Nat → Type u
 nil : Vector α 0
 cons : α → {n : Nat} → Vector α n → Vector α (n+1)
namespace Vector
def tailAux (v : Vector α m) : m = n + 1 → Vector α n :=
Vector.casesOn (motive := fun x _ => x = n + 1 → Vector α n) v
(fun h : 0 = n + 1 => Nat.noConfusion h)
(fun (a : α) (m : Nat) (as : Vector α m) =>
fun (h : m + 1 = n + 1) =>
Nat.noConfusion h (fun h1 : m = n => h1 ▸ as))
def tail (v : Vector α (n+1)) : Vector α n :=
tailAux v rfl
end Vector
In the nil
case, m
is instantiated to 0
, and
noConfusion
makes use of the fact that 0 = succ n
cannot
occur. Otherwise, v
is of the form a :: w
, and we can simply
return w
, after casting it from a vector of length m
to a
vector of length n
.
The difficulty in defining tail
is to maintain the relationships between the indices.
The hypothesis e : m = n + 1
in tailAux
is used to communicate the relationship
between n
and the index associated with the minor premise.
Moreover, the zero = n + 1
case is unreachable, and the canonical way to discard such
a case is to use noConfusion
.
The tail
function is, however, easy to define using recursive
equations, and the equation compiler generates all the boilerplate
code automatically for us. Here are a number of similar examples:
inductive Vector (α : Type u) : Nat → Type u
 nil : Vector α 0
 cons : α → {n : Nat} → Vector α n → Vector α (n+1)
namespace Vector
def head : {n : Nat} → Vector α (n+1) → α
 n, cons a as => a
def tail : {n : Nat} → Vector α (n+1) → Vector α n
 n, cons a as => as
theorem eta : ∀ {n : Nat} (v : Vector α (n+1)), cons (head v) (tail v) = v
 n, cons a as => rfl
def map (f : α → β → γ) : {n : Nat} → Vector α n → Vector β n → Vector γ n
 0, nil, nil => nil
 n+1, cons a as, cons b bs => cons (f a b) (map f as bs)
def zip : {n : Nat} → Vector α n → Vector β n → Vector (α × β) n
 0, nil, nil => nil
 n+1, cons a as, cons b bs => cons (a, b) (zip as bs)
end Vector
Note that we can omit recursive equations for "unreachable" cases such
as head nil
. The automatically generated definitions for indexed
families are far from straightforward. For example:
inductive Vector (α : Type u) : Nat → Type u
 nil : Vector α 0
 cons : α → {n : Nat} → Vector α n → Vector α (n+1)
namespace Vector
def map (f : α → β → γ) : {n : Nat} → Vector α n → Vector β n → Vector γ n
 0, nil, nil => nil
 n+1, cons a as, cons b bs => cons (f a b) (map f as bs)
#print map
#print map.match_1
end Vector
The map
function is even more tedious to define by hand than the
tail
function. We encourage you to try it, using recOn
,
casesOn
and noConfusion
.
Inaccessible Patterns
Sometimes an argument in a dependent matching pattern is not essential
to the definition, but nonetheless has to be included to specialize
the type of the expression appropriately. Lean allows users to mark
such subterms as inaccessible for pattern matching. These
annotations are essential, for example, when a term occurring in the
lefthand side is neither a variable nor a constructor application,
because these are not suitable targets for pattern matching. We can
view such inaccessible patterns as "don't care" components of the
patterns. You can declare a subterm inaccessible by writing
.(t)
. If the inaccessible pattern can be inferred, you can also write
_
.
The following example, we declare an inductive type that defines the
property of "being in the image of f
". You can view an element of
the type ImageOf f b
as evidence that b
is in the image of
f
, whereby the constructor imf
is used to build such
evidence. We can then define any function f
with an "inverse"
which takes anything in the image of f
to an element that is
mapped to it. The typing rules forces us to write f a
for the
first argument, but this term is neither a variable nor a constructor
application, and plays no role in the patternmatching definition. To
define the function inverse
below, we have to mark f a
inaccessible.
inductive ImageOf {α β : Type u} (f : α → β) : β → Type u where
 imf : (a : α) → ImageOf f (f a)
open ImageOf
def inverse {f : α → β} : (b : β) → ImageOf f b → α
 .(f a), imf a => a
def inverse' {f : α → β} : (b : β) → ImageOf f b → α
 _, imf a => a
In the example above, the inaccessible annotation makes it clear that
f
is not a pattern matching variable.
Inaccessible patterns can be used to clarify and control definitions that
make use of dependent pattern matching. Consider the following
definition of the function Vector.add
, which adds two vectors of
elements of a type, assuming that type has an associated addition
function:
inductive Vector (α : Type u) : Nat → Type u
 nil : Vector α 0
 cons : α → {n : Nat} → Vector α n → Vector α (n+1)
namespace Vector
def add [Add α] : {n : Nat} → Vector α n → Vector α n → Vector α n
 0, nil, nil => nil
 n+1, cons a as, cons b bs => cons (a + b) (add as bs)
end Vector
The argument {n : Nat}
appear after the colon, because it cannot
be held fixed throughout the definition. When implementing this
definition, the equation compiler starts with a case distinction as to
whether the first argument is 0
or of the form n+1
. This is
followed by nested case splits on the next two arguments, and in each
case the equation compiler rules out the cases are not compatible with
the first pattern.
But, in fact, a case split is not required on the first argument; the
casesOn
eliminator for Vector
automatically abstracts this
argument and replaces it by 0
and n + 1
when we do a case
split on the second argument. Using inaccessible patterns, we can prompt
the equation compiler to avoid the case split on n
inductive Vector (α : Type u) : Nat → Type u
 nil : Vector α 0
 cons : α → {n : Nat} → Vector α n → Vector α (n+1)
namespace Vector
def add [Add α] : {n : Nat} → Vector α n → Vector α n → Vector α n
 .(_), nil, nil => nil
 .(_), cons a as, cons b bs => cons (a + b) (add as bs)
end Vector
Marking the position as an inaccessible pattern tells the equation compiler first, that the form of the argument should be inferred from the constraints posed by the other arguments, and, second, that the first argument should not participate in pattern matching.
The inaccessible pattern .(_)
can be written as _
for convenience.
inductive Vector (α : Type u) : Nat → Type u
 nil : Vector α 0
 cons : α → {n : Nat} → Vector α n → Vector α (n+1)
namespace Vector
def add [Add α] : {n : Nat} → Vector α n → Vector α n → Vector α n
 _, nil, nil => nil
 _, cons a as, cons b bs => cons (a + b) (add as bs)
end Vector
As we mentioned above, the argument {n : Nat}
is part of the
pattern matching, because it cannot be held fixed throughout the
definition. In previous Lean versions, users often found it cumbersome
to have to include these extra discriminants. Thus, Lean 4
implements a new feature, discriminant refinement, which includes
these extra discriminants automatically for us.
inductive Vector (α : Type u) : Nat → Type u
 nil : Vector α 0
 cons : α → {n : Nat} → Vector α n → Vector α (n+1)
namespace Vector
def add [Add α] {n : Nat} : Vector α n → Vector α n → Vector α n
 nil, nil => nil
 cons a as, cons b bs => cons (a + b) (add as bs)
end Vector
When combined with the auto bound implicits feature, you can simplify the declare further and write:
inductive Vector (α : Type u) : Nat → Type u
 nil : Vector α 0
 cons : α → {n : Nat} → Vector α n → Vector α (n+1)
namespace Vector
def add [Add α] : Vector α n → Vector α n → Vector α n
 nil, nil => nil
 cons a as, cons b bs => cons (a + b) (add as bs)
end Vector
Using these new features, you can write the other vector functions defined in the previous sections more compactly as follows:
inductive Vector (α : Type u) : Nat → Type u
 nil : Vector α 0
 cons : α → {n : Nat} → Vector α n → Vector α (n+1)
namespace Vector
def head : Vector α (n+1) → α
 cons a as => a
def tail : Vector α (n+1) → Vector α n
 cons a as => as
theorem eta : (v : Vector α (n+1)) → cons (head v) (tail v) = v
 cons a as => rfl
def map (f : α → β → γ) : Vector α n → Vector β n → Vector γ n
 nil, nil => nil
 cons a as, cons b bs => cons (f a b) (map f as bs)
def zip : Vector α n → Vector β n → Vector (α × β) n
 nil, nil => nil
 cons a as, cons b bs => cons (a, b) (zip as bs)
end Vector
Match Expressions
Lean also provides a compiler for matchwith expressions found in many functional languages.
def isNotZero (m : Nat) : Bool :=
match m with
 0 => false
 n+1 => true
This does not look very different from an ordinary pattern matching
definition, but the point is that a match
can be used anywhere in
an expression, and with arbitrary arguments.
def isNotZero (m : Nat) : Bool :=
match m with
 0 => false
 n+1 => true
def filter (p : α → Bool) : List α → List α
 [] => []
 a :: as =>
match p a with
 true => a :: filter p as
 false => filter p as
example : filter isNotZero [1, 0, 0, 3, 0] = [1, 3] := rfl
Here is another example:
def foo (n : Nat) (b c : Bool) :=
5 + match n  5, b && c with
 0, true => 0
 m+1, true => m + 7
 0, false => 5
 m+1, false => m + 3
#eval foo 7 true false
example : foo 7 true false = 9 := rfl
Lean uses the match
construct internally to implement patternmatching in all parts of the system.
Thus, all four of these definitions have the same net effect.
def bar₁ : Nat × Nat → Nat
 (m, n) => m + n
def bar₂ (p : Nat × Nat) : Nat :=
match p with
 (m, n) => m + n
def bar₃ : Nat × Nat → Nat :=
fun (m, n) => m + n
def bar₄ (p : Nat × Nat) : Nat :=
let (m, n) := p; m + n
These variations are equally useful for destructing propositions:
variable (p q : Nat → Prop)
example : (∃ x, p x) → (∃ y, q y) → ∃ x y, p x ∧ q y
 ⟨x, px⟩, ⟨y, qy⟩ => ⟨x, y, px, qy⟩
example (h₀ : ∃ x, p x) (h₁ : ∃ y, q y)
: ∃ x y, p x ∧ q y :=
match h₀, h₁ with
 ⟨x, px⟩, ⟨y, qy⟩ => ⟨x, y, px, qy⟩
example : (∃ x, p x) → (∃ y, q y) → ∃ x y, p x ∧ q y :=
fun ⟨x, px⟩ ⟨y, qy⟩ => ⟨x, y, px, qy⟩
example (h₀ : ∃ x, p x) (h₁ : ∃ y, q y)
: ∃ x y, p x ∧ q y :=
let ⟨x, px⟩ := h₀
let ⟨y, qy⟩ := h₁
⟨x, y, px, qy⟩
Local Recursive Declarations
You can define local recursive declarations using the let rec
keyword.
def replicate (n : Nat) (a : α) : List α :=
let rec loop : Nat → List α → List α
 0, as => as
 n+1, as => loop n (a::as)
loop n []
#check @replicate.loop
 {α : Type} → α → Nat → List α → List α
Lean creates an auxiliary declaration for each let rec
. In the example above,
it created the declaration replicate.loop
for the let rec loop
occurring at replicate
.
Note that, Lean "closes" the declaration by adding any local variable occurring in the
let rec
declaration as additional parameters. For example, the local variable a
occurs
at let rec loop
.
You can also use let rec
in tactic mode and for creating proofs by induction.
def replicate (n : Nat) (a : α) : List α :=
let rec loop : Nat → List α → List α
 0, as => as
 n+1, as => loop n (a::as)
loop n []
theorem length_replicate (n : Nat) (a : α) : (replicate n a).length = n := by
let rec aux (n : Nat) (as : List α)
: (replicate.loop a n as).length = n + as.length := by
match n with
 0 => simp [replicate.loop]
 n+1 => simp [replicate.loop, aux n, Nat.add_succ, Nat.succ_add]
exact aux n []
You can also introduce auxiliary recursive declarations using a where
clause after your definition.
Lean converts them into a let rec
.
def replicate (n : Nat) (a : α) : List α :=
loop n []
where
loop : Nat → List α → List α
 0, as => as
 n+1, as => loop n (a::as)
theorem length_replicate (n : Nat) (a : α) : (replicate n a).length = n := by
exact aux n []
where
aux (n : Nat) (as : List α)
: (replicate.loop a n as).length = n + as.length := by
match n with
 0 => simp [replicate.loop]
 n+1 => simp [replicate.loop, aux n, Nat.add_succ, Nat.succ_add]
Exercises

Open a namespace
Hidden
to avoid naming conflicts, and use the equation compiler to define addition, multiplication, and exponentiation on the natural numbers. Then use the equation compiler to derive some of their basic properties. 
Similarly, use the equation compiler to define some basic operations on lists (like the
reverse
function) and prove theorems about lists by induction (such as the fact thatreverse (reverse xs) = xs
for any listxs
). 
Define your own function to carry out courseofvalue recursion on the natural numbers. Similarly, see if you can figure out how to define
WellFounded.fix
on your own. 
Following the examples in Section Dependent Pattern Matching, define a function that will append two vectors. This is tricky; you will have to define an auxiliary function.

Consider the following type of arithmetic expressions. The idea is that
var n
is a variable,vₙ
, andconst n
is the constant whose value isn
.
inductive Expr where
 const : Nat → Expr
 var : Nat → Expr
 plus : Expr → Expr → Expr
 times : Expr → Expr → Expr
deriving Repr
open Expr
def sampleExpr : Expr :=
plus (times (var 0) (const 7)) (times (const 2) (var 1))
Here sampleExpr
represents (v₀ * 7) + (2 * v₁)
.
Write a function that evaluates such an expression, evaluating each var n
to v n
.
inductive Expr where
 const : Nat → Expr
 var : Nat → Expr
 plus : Expr → Expr → Expr
 times : Expr → Expr → Expr
deriving Repr
open Expr
def sampleExpr : Expr :=
plus (times (var 0) (const 7)) (times (const 2) (var 1))
def eval (v : Nat → Nat) : Expr → Nat
 const n => sorry
 var n => v n
 plus e₁ e₂ => sorry
 times e₁ e₂ => sorry
def sampleVal : Nat → Nat
 0 => 5
 1 => 6
 _ => 0
 Try it out. You should get 47 here.
 #eval eval sampleVal sampleExpr
Implement "constant fusion," a procedure that simplifies subterms like
5 + 7
to 12
. Using the auxiliary function simpConst
,
define a function "fuse": to simplify a plus or a times, first
simplify the arguments recursively, and then apply simpConst
to
try to simplify the result.
inductive Expr where
 const : Nat → Expr
 var : Nat → Expr
 plus : Expr → Expr → Expr
 times : Expr → Expr → Expr
deriving Repr
open Expr
def eval (v : Nat → Nat) : Expr → Nat
 const n => sorry
 var n => v n
 plus e₁ e₂ => sorry
 times e₁ e₂ => sorry
def simpConst : Expr → Expr
 plus (const n₁) (const n₂) => const (n₁ + n₂)
 times (const n₁) (const n₂) => const (n₁ * n₂)
 e => e
def fuse : Expr → Expr := sorry
theorem simpConst_eq (v : Nat → Nat)
: ∀ e : Expr, eval v (simpConst e) = eval v e :=
sorry
theorem fuse_eq (v : Nat → Nat)
: ∀ e : Expr, eval v (fuse e) = eval v e :=
sorry
The last two theorems show that the definitions preserve the value.
Structures and Records
We have seen that Lean's foundational system includes inductive
types. We have, moreover, noted that it is a remarkable fact that it
is possible to construct a substantial edifice of mathematics based on
nothing more than the type universes, dependent arrow types, and inductive types;
everything else follows from those. The Lean standard library contains
many instances of inductive types (e.g., Nat
, Prod
, List
),
and even the logical connectives are defined using inductive types.
Recall that a nonrecursive inductive type that contains only one
constructor is called a structure or record. The product type is a
structure, as is the dependent product (Sigma) type.
In general, whenever we define a structure S
, we usually
define projection functions that allow us to "destruct" each
instance of S
and retrieve the values that are stored in its
fields. The functions prod.fst
and prod.snd
, which return the
first and second elements of a pair, are examples of such projections.
When writing programs or formalizing mathematics, it is not uncommon
to define structures containing many fields. The structure
command, available in Lean, provides infrastructure to support this
process. When we define a structure using this command, Lean
automatically generates all the projection functions. The
structure
command also allows us to define new structures based on
previously defined ones. Moreover, Lean provides convenient notation
for defining instances of a given structure.
Declaring Structures
The structure command is essentially a "front end" for defining
inductive data types. Every structure
declaration introduces a
namespace with the same name. The general form is as follows:
structure <name> <parameters> <parentstructures> where
<constructor> :: <fields>
Most parts are optional. Here is an example:
structure Point (α : Type u) where
mk :: (x : α) (y : α)
Values of type Point
are created using Point.mk a b
, and the
fields of a point p
are accessed using Point.x p
and
Point.y p
(but p.x
and p.y
also work, see below).
The structure command also generates useful recursors and
theorems. Here are some of the constructions generated for the
declaration above.
structure Point (α : Type u) where
mk :: (x : α) (y : α)
#check Point  a Type
#check @Point.rec  the eliminator
#check @Point.mk  the constructor
#check @Point.x  a projection
#check @Point.y  a projection
If the constructor name is not provided, then a constructor is named
mk
by default. You can also avoid the parentheses around field
names if you add a line break between each field.
structure Point (α : Type u) where
x : α
y : α
Here are some simple theorems and expressions that use the generated
constructions. As usual, you can avoid the prefix Point
by using
the command open Point
.
structure Point (α : Type u) where
x : α
y : α
#eval Point.x (Point.mk 10 20)
#eval Point.y (Point.mk 10 20)
open Point
example (a b : α) : x (mk a b) = a :=
rfl
example (a b : α) : y (mk a b) = b :=
rfl
Given p : Point Nat
, the dot notation p.x
is shorthand for
Point.x p
. This provides a convenient way of accessing the fields
of a structure.
structure Point (α : Type u) where
x : α
y : α
def p := Point.mk 10 20
#check p.x  Nat
#eval p.x  10
#eval p.y  20
The dot notation is convenient not just for accessing the projections
of a record, but also for applying functions defined in a namespace
with the same name. Recall from the Conjunction section if p
has type Point
, the expression p.foo
is interpreted as
Point.foo p
, assuming that the first nonimplicit argument to
foo
has type Point
. The expression p.add q
is therefore
shorthand for Point.add p q
in the example below.
structure Point (α : Type u) where
x : α
y : α
deriving Repr
def Point.add (p q : Point Nat) :=
mk (p.x + q.x) (p.y + q.y)
def p : Point Nat := Point.mk 1 2
def q : Point Nat := Point.mk 3 4
#eval p.add q  {x := 4, y := 6}
In the next chapter, you will learn how to define a function like
add
so that it works generically for elements of Point α
rather than just Point Nat
, assuming α
has an associated
addition operation.
More generally, given an expression p.foo x y z
where p : Point
,
Lean will insert p
at the first argument to Point.foo
of type
Point
. For example, with the definition of scalar multiplication
below, p.smul 3
is interpreted as Point.smul 3 p
.
structure Point (α : Type u) where
x : α
y : α
deriving Repr
def Point.smul (n : Nat) (p : Point Nat) :=
Point.mk (n * p.x) (n * p.y)
def p : Point Nat := Point.mk 1 2
#eval p.smul 3  {x := 3, y := 6}
It is common to use a similar trick with the List.map
function,
which takes a list as its second nonimplicit argument:
#check @List.map
def xs : List Nat := [1, 2, 3]
def f : Nat → Nat := fun x => x * x
#eval xs.map f  [1, 4, 9]
Here xs.map f
is interpreted as List.map f xs
.
Objects
We have been using constructors to create elements of a structure type. For structures containing many fields, this is often inconvenient, because we have to remember the order in which the fields were defined. Lean therefore provides the following alternative notations for defining elements of a structure type.
{ (<fieldname> := <expr>)* : structuretype }
or
{ (<fieldname> := <expr>)* }
The suffix : structuretype
can be omitted whenever the name of
the structure can be inferred from the expected type. For example, we
use this notation to define "points." The order that the fields are
specified does not matter, so all the expressions below define the
same point.
structure Point (α : Type u) where
x : α
y : α
#check { x := 10, y := 20 : Point Nat }  Point ℕ
#check { y := 20, x := 10 : Point _ }
#check ({ x := 10, y := 20 } : Point Nat)
example : Point Nat :=
{ y := 20, x := 10 }
If the value of a field is not specified, Lean tries to infer it. If the unspecified fields cannot be inferred, Lean flags an error indicating the corresponding placeholder could not be synthesized.
structure MyStruct where
{α : Type u}
{β : Type v}
a : α
b : β
#check { a := 10, b := true : MyStruct }
Record update is another common operation which amounts to creating
a new record object by modifying the value of one or more fields in an
old one. Lean allows you to specify that unassigned fields in the
specification of a record should be taken from a previously defined
structure object s
by adding the annotation s with
before the field
assignments. If more than one record object is provided, then they are
visited in order until Lean finds one that contains the unspecified
field. Lean raises an error if any of the field names remain
unspecified after all the objects are visited.
structure Point (α : Type u) where
x : α
y : α
deriving Repr
def p : Point Nat :=
{ x := 1, y := 2 }
#eval { p with y := 3 }  { x := 1, y := 3 }
#eval { p with x := 4 }  { x := 4, y := 2 }
structure Point3 (α : Type u) where
x : α
y : α
z : α
def q : Point3 Nat :=
{ x := 5, y := 5, z := 5 }
def r : Point3 Nat :=
{ p, q with x := 6 }
example : r.x = 6 := rfl
example : r.y = 2 := rfl
example : r.z = 5 := rfl
Inheritance
We can extend existing structures by adding new fields. This feature allows us to simulate a form of inheritance.
structure Point (α : Type u) where
x : α
y : α
inductive Color where
 red  green  blue
structure ColorPoint (α : Type u) extends Point α where
c : Color
In the next example, we define a structure using multiple inheritance, and then define an object using objects of the parent structures.
structure Point (α : Type u) where
x : α
y : α
z : α
structure RGBValue where
red : Nat
green : Nat
blue : Nat
structure RedGreenPoint (α : Type u) extends Point α, RGBValue where
no_blue : blue = 0
def p : Point Nat :=
{ x := 10, y := 10, z := 20 }
def rgp : RedGreenPoint Nat :=
{ p with red := 200, green := 40, blue := 0, no_blue := rfl }
example : rgp.x = 10 := rfl
example : rgp.red = 200 := rfl
Type Classes
Type classes were introduced as a principled way of enabling adhoc polymorphism in functional programming languages. We first observe that it would be easy to implement an adhoc polymorphic function (such as addition) if the function simply took the typespecific implementation of addition as an argument and then called that implementation on the remaining arguments. For example, suppose we declare a structure in Lean to hold implementations of addition.
namespace Ex
structure Add (a : Type) where
add : a → a → a
#check @Add.add
 Add.add : {a : Type} → Add a → a → a → a
end Ex
In the above Lean code, the field add
has type
Add.add : {a : Type} → Add a → a → a → a
where the curly braces around the type a
mean that it is an implicit argument.
We could implement double
by:
namespace Ex
structure Add (a : Type) where
add : a → a → a
def double (s : Add a) (x : a) : a :=
s.add x x
#eval double { add := Nat.add } 10
 20
#eval double { add := Nat.mul } 10
 100
#eval double { add := Int.add } 10
 20
end Ex
Note that you can double a natural number n
by double { add := Nat.add } n
.
Of course, it would be highly cumbersome for users to manually pass the
implementations around in this way.
Indeed, it would defeat most of the potential benefits of adhoc
polymorphism.
The main idea behind type classes is to make arguments such as Add a
implicit,
and to use a database of userdefined instances to synthesize the desired instances
automatically through a process known as typeclass resolution. In Lean, by changing
structure
to class
in the example above, the type of Add.add
becomes:
namespace Ex
class Add (a : Type) where
add : a → a → a
#check @Add.add
 Add.add : {a : Type} → [self : Add a] → a → a → a
end Ex
where the square brackets indicate that the argument of type Add a
is instance implicit,
i.e. that it should be synthesized using typeclass resolution. This version of
add
is the Lean analogue of the Haskell term add :: Add a => a > a > a
.
Similarly, we can register instances by:
namespace Ex
class Add (a : Type) where
add : a → a → a
instance : Add Nat where
add := Nat.add
instance : Add Int where
add := Int.add
instance : Add Float where
add := Float.add
end Ex
Then for n : Nat
and m : Nat
, the term Add.add n m
triggers typeclass resolution with
the goal of Add Nat
, and typeclass resolution will synthesize the instance for Nat
above.
We can now reimplement double
using an instance implicit by:
namespace Ex
class Add (a : Type) where
add : a → a → a
instance : Add Nat where
add := Nat.add
instance : Add Int where
add := Int.add
instance : Add Float where
add := Float.add
def double [Add a] (x : a) : a :=
Add.add x x
#check @double
 @double : {a : Type} → [inst : Add a] → a → a
#eval double 10
 20
#eval double (10 : Int)
 100
#eval double (7 : Float)
 14.000000
#eval double (239.0 + 2)
 482.000000
end Ex
In general, instances may depend on other instances in complicated ways. For example,
you can declare an (anonymous) instance stating that if a
has addition, then Array a
has addition:
instance [Add a] : Add (Array a) where
add x y := Array.zipWith x y (· + ·)
#eval Add.add #[1, 2] #[3, 4]
 #[4, 6]
#eval #[1, 2] + #[3, 4]
 #[4, 6]
Note that (· + ·)
is notation for fun x y => x + y
in Lean.
The example above demonstrates how type classes are used to overload notation.
Now, we explore another application. We often need an arbitrary element of a given type.
Recall that types may not have any elements in Lean.
It often happens that we would like a definition to return an arbitrary element in a "corner case."
For example, we may like the expression head xs
to be of type a
when xs
is of type List a
.
Similarly, many theorems hold under the additional assumption that a type is not empty.
For example, if a
is a type, exists x : a, x = x
is true only if a
is not empty.
The standard library defines a type class Inhabited
to enable type class inference to infer a
"default" element of an inhabited type.
Let us start with the first step of the program above, declaring an appropriate class:
namespace Ex
class Inhabited (a : Type u) where
default : a
#check @Inhabited.default
 Inhabited.default : {a : Type u} → [self : Inhabited a] → a
end Ex
Note Inhabited.default
doesn't have any explicit arguments.
An element of the class Inhabited a
is simply an expression of the form Inhabited.mk x
, for some element x : a
.
The projection Inhabited.default
will allow us to "extract" such an element of a
from an element of Inhabited a
.
Now we populate the class with some instances:
namespace Ex
class Inhabited (a : Type _) where
default : a
instance : Inhabited Bool where
default := true
instance : Inhabited Nat where
default := 0
instance : Inhabited Unit where
default := ()
instance : Inhabited Prop where
default := True
#eval (Inhabited.default : Nat)
 0
#eval (Inhabited.default : Bool)
 true
end Ex
You can use the command export
to create the alias default
for Inhabited.default
namespace Ex
class Inhabited (a : Type _) where
default : a
instance : Inhabited Bool where
default := true
instance : Inhabited Nat where
default := 0
instance : Inhabited Unit where
default := ()
instance : Inhabited Prop where
default := True
export Inhabited (default)
#eval (default : Nat)
 0
#eval (default : Bool)
 true
end Ex
Chaining Instances
If that were the extent of type class inference, it would not be all that impressive; it would be simply a mechanism of storing a list of instances for the elaborator to find in a lookup table. What makes type class inference powerful is that one can chain instances. That is, an instance declaration can in turn depend on an implicit instance of a type class. This causes class inference to chain through instances recursively, backtracking when necessary, in a Prologlike search.
For example, the following definition shows that if two types a
and b
are inhabited, then so is their product:
instance [Inhabited a] [Inhabited b] : Inhabited (a × b) where
default := (default, default)
With this added to the earlier instance declarations, type class instance can infer, for example, a default element of Nat × Bool
:
namespace Ex
class Inhabited (a : Type u) where
default : a
instance : Inhabited Bool where
default := true
instance : Inhabited Nat where
default := 0
opaque default [Inhabited a] : a :=
Inhabited.default
instance [Inhabited a] [Inhabited b] : Inhabited (a × b) where
default := (default, default)
#eval (default : Nat × Bool)
 (0, true)
end Ex
Similarly, we can inhabit type function with suitable constant functions:
instance [Inhabited b] : Inhabited (a → b) where
default := fun _ => default
As an exercise, try defining default instances for other types, such as List
and Sum
types.
The Lean standard library contains the definition inferInstance
. It has type {α : Sort u} → [i : α] → α
,
and is useful for triggering the type class resolution procedure when the expected type is an instance.
#check (inferInstance : Inhabited Nat)  Inhabited Nat
def foo : Inhabited (Nat × Nat) :=
inferInstance
theorem ex : foo.default = (default, default) :=
rfl
You can use the command #print
to inspect how simple inferInstance
is.
#print inferInstance
ToString
The polymorphic method toString
has type {α : Type u} → [ToString α] → α → String
. You implement the instance
for your own types and use chaining to convert complex values into strings. Lean comes with ToString
instances
for most builtin types.
structure Person where
name : String
age : Nat
instance : ToString Person where
toString p := p.name ++ "@" ++ toString p.age
#eval toString { name := "Leo", age := 542 : Person }
#eval toString ({ name := "Daniel", age := 18 : Person }, "hello")
Numerals
Numerals are polymorphic in Lean. You can use a numeral (e.g., 2
) to denote an element of any type that implements
the type class OfNat
.
structure Rational where
num : Int
den : Nat
inv : den ≠ 0
instance : OfNat Rational n where
ofNat := { num := n, den := 1, inv := by decide }
instance : ToString Rational where
toString r := s!"{r.num}/{r.den}"
#eval (2 : Rational)  2/1
#check (2 : Rational)  Rational
#check (2 : Nat)  Nat
Lean elaborates the terms (2 : Nat)
and (2 : Rational)
as
OfNat.ofNat Nat 2 (instOfNatNat 2)
and
OfNat.ofNat Rational 2 (instOfNatRational 2)
respectively.
We say the numerals 2
occurring in the elaborated terms are raw natural numbers.
You can input the raw natural number 2
using the macro nat_lit 2
.
#check nat_lit 2  Nat
Raw natural numbers are not polymorphic.
The OfNat
instance is parametric on the numeral. So, you can define instances for particular numerals.
The second argument is often a variable as in the example above, or a raw natural number.
class Monoid (α : Type u) where
unit : α
op : α → α → α
instance [s : Monoid α] : OfNat α (nat_lit 1) where
ofNat := s.unit
def getUnit [Monoid α] : α :=
1
Output Parameters
By default, Lean only tries to synthesize an instance Inhabited T
when the term T
is known and does not
contain missing parts. The following command produces the error
"typeclass instance problem is stuck, it is often due to metavariables ?m.7
" because the type has a missing part (i.e., the _
).
#check_failure (inferInstance : Inhabited (Nat × _))
You can view the parameter of the type class Inhabited
as an input value for the type class synthesizer.
When a type class has multiple parameters, you can mark some of them as output parameters.
Lean will start type class synthesizer even when these parameters have missing parts.
In the following example, we use output parameters to define a heterogeneous polymorphic
multiplication.
namespace Ex
class HMul (α : Type u) (β : Type v) (γ : outParam (Type w)) where
hMul : α → β → γ
export HMul (hMul)
instance : HMul Nat Nat Nat where
hMul := Nat.mul
instance : HMul Nat (Array Nat) (Array Nat) where
hMul a bs := bs.map (fun b => hMul a b)
#eval hMul 4 3  12
#eval hMul 4 #[2, 3, 4]  #[8, 12, 16]
end Ex
The parameters α
and β
are considered input parameters and γ
an output one.
Given an application hMul a b
, after the types of a
and b
are known, the type class
synthesizer is invoked, and the resulting type is obtained from the output parameter γ
.
In the example above, we defined two instances. The first one is the homogeneous
multiplication for natural numbers. The second is the scalar multiplication for arrays.
Note that you chain instances and generalize the second instance.
namespace Ex
class HMul (α : Type u) (β : Type v) (γ : outParam (Type w)) where
hMul : α → β → γ
export HMul (hMul)
instance : HMul Nat Nat Nat where
hMul := Nat.mul
instance : HMul Int Int Int where
hMul := Int.mul
instance [HMul α β γ] : HMul α (Array β) (Array γ) where
hMul a bs := bs.map (fun b => hMul a b)
#eval hMul 4 3  12
#eval hMul 4 #[2, 3, 4]  #[8, 12, 16]
#eval hMul (2) #[3, 1, 4]  #[6, 2, 8]
#eval hMul 2 #[#[2, 3], #[0, 4]]  #[#[4, 6], #[0, 8]]
end Ex
You can use our new scalar array multiplication instance on arrays of type Array β
with a scalar of type α
whenever you have an instance HMul α β γ
.
In the last #eval
, note that the instance was used twice on an array of arrays.
Default Instances
In the class HMul
, the parameters α
and β
are treated as input values.
Thus, type class synthesis only starts after these two types are known. This may often
be too restrictive.
namespace Ex
class HMul (α : Type u) (β : Type v) (γ : outParam (Type w)) where
hMul : α → β → γ
export HMul (hMul)
instance : HMul Int Int Int where
hMul := Int.mul
def xs : List Int := [1, 2, 3]
 Error "typeclass instance problem is stuck, it is often due to metavariables HMul ?m.89 ?m.90 ?m.91"
#check_failure fun y => xs.map (fun x => hMul x y)
end Ex
The instance HMul
is not synthesized by Lean because the type of y
has not been provided.
However, it is natural to assume that the type of y
and x
should be the same in
this kind of situation. We can achieve exactly that using default instances.
namespace Ex
class HMul (α : Type u) (β : Type v) (γ : outParam (Type w)) where
hMul : α → β → γ
export HMul (hMul)
@[default_instance]
instance : HMul Int Int Int where
hMul := Int.mul
def xs : List Int := [1, 2, 3]
#check fun y => xs.map (fun x => hMul x y)  Int → List Int
end Ex
By tagging the instance above with the attribute default_instance
, we are instructing Lean
to use this instance on pending type class synthesis problems.
The actual Lean implementation defines homogeneous and heterogeneous classes for arithmetical operators.
Moreover, a+b
, a*b
, ab
, a/b
, and a%b
are notations for the heterogeneous versions.
The instance OfNat Nat n
is the default instance (with priority 100) for the OfNat
class. This is why the numeral
2
has type Nat
when the expected type is not known. You can define default instances with higher
priority to override the builtin ones.
structure Rational where
num : Int
den : Nat
inv : den ≠ 0
@[default_instance 200]
instance : OfNat Rational n where
ofNat := { num := n, den := 1, inv := by decide }
instance : ToString Rational where
toString r := s!"{r.num}/{r.den}"
#check 2  Rational
Priorities are also useful to control the interaction between different default instances.
For example, suppose xs
has type List α
. When elaborating xs.map (fun x => 2 * x)
, we want the homogeneous instance for multiplication
to have higher priority than the default instance for OfNat
. This is particularly important when we have implemented only the instance
HMul α α α
, and did not implement HMul Nat α α
.
Now, we reveal how the notation a*b
is defined in Lean.
namespace Ex
class OfNat (α : Type u) (n : Nat) where
ofNat : α
@[default_instance]
instance (n : Nat) : OfNat Nat n where
ofNat := n
class HMul (α : Type u) (β : Type v) (γ : outParam (Type w)) where
hMul : α → β → γ
class Mul (α : Type u) where
mul : α → α → α
@[default_instance 10]
instance [Mul α] : HMul α α α where
hMul a b := Mul.mul a b
infixl:70 " * " => HMul.hMul
end Ex
The Mul
class is convenient for types that only implement the homogeneous multiplication.
Local Instances
Type classes are implemented using attributes in Lean. Thus, you can
use the local
modifier to indicate that they only have effect until
the current section
or namespace
is closed, or until the end
of the current file.
structure Point where
x : Nat
y : Nat
section
local instance : Add Point where
add a b := { x := a.x + b.x, y := a.y + b.y }
def double (p : Point) :=
p + p
end  instance `Add Point` is not active anymore
 def triple (p : Point) :=
 p + p + p  Error: failed to synthesize instance
You can also temporarily disable an instance using the attribute
command
until the current section
or namespace
is closed, or until the end
of the current file.
structure Point where
x : Nat
y : Nat
instance addPoint : Add Point where
add a b := { x := a.x + b.x, y := a.y + b.y }
def double (p : Point) :=
p + p
attribute [instance] addPoint
 def triple (p : Point) :=
 p + p + p  Error: failed to synthesize instance
We recommend you only use this command to diagnose problems.
Scoped Instances
You can also declare scoped instances in namespaces. This kind of instance is only active when you are inside of the namespace or open the namespace.
structure Point where
x : Nat
y : Nat
namespace Point
scoped instance : Add Point where
add a b := { x := a.x + b.x, y := a.y + b.y }
def double (p : Point) :=
p + p
end Point
 instance `Add Point` is not active anymore
 #check fun (p : Point) => p + p + p  Error
namespace Point
 instance `Add Point` is active again
#check fun (p : Point) => p + p + p
end Point
open Point  activates instance `Add Point`
#check fun (p : Point) => p + p + p
You can use the command open scoped <namespace>
to activate scoped attributes but will
not "open" the names from the namespace.
structure Point where
x : Nat
y : Nat
namespace Point
scoped instance : Add Point where
add a b := { x := a.x + b.x, y := a.y + b.y }
def double (p : Point) :=
p + p
end Point
open scoped Point  activates instance `Add Point`
#check fun (p : Point) => p + p + p
 #check fun (p : Point) => double p  Error: unknown identifier 'double'
Decidable Propositions
Let us consider another example of a type class defined in the
standard library, namely the type class of Decidable
propositions. Roughly speaking, an element of Prop
is said to be
decidable if we can decide whether it is true or false. The
distinction is only useful in constructive mathematics; classically,
every proposition is decidable. But if we use the classical principle,
say, to define a function by cases, that function will not be
computable. Algorithmically speaking, the Decidable
type class can
be used to infer a procedure that effectively determines whether or
not the proposition is true. As a result, the type class supports such
computational definitions when they are possible while at the same
time allowing a smooth transition to the use of classical definitions
and classical reasoning.
In the standard library, Decidable
is defined formally as follows:
namespace Hidden
class inductive Decidable (p : Prop) where
 isFalse (h : ¬p) : Decidable p
 isTrue (h : p) : Decidable p
end Hidden
Logically speaking, having an element t : Decidable p
is stronger
than having an element t : p ∨ ¬p
; it enables us to define values
of an arbitrary type depending on the truth value of p
. For
example, for the expression if p then a else b
to make sense, we
need to know that p
is decidable. That expression is syntactic
sugar for ite p a b
, where ite
is defined as follows:
namespace Hidden
def ite {α : Sort u} (c : Prop) [h : Decidable c] (t e : α) : α :=
Decidable.casesOn (motive := fun _ => α) h (fun _ => e) (fun _ => t)
end Hidden
The standard library also contains a variant of ite
called
dite
, the dependent ifthenelse expression. It is defined as
follows:
namespace Hidden
def dite {α : Sort u} (c : Prop) [h : Decidable c] (t : c → α) (e : Not c → α) : α :=
Decidable.casesOn (motive := fun _ => α) h e t
end Hidden
That is, in dite c t e
, we can assume hc : c
in the "then"
branch, and hnc : ¬ c
in the "else" branch. To make dite
more
convenient to use, Lean allows us to write if h : c then t else e
instead of dite c (λ h : c => t) (λ h : ¬ c => e)
.
Without classical logic, we cannot prove that every proposition is decidable. But we can prove that certain propositions are decidable. For example, we can prove the decidability of basic operations like equality and comparisons on the natural numbers and the integers. Moreover, decidability is preserved under propositional connectives:
#check @instDecidableAnd
 {p q : Prop} → [Decidable p] → [Decidable q] → Decidable (And p q)
#check @instDecidableOr
#check @instDecidableNot
Thus we can carry out definitions by cases on decidable predicates on the natural numbers:
def step (a b x : Nat) : Nat :=
if x < a ∨ x > b then 0 else 1
set_option pp.explicit true
#print step
Turning on implicit arguments shows that the elaborator has inferred
the decidability of the proposition x < a ∨ x > b
, simply by
applying appropriate instances.
With the classical axioms, we can prove that every proposition is
decidable. You can import the classical axioms and make the generic
instance of decidability available by opening the Classical
namespace.
open Classical
Thereafter Decidable p
has an instance for every p
.
Thus all theorems in the library
that rely on decidability assumptions are freely available when you
want to reason classically. In Chapter Axioms and Computation,
we will see that using the law of the
excluded middle to define functions can prevent them from being used
computationally. Thus, the standard library assigns a low priority to
the propDecidable
instance.
namespace Hidden
open Classical
noncomputable scoped
instance (priority := low) propDecidable (a : Prop) : Decidable a :=
choice < match em a with
 Or.inl h => ⟨isTrue h⟩
 Or.inr h => ⟨isFalse h⟩
end Hidden
This guarantees that Lean will favor other instances and fall back on
propDecidable
only after other attempts to infer decidability have
failed.
The Decidable
type class also provides a bit of smallscale
automation for proving theorems. The standard library introduces the
tactic decide
that uses the Decidable
instance to solve simple goals.
example : 10 < 5 ∨ 1 > 0 := by
decide
example : ¬ (True ∧ False) := by
decide
example : 10 * 20 = 200 := by
decide
theorem ex : True ∧ 2 = 1+1 := by
decide
#print ex
 theorem ex : True ∧ 2 = 1 + 1 :=
 of_decide_eq_true (Eq.refl true)
#check @of_decide_eq_true
 ∀ {p : Prop} [Decidable p], decide p = true → p
#check @decide
 (p : Prop) → [Decidable p] → Bool
They work as follows. The expression decide p
tries to infer a
decision procedure for p
, and, if it is successful, evaluates to
either true
or false
. In particular, if p
is a true closed
expression, decide p
will reduce definitionally to the Boolean true
.
On the assumption that decide p = true
holds, of_decide_eq_true
produces a proof of p
. The tactic decide
puts it all together to
prove a target p
. By the previous observations,
decide
will succeed any time the inferred decision procedure
for c
has enough information to evaluate, definitionally, to the isTrue
case.
Managing Type Class Inference
If you are ever in a situation where you need to supply an expression
that Lean can infer by type class inference, you can ask Lean to carry
out the inference using inferInstance
:
def foo : Add Nat := inferInstance
def bar : Inhabited (Nat → Nat) := inferInstance
#check @inferInstance
 {α : Sort u} → [α] → α
In fact, you can use Lean's (t : T)
notation to specify the class whose instance you are looking for,
in a concise manner:
#check (inferInstance : Add Nat)
You can also use the auxiliary definition inferInstanceAs
:
#check inferInstanceAs (Add Nat)
#check @inferInstanceAs
 (α : Sort u) → [α] → α
Sometimes Lean can't find an instance because the class is buried
under a definition. For example, Lean cannot
find an instance of Inhabited (Set α)
. We can declare one
explicitly:
def Set (α : Type u) := α → Prop
 fails
 example : Inhabited (Set α) :=
 inferInstance
instance : Inhabited (Set α) :=
inferInstanceAs (Inhabited (α → Prop))
At times, you may find that the type class inference fails to find an expected instance, or, worse, falls into an infinite loop and times out. To help debug in these situations, Lean enables you to request a trace of the search:
set_option trace.Meta.synthInstance true
If you are using VS Code, you can read the results by hovering over
the relevant theorem or definition, or opening the messages window
with CtrlShiftEnter
. In Emacs, you can use Cc Cx
to run an
independent Lean process on your file, and the output buffer will show
a trace every time the type class resolution procedure is subsequently
triggered.
You can also limit the search using the following options:
set_option synthInstance.maxHeartbeats 10000
set_option synthInstance.maxSize 400
Option synthInstance.maxHeartbeats
specifies the maximum amount of
heartbeats per typeclass resolution problem. A heartbeat is the number of
(small) memory allocations (in thousands), 0 means there is no limit.
Option synthInstance.maxSize
is the maximum number of instances used
to construct a solution in the type class instance synthesis procedure.
Remember also that in both the VS Code and Emacs editor modes, tab
completion works in set_option
, to help you find suitable options.
As noted above, the type class instances in a given context represent a Prologlike program, which gives rise to a backtracking search. Both the efficiency of the program and the solutions that are found can depend on the order in which the system tries the instance. Instances which are declared last are tried first. Moreover, if instances are declared in other modules, the order in which they are tried depends on the order in which namespaces are opened. Instances declared in namespaces which are opened later are tried earlier.
You can change the order that type class instances are tried by assigning them a priority. When an instance is declared, it is assigned a default priority value. You can assign other priorities when defining an instance. The following example illustrates how this is done:
class Foo where
a : Nat
b : Nat
instance (priority := default+1) i1 : Foo where
a := 1
b := 1
instance i2 : Foo where
a := 2
b := 2
example : Foo.a = 1 :=
rfl
instance (priority := default+2) i3 : Foo where
a := 3
b := 3
example : Foo.a = 3 :=
rfl
Coercions using Type Classes
The most basic type of coercion maps elements of one type to another. For example, a coercion from Nat
to Int
allows us to view any element n : Nat
as an element of Int
. But some coercions depend on parameters; for example, for any type α
, we can view any element as : List α
as an element of Set α
, namely, the set of elements occurring in the list. The corresponding coercion is defined on the "family" of types List α
, parameterized by α
.
Lean allows us to declare three kinds of coercions:
 from a family of types to another family of types
 from a family of types to the class of sorts
 from a family of types to the class of function types
The first kind of coercion allows us to view any element of a member of the source family as an element of a corresponding member of the target family. The second kind of coercion allows us to view any element of a member of the source family as a type. The third kind of coercion allows us to view any element of the source family as a function. Let us consider each of these in turn.
In Lean, coercions are implemented on top of the type class resolution framework. We define a coercion from α
to β
by declaring an instance of Coe α β
. For example, we can define a coercion from Bool
to Prop
as follows:
instance : Coe Bool Prop where
coe b := b = true
This enables us to use boolean terms in ifthenelse expressions:
#eval if true then 5 else 3
#eval if false then 5 else 3
We can define a coercion from List α
to Set α
as follows:
def Set (α : Type u) := α → Prop
def Set.empty {α : Type u} : Set α := fun _ => False
def Set.mem (a : α) (s : Set α) : Prop := s a
def Set.singleton (a : α) : Set α := fun x => x = a
def Set.union (a b : Set α) : Set α := fun x => a x ∨ b x
notation "{ " a " }" => Set.singleton a
infix:55 " ∪ " => Set.union
def List.toSet : List α → Set α
 [] => Set.empty
 a::as => {a} ∪ as.toSet
instance : Coe (List α) (Set α) where
coe a := a.toSet
def s : Set Nat := {1}
#check s ∪ [2, 3]
 s ∪ List.toSet [2, 3] : Set Nat
We can use the notation ↑
to force a coercion to be introduced in a particular place. It is also helpful to make our intent clear, and work around limitations of the coercion resolution system.
def Set (α : Type u) := α → Prop
def Set.empty {α : Type u} : Set α := fun _ => False
def Set.mem (a : α) (s : Set α) : Prop := s a
def Set.singleton (a : α) : Set α := fun x => x = a
def Set.union (a b : Set α) : Set α := fun x => a x ∨ b x
notation "{ " a " }" => Set.singleton a
infix:55 " ∪ " => Set.union
def List.toSet : List α → Set α
 [] => Set.empty
 a::as => {a} ∪ as.toSet
instance : Coe (List α) (Set α) where
coe a := a.toSet
def s : Set Nat := {1}
#check let x := ↑[2, 3]; s ∪ x
 let x := List.toSet [2, 3]; s ∪ x : Set Nat
#check let x := [2, 3]; s ∪ x
 let x := [2, 3]; s ∪ List.toSet x : Set Nat
Lean also supports dependent coercions using the type class CoeDep
. For example, we cannot coerce arbitrary propositions to Bool
, only the ones that implement the Decidable
typeclass.
instance (p : Prop) [Decidable p] : CoeDep Prop p Bool where
coe := decide p
Lean will also chain (nondependent) coercions as necessary. Actually, the type class CoeT
is the transitive closure of Coe
.
Let us now consider the second kind of coercion. By the class of sorts, we mean the collection of universes Type u
. A coercion of the second kind is of the form:
c : (x1 : A1) → ... → (xn : An) → F x1 ... xn → Type u
where F
is a family of types as above. This allows us to write s : t
whenever t
is of type F a1 ... an
. In other words, the coercion allows us to view the elements of F a1 ... an
as types. This is very useful when defining algebraic structures in which one component, the carrier of the structure, is a Type
. For example, we can define a semigroup as follows:
structure Semigroup where
carrier : Type u
mul : carrier → carrier → carrier
mul_assoc (a b c : carrier) : mul (mul a b) c = mul a (mul b c)
instance (S : Semigroup) : Mul S.carrier where
mul a b := S.mul a b
In other words, a semigroup consists of a type, carrier
, and a multiplication, mul
, with the property that the multiplication is associative. The instance
command allows us to write a * b
instead of Semigroup.mul S a b
whenever we have a b : S.carrier
; notice that Lean can infer the argument S
from the types of a
and b
. The function Semigroup.carrier
maps the class Semigroup
to the sort Type u
:
structure Semigroup where
carrier : Type u
mul : carrier → carrier → carrier
mul_assoc (a b c : carrier) : mul (mul a b) c = mul a (mul b c)
instance (S : Semigroup) : Mul S.carrier where
mul a b := S.mul a b
#check Semigroup.carrier
If we declare this function to be a coercion, then whenever we have a semigroup S : Semigroup
, we can write a : S
instead of a : S.carrier
:
structure Semigroup where
carrier : Type u
mul : carrier → carrier → carrier
mul_assoc (a b c : carrier) : mul (mul a b) c = mul a (mul b c)
instance (S : Semigroup) : Mul S.carrier where
mul a b := S.mul a b
instance : CoeSort Semigroup (Type u) where
coe s := s.carrier
example (S : Semigroup) (a b c : S) : (a * b) * c = a * (b * c) :=
Semigroup.mul_assoc _ a b c
It is the coercion that makes it possible to write (a b c : S)
. Note that, we define an instance of CoeSort Semigroup (Type u)
instead of Coe Semigroup (Type u)
.
By the class of function types, we mean the collection of Pi types (z : B) → C
. The third kind of coercion has the form:
c : (x1 : A1) → ... → (xn : An) → (y : F x1 ... xn) → (z : B) → C
where F
is again a family of types and B
and C
can depend on x1, ..., xn, y
. This makes it possible to write t s
whenever t
is an element of F a1 ... an
. In other words, the coercion enables us to view elements of F a1 ... an
as functions. Continuing the example above, we can define the notion of a morphism between semigroups S1
and S2
. That is, a function from the carrier of S1
to the carrier of S2
(note the implicit coercion) that respects the multiplication. The projection morphism.mor
takes a morphism to the underlying function:
structure Semigroup where
carrier : Type u
mul : carrier → carrier → carrier
mul_assoc (a b c : carrier) : mul (mul a b) c = mul a (mul b c)
instance (S : Semigroup) : Mul S.carrier where
mul a b := S.mul a b
instance : CoeSort Semigroup (Type u) where
coe s := s.carrier
structure Morphism (S1 S2 : Semigroup) where
mor : S1 → S2
resp_mul : ∀ a b : S1, mor (a * b) = (mor a) * (mor b)
#check @Morphism.mor
As a result, it is a prime candidate for the third type of coercion.
structure Semigroup where
carrier : Type u
mul : carrier → carrier → carrier
mul_assoc (a b c : carrier) : mul (mul a b) c = mul a (mul b c)
instance (S : Semigroup) : Mul S.carrier where
mul a b := S.mul a b
instance : CoeSort Semigroup (Type u) where
coe s := s.carrier
structure Morphism (S1 S2 : Semigroup) where
mor : S1 → S2
resp_mul : ∀ a b : S1, mor (a * b) = (mor a) * (mor b)
instance (S1 S2 : Semigroup) : CoeFun (Morphism S1 S2) (fun _ => S1 → S2) where
coe m := m.mor
theorem resp_mul {S1 S2 : Semigroup} (f : Morphism S1 S2) (a b : S1)
: f (a * b) = f a * f b :=
f.resp_mul a b
example (S1 S2 : Semigroup) (f : Morphism S1 S2) (a : S1) :
f (a * a * a) = f a * f a * f a :=
calc f (a * a * a)
_ = f (a * a) * f a := by rw [resp_mul f]
_ = f a * f a * f a := by rw [resp_mul f]
With the coercion in place, we can write f (a * a * a)
instead of f.mor (a * a * a)
. When the Morphism
, f
, is used where a function is expected, Lean inserts the coercion. Similar to CoeSort
, we have yet another class CoeFun
for this class of coercions. The field F
is used to specify the function type we are coercing to. This type may depend on the type we are coercing from.
The Conversion Tactic Mode
Inside a tactic block, one can use the keyword conv
to enter
conversion mode. This mode allows to travel inside assumptions and
goals, even inside function abstractions and dependent arrows, to apply rewriting or
simplifying steps.
Basic navigation and rewriting
As a first example, let us prove example
(a b c : Nat) : a * (b * c) = a * (c * b)
(examples in this file are somewhat artificial since
other tactics could finish them immediately). The naive
first attempt is to enter tactic mode and try rw [Nat.mul_comm]
. But this
transforms the goal into b * c * a = a * (c * b)
, after commuting the
very first multiplication appearing in the term. There are several
ways to fix this issue, and one way is to use a more precise tool:
the conversion mode. The following code block shows the current target
after each line.
example (a b c : Nat) : a * (b * c) = a * (c * b) := by
conv =>
 ⊢ a * (b * c) = a * (c * b)
lhs
 ⊢ a * (b * c)
congr
 2 goals: ⊢ a, ⊢ b * c
rfl
 ⊢ b * c
rw [Nat.mul_comm]
The above snippet shows three navigation commands:
lhs
navigates to the left hand side of a relation (here equality), there is also arhs
navigating to the right hand side.congr
creates as many targets as there are (nondependent and explicit) arguments to the current head function (here the head function is multiplication).rfl
closes target using reflexivity.
Once arrived at the relevant target, we can use rw
as in normal
tactic mode.
The second main reason to use conversion mode is to rewrite under
binders. Suppose we want to prove example
(fun x : Nat => 0 + x) = (fun x => x)
.
The naive first attempt is to enter tactic mode and try
rw [Nat.zero_add]
. But this fails with a frustrating
error: tactic 'rewrite' failed, did not find instance of the pattern
in the target expression
0 + ?n
⊢ (fun x => 0 + x) = fun x => x
The solution is:
example : (fun x : Nat => 0 + x) = (fun x => x) := by
conv =>
lhs
intro x
rw [Nat.zero_add]
where intro x
is the navigation command entering inside the fun
binder.
Note that this example is somewhat artificial, one could also do:
example : (fun x : Nat => 0 + x) = (fun x => x) := by
funext x; rw [Nat.zero_add]
or just
example : (fun x : Nat => 0 + x) = (fun x => x) := by
simp
conv
can also rewrite a hypothesis h
from the local context, using conv at h
.
Pattern matching
Navigation using the above commands can be tedious. One can shortcut it using pattern matching as follows:
example (a b c : Nat) : a * (b * c) = a * (c * b) := by
conv in b * c => rw [Nat.mul_comm]
which is just syntax sugar for
example (a b c : Nat) : a * (b * c) = a * (c * b) := by
conv =>
pattern b * c
rw [Nat.mul_comm]
Of course, wildcards are allowed:
example (a b c : Nat) : a * (b * c) = a * (c * b) := by
conv in _ * c => rw [Nat.mul_comm]
Structuring conversion tactics
Curly brackets and .
can also be used in conv
mode to structure tactics.
example (a b c : Nat) : (0 + a) * (b * c) = a * (c * b) := by
conv =>
lhs
congr
. rw [Nat.zero_add]
. rw [Nat.mul_comm]
Other tactics inside conversion mode
arg i
enter thei
th nondependent explicit argument of an application.
example (a b c : Nat) : a * (b * c) = a * (c * b) := by
conv =>
 ⊢ a * (b * c) = a * (c * b)
lhs
 ⊢ a * (b * c)
arg 2
 ⊢ b * c
rw [Nat.mul_comm]

args
alternative name forcongr
. 
simp
applies the simplifier to the current goal. It supports the same options available in regular tactic mode.
def f (x : Nat) :=
if x > 0 then x + 1 else x + 2
example (g : Nat → Nat) (h₁ : g x = x + 1) (h₂ : x > 0) : g x = f x := by
conv =>
rhs
simp [f, h₂]
exact h₁
enter [1, x, 2, y]
iteratearg
andintro
with the given arguments. It is just the macro:
syntax enterArg := ident <> group("@"? num)
syntax "enter " "[" (colGt enterArg),+ "]": conv
macro_rules
 `(conv enter [$i:num]) => `(conv arg $i)
 `(conv enter [@$i:num]) => `(conv arg @$i)
 `(conv enter [$id:ident]) => `(conv ext $id)
 `(conv enter [$arg:enterArg, $args,*]) => `(conv (enter [$arg]; enter [$args,*]))

done
fail if there are unsolved goals. 
trace_state
display the current tactic state. 
whnf
put term in weak head normal form. 
tactic => <tactic sequence>
go back to regular tactic mode. This is useful for discharging goals not supported byconv
mode, and applying custom congruence and extensionality lemmas.
example (g : Nat → Nat → Nat)
(h₁ : ∀ x, x ≠ 0 → g x x = 1)
(h₂ : x ≠ 0)
: g x x + x = 1 + x := by
conv =>
lhs
 ⊢ g x x + x
arg 1
 ⊢ g x x
rw [h₁]
 2 goals: ⊢ 1, ⊢ x ≠ 0
. skip
. tactic => exact h₂
apply <term>
is syntax sugar fortactic => apply <term>
example (g : Nat → Nat → Nat)
(h₁ : ∀ x, x ≠ 0 → g x x = 1)
(h₂ : x ≠ 0)
: g x x + x = 1 + x := by
conv =>
lhs
arg 1
rw [h₁]
. skip
. apply h₂
Axioms and Computation
We have seen that the version of the Calculus of Constructions that
has been implemented in Lean includes dependent function types,
inductive types, and a hierarchy of universes that starts with an
impredicative, proofirrelevant Prop
at the bottom. In this
chapter, we consider ways of extending the CIC with additional axioms
and rules. Extending a foundational system in such a way is often
convenient; it can make it possible to prove more theorems, as well as
make it easier to prove theorems that could have been proved
otherwise. But there can be negative consequences of adding additional
axioms, consequences which may go beyond concerns about their
correctness. In particular, the use of axioms bears on the
computational content of definitions and theorems, in ways we will
explore here.
Lean is designed to support both computational and classical
reasoning. Users that are so inclined can stick to a "computationally
pure" fragment, which guarantees that closed expressions in the system
evaluate to canonical normal forms. In particular, any closed
computationally pure expression of type Nat
, for example, will
reduce to a numeral.
Lean's standard library defines an additional axiom, propositional
extensionality, and a quotient construction which in turn implies the
principle of function extensionality. These extensions are used, for
example, to develop theories of sets and finite sets. We will see
below that using these theorems can block evaluation in Lean's kernel,
so that closed terms of type Nat
no longer evaluate to numerals. But
Lean erases types and propositional information when compiling
definitions to bytecode for its virtual machine evaluator, and since
these axioms only add new propositions, they are compatible with that
computational interpretation. Even computationally inclined users may
wish to use the classical law of the excluded middle to reason about
computation. This also blocks evaluation in the kernel, but it is
compatible with compilation to bytecode.
The standard library also defines a choice principle that is entirely
antithetical to a computational interpretation, since it magically
produces "data" from a proposition asserting its existence. Its use is
essential to some classical constructions, and users can import it
when needed. But expressions that use this construction to produce
data do not have computational content, and in Lean we are required to
mark such definitions as noncomputable
to flag that fact.
Using a clever trick (known as Diaconescu's theorem), one can use propositional extensionality, function extensionality, and choice to derive the law of the excluded middle. As noted above, however, use of the law of the excluded middle is still compatible with bytecode compilation and code extraction, as are other classical principles, as long as they are not used to manufacture data.
To summarize, then, on top of the underlying framework of universes, dependent function types, and inductive types, the standard library adds three additional components:
 the axiom of propositional extensionality
 a quotient construction, which implies function extensionality
 a choice principle, which produces data from an existential proposition.
The first two of these block normalization within Lean, but are compatible with bytecode evaluation, whereas the third is not amenable to computational interpretation. We will spell out the details more precisely below.
Historical and Philosophical Context
For most of its history, mathematics was essentially computational:
geometry dealt with constructions of geometric objects, algebra was
concerned with algorithmic solutions to systems of equations, and
analysis provided means to compute the future behavior of systems
evolving over time. From the proof of a theorem to the effect that
"for every x
, there is a y
such that ...", it was generally
straightforward to extract an algorithm to compute such a y
given
x
.
In the nineteenth century, however, increases in the complexity of mathematical arguments pushed mathematicians to develop new styles of reasoning that suppress algorithmic information and invoke descriptions of mathematical objects that abstract away the details of how those objects are represented. The goal was to obtain a powerful "conceptual" understanding without getting bogged down in computational details, but this had the effect of admitting mathematical theorems that are simply false on a direct computational reading.
There is still fairly uniform agreement today that computation is important to mathematics. But there are different views as to how best to address computational concerns. From a constructive point of view, it is a mistake to separate mathematics from its computational roots; every meaningful mathematical theorem should have a direct computational interpretation. From a classical point of view, it is more fruitful to maintain a separation of concerns: we can use one language and body of methods to write computer programs, while maintaining the freedom to use nonconstructive theories and methods to reason about them. Lean is designed to support both of these approaches. Core parts of the library are developed constructively, but the system also provides support for carrying out classical mathematical reasoning.
Computationally, the purest part of dependent type theory avoids the
use of Prop
entirely. Inductive types and dependent function types
can be viewed as data types, and terms of these types can be
"evaluated" by applying reduction rules until no more rules can be
applied. In principle, any closed term (that is, term with no free
variables) of type Nat
should evaluate to a numeral, succ (... (succ zero)...)
.
Introducing a proofirrelevant Prop
and marking theorems
irreducible represents a first step towards separation of
concerns. The intention is that elements of a type p : Prop
should
play no role in computation, and so the particular construction of a
term t : p
is "irrelevant" in that sense. One can still define
computational objects that incorporate elements of type Prop
; the
point is that these elements can help us reason about the effects of
the computation, but can be ignored when we extract "code" from the
term. Elements of type Prop
are not entirely innocuous,
however. They include equations s = t : α
for any type α
, and
such equations can be used as casts, to type check terms. Below, we
will see examples of how such casts can block computation in the
system. However, computation is still possible under an evaluation
scheme that erases propositional content, ignores intermediate typing
constraints, and reduces terms until they reach a normal form. This is
precisely what Lean's virtual machine does.
Having adopted a proofirrelevant Prop
, one might consider it
legitimate to use, for example, the law of the excluded middle,
p ∨ ¬p
, where p
is any proposition. Of course, this, too, can block
computation according to the rules of CIC, but it does not block
bytecode evaluation, as described above. It is only the choice
principles discussed in :numref:choice
that completely erase the
distinction between the proofirrelevant and datarelevant parts of
the theory.
Propositional Extensionality
Propositional extensionality is the following axiom:
namespace Hidden
axiom propext {a b : Prop} : (a ↔ b) → a = b
end Hidden
It asserts that when two propositions imply one another, they are
actually equal. This is consistent with settheoretic interpretations
in which any element a : Prop
is either empty or the singleton set
{*}
, for some distinguished element *
. The axiom has the
effect that equivalent propositions can be substituted for one another
in any context:
theorem thm₁ (a b c d e : Prop) (h : a ↔ b) : (c ∧ a ∧ d → e) ↔ (c ∧ b ∧ d → e) :=
propext h ▸ Iff.refl _
theorem thm₂ (a b : Prop) (p : Prop → Prop) (h : a ↔ b) (h₁ : p a) : p b :=
propext h ▸ h₁
Function Extensionality
Similar to propositional extensionality, function extensionality
asserts that any two functions of type (x : α) → β x
that agree on
all their inputs are equal.
universe u v
#check (@funext :
{α : Type u}
→ {β : α → Type u}
→ {f g : (x : α) → β x}
→ (∀ (x : α), f x = g x)
→ f = g)
#print funext
From a classical, settheoretic perspective, this is exactly what it means for two functions to be equal. This is known as an "extensional" view of functions. From a constructive perspective, however, it is sometimes more natural to think of functions as algorithms, or computer programs, that are presented in some explicit way. It is certainly the case that two computer programs can compute the same answer for every input despite the fact that they are syntactically quite different. In much the same way, you might want to maintain a view of functions that does not force you to identify two functions that have the same input / output behavior. This is known as an "intensional" view of functions.
In fact, function extensionality follows from the existence of
quotients, which we describe in the next section. In the Lean standard
library, therefore, funext
is thus
proved from the quotient construction.
Suppose that for α : Type
we define the Set α := α → Prop
to
denote the type of subsets of α
, essentially identifying subsets
with predicates. By combining funext
and propext
, we obtain an
extensional theory of such sets:
def Set (α : Type u) := α → Prop
namespace Set
def mem (x : α) (a : Set α) := a x
infix:50 (priority := high) "∈" => mem
theorem setext {a b : Set α} (h : ∀ x, x ∈ a ↔ x ∈ b) : a = b :=
funext (fun x => propext (h x))
end Set
We can then proceed to define the empty set and set intersection, for example, and prove set identities:
def Set (α : Type u) := α → Prop
namespace Set
def mem (x : α) (a : Set α) := a x
infix:50 (priority := high) "∈" => mem
theorem setext {a b : Set α} (h : ∀ x, x ∈ a ↔ x ∈ b) : a = b :=
funext (fun x => propext (h x))
def empty : Set α := fun x => False
notation (priority := high) "∅" => empty
def inter (a b : Set α) : Set α :=
fun x => x ∈ a ∧ x ∈ b
infix:70 " ∩ " => inter
theorem inter_self (a : Set α) : a ∩ a = a :=
setext fun x => Iff.intro
(fun ⟨h, _⟩ => h)
(fun h => ⟨h, h⟩)
theorem inter_empty (a : Set α) : a ∩ ∅ = ∅ :=
setext fun x => Iff.intro
(fun ⟨_, h⟩ => h)
(fun h => False.elim h)
theorem empty_inter (a : Set α) : ∅ ∩ a = ∅ :=
setext fun x => Iff.intro
(fun ⟨h, _⟩ => h)
(fun h => False.elim h)
theorem inter.comm (a b : Set α) : a ∩ b = b ∩ a :=
setext fun x => Iff.intro
(fun ⟨h₁, h₂⟩ => ⟨h₂, h₁⟩)
(fun ⟨h₁, h₂⟩ => ⟨h₂, h₁⟩)
end Set
The following is an example of how function extensionality blocks computation inside the Lean kernel.
def f (x : Nat) := x
def g (x : Nat) := 0 + x
theorem f_eq_g : f = g :=
funext fun x => (Nat.zero_add x).symm
def val : Nat :=
Eq.recOn (motive := fun _ _ => Nat) f_eq_g 0
 does not reduce to 0
#reduce val
 evaluates to 0
#eval val
First, we show that the two functions f
and g
are equal using
function extensionality, and then we cast 0
of type Nat
by
replacing f
by g
in the type. Of course, the cast is
vacuous, because Nat
does not depend on f
. But that is enough
to do the damage: under the computational rules of the system, we now
have a closed term of Nat
that does not reduce to a numeral. In this
case, we may be tempted to reduce the expression to 0
. But in
nontrivial examples, eliminating cast changes the type of the term,
which might make an ambient expression type incorrect. The virtual
machine, however, has no trouble evaluating the expression to
0
. Here is a similarly contrived example that shows how
propext
can get in the way.
theorem tteq : (True ∧ True) = True :=
propext (Iff.intro (fun ⟨h, _⟩ => h) (fun h => ⟨h, h⟩))
def val : Nat :=
Eq.recOn (motive := fun _ _ => Nat) tteq 0
 does not reduce to 0
#reduce val
 evaluates to 0
#eval val
Current research programs, including work on observational type theory and cubical type theory, aim to extend type theory in ways that permit reductions for casts involving function extensionality, quotients, and more. But the solutions are not so clear cut, and the rules of Lean's underlying calculus do not sanction such reductions.
In a sense, however, a cast does not change the meaning of an
expression. Rather, it is a mechanism to reason about the expression's
type. Given an appropriate semantics, it then makes sense to reduce
terms in ways that preserve their meaning, ignoring the intermediate
bookkeeping needed to make the reductions type correct. In that case,
adding new axioms in Prop
does not matter; by proof irrelevance,
an expression in Prop
carries no information, and can be safely
ignored by the reduction procedures.
Quotients
Let α
be any type, and let r
be an equivalence relation on
α
. It is mathematically common to form the "quotient" α / r
,
that is, the type of elements of α
"modulo" r
. Set
theoretically, one can view α / r
as the set of equivalence
classes of α
modulo r
. If f : α → β
is any function that
respects the equivalence relation in the sense that for every
x y : α
, r x y
implies f x = f y
, then f
"lifts" to a function
f' : α / r → β
defined on each equivalence class ⟦x⟧
by
f' ⟦x⟧ = f x
. Lean's standard library extends the Calculus of
Constructions with additional constants that perform exactly these
constructions, and installs this last equation as a definitional
reduction rule.
In its most basic form, the quotient construction does not even
require r
to be an equivalence relation. The following constants
are built into Lean:
namespace Hidden
universe u v
axiom Quot : {α : Sort u} → (α → α → Prop) → Sort u
axiom Quot.mk : {α : Sort u} → (r : α → α → Prop) → α → Quot r
axiom Quot.ind :
∀ {α : Sort u} {r : α → α → Prop} {β : Quot r → Prop},
(∀ a, β (Quot.mk r a)) → (q : Quot r) → β q
axiom Quot.lift :
{α : Sort u} → {r : α → α → Prop} → {β : Sort u} → (f : α → β)
→ (∀ a b, r a b → f a = f b) → Quot r → β
end Hidden
The first one forms a type Quot r
given a type α
by any binary
relation r
on α
. The second maps α
to Quot α
, so that
if r : α → α → Prop
and a : α
, then Quot.mk r a
is an
element of Quot r
. The third principle, Quot.ind
, says that
every element of Quot.mk r a
is of this form. As for
Quot.lift
, given a function f : α → β
, if h
is a proof
that f
respects the relation r
, then Quot.lift f h
is the
corresponding function on Quot r
. The idea is that for each
element a
in α
, the function Quot.lift f h
maps
Quot.mk r a
(the r
class containing a
) to f a
, wherein h
shows that this function is well defined. In fact, the computation
principle is declared as a reduction rule, as the proof below makes
clear.
def mod7Rel (x y : Nat) : Prop :=
x % 7 = y % 7
 the quotient type
#check (Quot mod7Rel : Type)
 the class of a
#check (Quot.mk mod7Rel 4 : Quot mod7Rel)
def f (x : Nat) : Bool :=
x % 7 = 0
theorem f_respects (a b : Nat) (h : mod7Rel a b) : f a = f b := by
simp [mod7Rel, f] at *
rw [h]
#check (Quot.lift f f_respects : Quot mod7Rel → Bool)
 the computation principle
example (a : Nat) : Quot.lift f f_respects (Quot.mk mod7Rel a) = f a :=
rfl
The four constants, Quot
, Quot.mk
, Quot.ind
, and
Quot.lift
in and of themselves are not very strong. You can check
that the Quot.ind
is satisfied if we take Quot r
to be simply
α
, and take Quot.lift
to be the identity function (ignoring
h
). For that reason, these four constants are not viewed as
additional axioms.
They are, like inductively defined types and the associated constructors and recursors, viewed as part of the logical framework.
What makes the Quot
construction into a bona fide quotient is the
following additional axiom:
namespace Hidden
universe u v
axiom Quot.sound :
∀ {α : Type u} {r : α → α → Prop} {a b : α},
r a b → Quot.mk r a = Quot.mk r b
end Hidden
This is the axiom that asserts that any two elements of α
that are
related by r
become identified in the quotient. If a theorem or
definition makes use of Quot.sound
, it will show up in the
#print axioms
command.
Of course, the quotient construction is most commonly used in
situations when r
is an equivalence relation. Given r
as
above, if we define r'
according to the rule r' a b
iff
Quot.mk r a = Quot.mk r b
, then it's clear that r'
is an
equivalence relation. Indeed, r'
is the kernel of the function
a ↦ quot.mk r a
. The axiom Quot.sound
says that r a b
implies r' a b
. Using Quot.lift
and Quot.ind
, we can show
that r'
is the smallest equivalence relation containing r
, in
the sense that if r''
is any equivalence relation containing
r
, then r' a b
implies r'' a b
. In particular, if r
was an equivalence relation to start with, then for all a
and
b
we have r a b
iff r' a b
.
To support this common use case, the standard library defines the notion of a setoid, which is simply a type with an associated equivalence relation:
namespace Hidden
class Setoid (α : Sort u) where
r : α → α → Prop
iseqv : Equivalence r
instance {α : Sort u} [Setoid α] : HasEquiv α :=
⟨Setoid.r⟩
namespace Setoid
variable {α : Sort u} [Setoid α]
theorem refl (a : α) : a ≈ a :=
iseqv.refl a
theorem symm {a b : α} (hab : a ≈ b) : b ≈ a :=
iseqv.symm hab
theorem trans {a b c : α} (hab : a ≈ b) (hbc : b ≈ c) : a ≈ c :=
iseqv.trans hab hbc
end Setoid
end Hidden
Given a type α
, a relation r
on α
, and a proof p
that r
is an equivalence relation, we can define Setoid.mk r p
as an instance of the setoid class.
namespace Hidden
def Quotient {α : Sort u} (s : Setoid α) :=
@Quot α Setoid.r
end Hidden
The constants Quotient.mk
, Quotient.ind
, Quotient.lift
,
and Quotient.sound
are nothing more than the specializations of
the corresponding elements of Quot
. The fact that type class
inference can find the setoid associated to a type α
brings a
number of benefits. First, we can use the notation a ≈ b
(entered
with \approx
) for Setoid.r a b
, where the instance of
Setoid
is implicit in the notation Setoid.r
. We can use the
generic theorems Setoid.refl
, Setoid.symm
, Setoid.trans
to
reason about the relation. Specifically with quotients we can use the
generic notation ⟦a⟧
for Quot.mk Setoid.r
where the instance
of Setoid
is implicit in the notation Setoid.r
, as well as the
theorem Quotient.exact
:
universe u
#check (@Quotient.exact :
∀ {α : Sort u} {s : Setoid α} {a b : α},
Quotient.mk s a = Quotient.mk s b → a ≈ b)
Together with Quotient.sound
, this implies that the elements of
the quotient correspond exactly to the equivalence classes of elements
in α
.
Recall that in the standard library, α × β
represents the
Cartesian product of the types α
and β
. To illustrate the use
of quotients, let us define the type of unordered pairs of elements
of a type α
as a quotient of the type α × α
. First, we define
the relevant equivalence relation:
private def eqv (p₁ p₂ : α × α) : Prop :=
(p₁.1 = p₂.1 ∧ p₁.2 = p₂.2) ∨ (p₁.1 = p₂.2 ∧ p₁.2 = p₂.1)
infix:50 " ~ " => eqv
The next step is to prove that eqv
is in fact an equivalence
relation, which is to say, it is reflexive, symmetric and
transitive. We can prove these three facts in a convenient and
readable way by using dependent pattern matching to perform
caseanalysis and break the hypotheses into pieces that are then
reassembled to produce the conclusion.
private def eqv (p₁ p₂ : α × α) : Prop :=
(p₁.1 = p₂.1 ∧ p₁.2 = p₂.2) ∨ (p₁.1 = p₂.2 ∧ p₁.2 = p₂.1)
infix:50 " ~ " => eqv
private theorem eqv.refl (p : α × α) : p ~ p :=
Or.inl ⟨rfl, rfl⟩
private theorem eqv.symm : ∀ {p₁ p₂ : α × α}, p₁ ~ p₂ → p₂ ~ p₁
 (a₁, a₂), (b₁, b₂), (Or.inl ⟨a₁b₁, a₂b₂⟩) =>
Or.inl (by simp_all)
 (a₁, a₂), (b₁, b₂), (Or.inr ⟨a₁b₂, a₂b₁⟩) =>
Or.inr (by simp_all)
private theorem eqv.trans : ∀ {p₁ p₂ p₃ : α × α}, p₁ ~ p₂ → p₂ ~ p₃ → p₁ ~ p₃
 (a₁, a₂), (b₁, b₂), (c₁, c₂), Or.inl ⟨a₁b₁, a₂b₂⟩, Or.inl ⟨b₁c₁, b₂c₂⟩ =>
Or.inl (by simp_all)
 (a₁, a₂), (b₁, b₂), (c₁, c₂), Or.inl ⟨a₁b₁, a₂b₂⟩, Or.inr ⟨b₁c₂, b₂c₁⟩ =>
Or.inr (by simp_all)
 (a₁, a₂), (b₁, b₂), (c₁, c₂), Or.inr ⟨a₁b₂, a₂b₁⟩, Or.inl ⟨b₁c₁, b₂c₂⟩ =>
Or.inr (by simp_all)
 (a₁, a₂), (b₁, b₂), (c₁, c₂), Or.inr ⟨a₁b₂, a₂b₁⟩, Or.inr ⟨b₁c₂, b₂c₁⟩ =>
Or.inl (by simp_all)
private theorem is_equivalence : Equivalence (@eqv α) :=
{ refl := eqv.refl, symm := eqv.symm, trans := eqv.trans }
Now that we have proved that eqv
is an equivalence relation, we
can construct a Setoid (α × α)
, and use it to define the type
UProd α
of unordered pairs.
private def eqv (p₁ p₂ : α × α) : Prop :=
(p₁.1 = p₂.1 ∧ p₁.2 = p₂.2) ∨ (p₁.1 = p₂.2 ∧ p₁.2 = p₂.1)
infix:50 " ~ " => eqv
private theorem eqv.refl (p : α × α) : p ~ p :=
Or.inl ⟨rfl, rfl⟩
private theorem eqv.symm : ∀ {p₁ p₂ : α × α}, p₁ ~ p₂ → p₂ ~ p₁
 (a₁, a₂), (b₁, b₂), (Or.inl ⟨a₁b₁, a₂b₂⟩) =>
Or.inl (by simp_all)
 (a₁, a₂), (b₁, b₂), (Or.inr ⟨a₁b₂, a₂b₁⟩) =>
Or.inr (by simp_all)
private theorem eqv.trans : ∀ {p₁ p₂ p₃ : α × α}, p₁ ~ p₂ → p₂ ~ p₃ → p₁ ~ p₃
 (a₁, a₂), (b₁, b₂), (c₁, c₂), Or.inl ⟨a₁b₁, a₂b₂⟩, Or.inl ⟨b₁c₁, b₂c₂⟩ =>
Or.inl (by simp_all)
 (a₁, a₂), (b₁, b₂), (c₁, c₂), Or.inl ⟨a₁b₁, a₂b₂⟩, Or.inr ⟨b₁c₂, b₂c₁⟩ =>
Or.inr (by simp_all)
 (a₁, a₂), (b₁, b₂), (c₁, c₂), Or.inr ⟨a₁b₂, a₂b₁⟩, Or.inl ⟨b₁c₁, b₂c₂⟩ =>
Or.inr (by simp_all)
 (a₁, a₂), (b₁, b₂), (c₁, c₂), Or.inr ⟨a₁b₂, a₂b₁⟩, Or.inr ⟨b₁c₂, b₂c₁⟩ =>
Or.inl (by simp_all)
private theorem is_equivalence : Equivalence (@eqv α) :=
{ refl := eqv.refl, symm := eqv.symm, trans := eqv.trans }
instance uprodSetoid (α : Type u) : Setoid (α × α) where
r := eqv
iseqv := is_equivalence
def UProd (α : Type u) : Type u :=
Quotient (uprodSetoid α)
namespace UProd
def mk {α : Type} (a₁ a₂ : α) : UProd α :=
Quotient.mk' (a₁, a₂)
notation "{ " a₁ ", " a₂ " }" => mk a₁ a₂
end UProd
Notice that we locally define the notation {a₁, a₂}
for unordered
pairs as Quotient.mk (a₁, a₂)
. This is useful for illustrative
purposes, but it is not a good idea in general, since the notation
will shadow other uses of curly brackets, such as for records and
sets.
We can easily prove that {a₁, a₂} = {a₂, a₁}
using Quot.sound
,
since we have (a₁, a₂) ~ (a₂, a₁)
.
private def eqv (p₁ p₂ : α × α) : Prop :=
(p₁.1 = p₂.1 ∧ p₁.2 = p₂.2) ∨ (p₁.1 = p₂.2 ∧ p₁.2 = p₂.1)
infix:50 " ~ " => eqv
private theorem eqv.refl (p : α × α) : p ~ p :=
Or.inl ⟨rfl, rfl⟩
private theorem eqv.symm : ∀ {p₁ p₂ : α × α}, p₁ ~ p₂ → p₂ ~ p₁
 (a₁, a₂), (b₁, b₂), (Or.inl ⟨a₁b₁, a₂b₂⟩) =>
Or.inl (by simp_all)
 (a₁, a₂), (b₁, b₂), (Or.inr ⟨a₁b₂, a₂b₁⟩) =>
Or.inr (by simp_all)
private theorem eqv.trans : ∀ {p₁ p₂ p₃ : α × α}, p₁ ~ p₂ → p₂ ~ p₃ → p₁ ~ p₃
 (a₁, a₂), (b₁, b₂), (c₁, c₂), Or.inl ⟨a₁b₁, a₂b₂⟩, Or.inl ⟨b₁c₁, b₂c₂⟩ =>
Or.inl (by simp_all)
 (a₁, a₂), (b₁, b₂), (c₁, c₂), Or.inl ⟨a₁b₁, a₂b₂⟩, Or.inr ⟨b₁c₂, b₂c₁⟩ =>
Or.inr (by simp_all)
 (a₁, a₂), (b₁, b₂), (c₁, c₂), Or.inr ⟨a₁b₂, a₂b₁⟩, Or.inl ⟨b₁c₁, b₂c₂⟩ =>
Or.inr (by simp_all)
 (a₁, a₂), (b₁, b₂), (c₁, c₂), Or.inr ⟨a₁b₂, a₂b₁⟩, Or.inr ⟨b₁c₂, b₂c₁⟩ =>
Or.inl (by simp_all)
private theorem is_equivalence : Equivalence (@eqv α) :=
{ refl := eqv.refl, symm := eqv.symm, trans := eqv.trans }
instance uprodSetoid (α : Type u) : Setoid (α × α) where
r := eqv
iseqv := is_equivalence
def UProd (α : Type u) : Type u :=
Quotient (uprodSetoid α)
namespace UProd
def mk {α : Type} (a₁ a₂ : α) : UProd α :=
Quotient.mk' (a₁, a₂)
notation "{ " a₁ ", " a₂ " }" => mk a₁ a₂
theorem mk_eq_mk (a₁ a₂ : α) : {a₁, a₂} = {a₂, a₁} :=
Quot.sound (Or.inr ⟨rfl, rfl⟩)
end UProd
To complete the example, given a : α
and u : uprod α
, we
define the proposition a ∈ u
which should hold if a
is one of
the elements of the unordered pair u
. First, we define a similar
proposition mem_fn a u
on (ordered) pairs; then we show that
mem_fn
respects the equivalence relation eqv
with the lemma
mem_respects
. This is an idiom that is used extensively in the
Lean standard library.
private def eqv (p₁ p₂ : α × α) : Prop :=
(p₁.1 = p₂.1 ∧ p₁.2 = p₂.2) ∨ (p₁.1 = p₂.2 ∧ p₁.2 = p₂.1)
infix:50 " ~ " => eqv
private theorem eqv.refl (p : α × α) : p ~ p :=
Or.inl ⟨rfl, rfl⟩
private theorem eqv.symm : ∀ {p₁ p₂ : α × α}, p₁ ~ p₂ → p₂ ~ p₁
 (a₁, a₂), (b₁, b₂), (Or.inl ⟨a₁b₁, a₂b₂⟩) =>
Or.inl (by simp_all)
 (a₁, a₂), (b₁, b₂), (Or.inr ⟨a₁b₂, a₂b₁⟩) =>
Or.inr (by simp_all)
private theorem eqv.trans : ∀ {p₁ p₂ p₃ : α × α}, p₁ ~ p₂ → p₂ ~ p₃ → p₁ ~ p₃
 (a₁, a₂), (b₁, b₂), (c₁, c₂), Or.inl ⟨a₁b₁, a₂b₂⟩, Or.inl ⟨b₁c₁, b₂c₂⟩ =>
Or.inl (by simp_all)
 (a₁, a₂), (b₁, b₂), (c₁, c₂), Or.inl ⟨a₁b₁, a₂b₂⟩, Or.inr ⟨b₁c₂, b₂c₁⟩ =>
Or.inr (by simp_all)
 (a₁, a₂), (b₁, b₂), (c₁, c₂), Or.inr ⟨a₁b₂, a₂b₁⟩, Or.inl ⟨b₁c₁, b₂c₂⟩ =>
Or.inr (by simp_all)
 (a₁, a₂), (b₁, b₂), (c₁, c₂), Or.inr ⟨a₁b₂, a₂b₁⟩, Or.inr ⟨b₁c₂, b₂c₁⟩ =>
Or.inl (by simp_all)
private theorem is_equivalence : Equivalence (@eqv α) :=
{ refl := eqv.refl, symm := eqv.symm, trans := eqv.trans }
instance uprodSetoid (α : Type u) : Setoid (α × α) where
r := eqv
iseqv := is_equivalence
def UProd (α : Type u) : Type u :=
Quotient (uprodSetoid α)
namespace UProd
def mk {α : Type} (a₁ a₂ : α) : UProd α :=
Quotient.mk' (a₁, a₂)
notation "{ " a₁ ", " a₂ " }" => mk a₁ a₂
theorem mk_eq_mk (a₁ a₂ : α) : {a₁, a₂} = {a₂, a₁} :=
Quot.sound (Or.inr ⟨rfl, rfl⟩)
private def mem_fn (a : α) : α × α → Prop
 (a₁, a₂) => a = a₁ ∨ a = a₂
 auxiliary lemma for proving mem_respects
private theorem mem_swap {a : α} :
∀ {p : α × α}, mem_fn a p = mem_fn a (⟨p.2, p.1⟩)
 (a₁, a₂) => by
apply propext
apply Iff.intro
. intro
 Or.inl h => exact Or.inr h
 Or.inr h => exact Or.inl h
. intro
 Or.inl h => exact Or.inr h
 Or.inr h => exact Or.inl h
private theorem mem_respects
: {p₁ p₂ : α × α} → (a : α) → p₁ ~ p₂ → mem_fn a p₁ = mem_fn a p₂
 (a₁, a₂), (b₁, b₂), a, Or.inl ⟨a₁b₁, a₂b₂⟩ => by simp_all
 (a₁, a₂), (b₁, b₂), a, Or.inr ⟨a₁b₂, a₂b₁⟩ => by simp_all; apply mem_swap
def mem (a : α) (u : UProd α) : Prop :=
Quot.liftOn u (fun p => mem_fn a p) (fun p₁ p₂ e => mem_respects a e)
infix:50 (priority := high) " ∈ " => mem
theorem mem_mk_left (a b : α) : a ∈ {a, b} :=
Or.inl rfl
theorem mem_mk_right (a b : α) : b ∈ {a, b} :=
Or.inr rfl
theorem mem_or_mem_of_mem_mk {a b c : α} : c ∈ {a, b} → c = a ∨ c = b :=
fun h => h
end UProd
For convenience, the standard library also defines Quotient.lift₂
for lifting binary functions, and Quotient.ind₂
for induction on
two variables.
We close this section with some hints as to why the quotient
construction implies function extensionality. It is not hard to show
that extensional equality on the (x : α) → β x
is an equivalence
relation, and so we can consider the type extfun α β
of functions
"up to equivalence." Of course, application respects that equivalence
in the sense that if f₁
is equivalent to f₂
, then f₁ a
is
equal to f₂ a
. Thus application gives rise to a function
extfun_app : extfun α β → (x : α) → β x
. But for every f
,
extfun_app ⟦f⟧
is definitionally equal to fun x => f x
, which is
in turn definitionally equal to f
. So, when f₁
and f₂
are
extensionally equal, we have the following chain of equalities:
f₁ = extfun_app ⟦f₁⟧ = extfun_app ⟦f₂⟧ = f₂
As a result, f₁
is equal to f₂
.
Choice
To state the final axiom defined in the standard library, we need the
Nonempty
type, which is defined as follows:
namespace Hidden
class inductive Nonempty (α : Sort u) : Prop where
 intro (val : α) : Nonempty α
end Hidden
Because Nonempty α
has type Prop
and its constructor contains data, it can only eliminate to Prop
.
In fact, Nonempty α
is equivalent to ∃ x : α, True
:
example (α : Type u) : Nonempty α ↔ ∃ x : α, True :=
Iff.intro (fun ⟨a⟩ => ⟨a, trivial⟩) (fun ⟨a, h⟩ => ⟨a⟩)
Our axiom of choice is now expressed simply as follows:
namespace Hidden
universe u
axiom choice {α : Sort u} : Nonempty α → α
end Hidden
Given only the assertion h
that α
is nonempty, choice h
magically produces an element of α
. Of course, this blocks any
meaningful computation: by the interpretation of Prop
, h
contains no information at all as to how to find such an element.
This is found in the Classical
namespace, so the full name of the
theorem is Classical.choice
. The choice principle is equivalent to
the principle of indefinite description, which can be expressed with
subtypes as follows:
namespace Hidden
universe u
axiom choice {α : Sort u} : Nonempty α → α
noncomputable def indefiniteDescription {α : Sort u} (p : α → Prop)
(h : ∃ x, p x) : {x // p x} :=
choice < let ⟨x, px⟩ := h; ⟨⟨x, px⟩⟩
end Hidden
Because it depends on choice
, Lean cannot generate bytecode for
indefiniteDescription
, and so requires us to mark the definition
as noncomputable
. Also in the Classical
namespace, the
function choose
and the property choose_spec
decompose the two
parts of the output of indefiniteDescription
:
open Classical
namespace Hidden
noncomputable def choose {α : Sort u} {p : α → Prop} (h : ∃ x, p x) : α :=
(indefiniteDescription p h).val
theorem choose_spec {α : Sort u} {p : α → Prop} (h : ∃ x, p x) : p (choose h) :=
(indefiniteDescription p h).property
end Hidden
The choice
principle also erases the distinction between the
property of being Nonempty
and the more constructive property of
being Inhabited
:
open Classical
theorem inhabited_of_nonempty : Nonempty α → Inhabited α :=
fun h => choice (let ⟨a⟩ := h; ⟨⟨a⟩⟩)
In the next section, we will see that propext
, funext
, and
choice
, taken together, imply the law of the excluded middle and
the decidability of all propositions. Using those, one can strengthen
the principle of indefinite description as follows:
open Classical
universe u
#check (@strongIndefiniteDescription :
{α : Sort u} → (p : α → Prop)
→ Nonempty α → {x // (∃ (y : α), p y) → p x})
Assuming the ambient type α
is nonempty,
strongIndefiniteDescription p
produces an element of α
satisfying p
if there is one. The data component of this
definition is conventionally known as Hilbert's epsilon function:
open Classical
universe u
#check (@epsilon :
{α : Sort u} → [Nonempty α]
→ (α → Prop) → α)
#check (@epsilon_spec :
∀ {α : Sort u} {p : α → Prop} (hex : ∃ (y : α), p y),
p (@epsilon _ (nonempty_of_exists hex) p))
The Law of the Excluded Middle
The law of the excluded middle is the following
open Classical
#check (@em : ∀ (p : Prop), p ∨ ¬p)
Diaconescu's theorem states
that the axiom of choice is sufficient to derive the law of excluded
middle. More precisely, it shows that the law of the excluded middle
follows from Classical.choice
, propext
, and funext
. We
sketch the proof that is found in the standard library.
First, we import the necessary axioms, and define two predicates U
and V
:
namespace Hidden
open Classical
theorem em (p : Prop) : p ∨ ¬p :=
let U (x : Prop) : Prop := x = True ∨ p
let V (x : Prop) : Prop := x = False ∨ p
have exU : ∃ x, U x := ⟨True, Or.inl rfl⟩
have exV : ∃ x, V x := ⟨False, Or.inl rfl⟩
sorry
end Hidden
If p
is true, then every element of Prop
is in both U
and V
.
If p
is false, then U
is the singleton true
, and V
is the singleton false
.
Next, we use some
to choose an element from each of U
and V
:
namespace Hidden
open Classical
theorem em (p : Prop) : p ∨ ¬p :=
let U (x : Prop) : Prop := x = True ∨ p
let V (x : Prop) : Prop := x = False ∨ p
have exU : ∃ x, U x := ⟨True, Or.inl rfl⟩
have exV : ∃ x, V x := ⟨False, Or.inl rfl⟩
let u : Prop := choose exU
let v : Prop := choose exV
have u_def : U u := choose_spec exU
have v_def : V v := choose_spec exV
sorry
end Hidden
Each of U
and V
is a disjunction, so u_def
and v_def
represent four cases. In one of these cases, u = True
and
v = False
, and in all the other cases, p
is true. Thus we have:
namespace Hidden
open Classical
theorem em (p : Prop) : p ∨ ¬p :=
let U (x : Prop) : Prop := x = True ∨ p
let V (x : Prop) : Prop := x = False ∨ p
have exU : ∃ x, U x := ⟨True, Or.inl rfl⟩
have exV : ∃ x, V x := ⟨False, Or.inl rfl⟩
let u : Prop := choose exU
let v : Prop := choose exV
have u_def : U u := choose_spec exU
have v_def : V v := choose_spec exV
have not_uv_or_p : u ≠ v ∨ p :=
match u_def, v_def with
 Or.inr h, _ => Or.inr h
 _, Or.inr h => Or.inr h
 Or.inl hut, Or.inl hvf =>
have hne : u ≠ v := by simp [hvf, hut, true_ne_false]
Or.inl hne
sorry
end Hidden
On the other hand, if p
is true, then, by function extensionality
and propositional extensionality, U
and V
are equal. By the
definition of u
and v
, this implies that they are equal as well.
namespace Hidden
open Classical
theorem em (p : Prop) : p ∨ ¬p :=
let U (x : Prop) : Prop := x = True ∨ p
let V (x : Prop) : Prop := x = False ∨ p
have exU : ∃ x, U x := ⟨True, Or.inl rfl⟩
have exV : ∃ x, V x := ⟨False, Or.inl rfl⟩
let u : Prop := choose exU
let v : Prop := choose exV
have u_def : U u := choose_spec exU
have v_def : V v := choose_spec exV
have not_uv_or_p : u ≠ v ∨ p :=
match u_def, v_def with
 Or.inr h, _ => Or.inr h
 _, Or.inr h => Or.inr h
 Or.inl hut, Or.inl hvf =>
have hne : u ≠ v := by simp [hvf, hut, true_ne_false]
Or.inl hne
have p_implies_uv : p → u = v :=
fun hp =>
have hpred : U = V :=
funext fun x =>
have hl : (x = True ∨ p) → (x = False ∨ p) :=
fun _ => Or.inr hp
have hr : (x = False ∨ p) → (x = True ∨ p) :=
fun _ => Or.inr hp
show (x = True ∨ p) = (x = False ∨ p) from
propext (Iff.intro hl hr)
have h₀ : ∀ exU exV, @choose _ U exU = @choose _ V exV := by
rw [hpred]; intros; rfl
show u = v from h₀ _ _
sorry
end Hidden
Putting these last two facts together yields the desired conclusion:
namespace Hidden
open Classical
theorem em (p : Prop) : p ∨ ¬p :=
let U (x : Prop) : Prop := x = True ∨ p
let V (x : Prop) : Prop := x = False ∨ p
have exU : ∃ x, U x := ⟨True, Or.inl rfl⟩
have exV : ∃ x, V x := ⟨False, Or.inl rfl⟩
let u : Prop := choose exU
let v : Prop := choose exV
have u_def : U u := choose_spec exU
have v_def : V v := choose_spec exV
have not_uv_or_p : u ≠ v ∨ p :=
match u_def, v_def with
 Or.inr h, _ => Or.inr h
 _, Or.inr h => Or.inr h
 Or.inl hut, Or.inl hvf =>
have hne : u ≠ v := by simp [hvf, hut, true_ne_false]
Or.inl hne
have p_implies_uv : p → u = v :=
fun hp =>
have hpred : U = V :=
funext fun x =>
have hl : (x = True ∨ p) → (x = False ∨ p) :=
fun _ => Or.inr hp
have hr : (x = False ∨ p) → (x = True ∨ p) :=
fun _ => Or.inr hp
show (x = True ∨ p) = (x = False ∨ p) from
propext (Iff.intro hl hr)
have h₀ : ∀ exU exV, @choose _ U exU = @choose _ V exV := by
rw [hpred]; intros; rfl
show u = v from h₀ _ _
match not_uv_or_p with
 Or.inl hne => Or.inr (mt p_implies_uv hne)
 Or.inr h => Or.inl h
end Hidden
Consequences of excluded middle include doublenegation elimination, proof by cases, and proof by contradiction, all of which are described in the Section Classical Logic. The law of the excluded middle and propositional extensionality imply propositional completeness:
namespace Hidden
open Classical
theorem propComplete (a : Prop) : a = True ∨ a = False :=
match em a with
 Or.inl ha => Or.inl (propext (Iff.intro (fun _ => ⟨⟩) (fun _ => ha)))
 Or.inr hn => Or.inr (propext (Iff.intro (fun h => hn h) (fun h => False.elim h)))
end Hidden
Together with choice, we also get the stronger principle that every
proposition is decidable. Recall that the class of Decidable
propositions is defined as follows:
namespace Hidden
class inductive Decidable (p : Prop) where
 isFalse (h : ¬p) : Decidable p
 isTrue (h : p) : Decidable p
end Hidden
In contrast to p ∨ ¬ p
, which can only eliminate to Prop
, the
type Decidable p
is equivalent to the sum type Sum p (¬ p)
, which
can eliminate to any type. It is this data that is needed to write an
ifthenelse expression.
As an example of classical reasoning, we use choose
to show that if
f : α → β
is injective and α
is inhabited, then f
has a
left inverse. To define the left inverse linv
, we use a dependent
ifthenelse expression. Recall that if h : c then t else e
is
notation for dite c (fun h : c => t) (fun h : ¬ c => e)
. In the definition
of linv
, choice is used twice: first, to show that
(∃ a : A, f a = b)
is "decidable," and then to choose an a
such that
f a = b
. Notice that propDecidable
is a scoped instance and is activated
by the open Classical
command. We use this instance to justify
the ifthenelse expression. (See also the discussion in
Section Decidable Propositions).
open Classical
noncomputable def linv [Inhabited α] (f : α → β) : β → α :=
fun b : β => if ex : (∃ a : α, f a = b) then choose ex else default
theorem linv_comp_self {f : α → β} [Inhabited α]
(inj : ∀ {a b}, f a = f b → a = b)
: linv f ∘ f = id :=
funext fun a =>
have ex : ∃ a₁ : α, f a₁ = f a := ⟨a, rfl⟩
have feq : f (choose ex) = f a := choose_spec ex
calc linv f (f a)
_ = choose ex := dif_pos ex
_ = a := inj feq
From a classical point of view, linv
is a function. From a
constructive point of view, it is unacceptable; because there is no
way to implement such a function in general, the construction is not
informative.