lean2/doc/lean/tutorial.org

• Lean Tutorial
  • Introduction
  • Getting started
  • Basics
  • Propositional logic
  • Functions
  • Definitional equality
  • Provable equality
  • Proving
    • And (conjunction)
    • (disjunction)
    • Not (negation)
    • Iff (if-and-only-if)
    • True and False
    • Rewrite rules
  • Dependent functions and quantifiers

Lean Tutorial

Introduction

Lean is an automatic and interactive theorem prover. It can be used to create specifications, build mathematical libraries, and solve constraints. In this tutorial, we introduce basic concepts, the logic used in Lean, and the main commands.

Getting started

We can use Lean in interactive or batch mode. The following example just displays the message `hello world`.

  print "hello world"

All we have to do to run your first example is to call the lean executable with the name of the text file that contains the command above. If you saved the above command in the file hello.lean, then you just have to execute

  lean hello.lean

As a more complex example, the next example defines a function that doubles the input value.

  import data.nat
  open nat
  -- defines the double function
  definition double (x : nat) := x + x

Basics

We can also view Lean as a suite of tools for evaluating and processing expressions representing terms, definitions, and theorems.

Every expression has a unique type in Lean. The command check returns the type of a given expression.

  import logic
  check true
  check and

The last command returns Prop → Prop → Prop. That is, the type of and is a function that takes two propositions and return a proposition, Prop is the type of propositions.

The command import loads existing libraries and extensions.

  import data.nat
  check nat.ge

We say nat.ge is a hierarchical name comprised of two parts: nat and ge.

The command open creates aliases based on a given prefix. The command also imports notation, hints, and other features. We will discuss its other applications later. Regarding aliases, the following command creates aliases for all objects starting with nat, and imports all notations defined in this namespace.

  import data.nat
  open nat
  check ge -- display the type of nat.ge

The command constant assigns a type to an identifier. The following command postulates/assumes that n, m and o have type nat.

  import data.nat
  open nat
  constant n : nat
  constant m : nat
  constant o : nat
  -- The command 'open nat' also imported the notation defined at the namespace 'nat'
  check n + m
  check n ≤ m

The command constants n m o : nat can be used as a shorthand for the three commands above.

In Lean, proofs are also expressions, and all functionality provided for manipulating expressions is also available for manipulating proofs. For example, eq.refl n is a proof for n = n. In Lean, eq.refl is the reflexivity theorem.

  import data.nat
  open nat
  constant n : nat
  check eq.refl n

The command axiom postulates that a given proposition holds. The following commands postulate two axioms Ax1 and Ax2 that state that n = m and m = o. Ax1 and Ax2 are not just names. For example, eq.trans Ax1 Ax2 is a proof that n = o, where eq.trans is the transitivity theorem.

  import data.nat
  open nat
  constants m n o : nat
  axiom Ax1 : n = m
  axiom Ax2 : m = o
  check eq.trans Ax1 Ax2

The expression eq.trans Ax1 Ax2 is just a function application like any other. Moreover, in Lean, propositions are types. Any proposition P can be used as a type. The elements of type P can be viewed as the proofs of P. Moreover, in Lean, proof checking is type checking. For example, the Lean type checker will reject the type incorrect term eq.trans Ax2 Ax1.

Because we use proposition as types, we must support empty types. For example, the type false must be empty, since we don't have a proof for false.

Most systems based on the propositions as types paradigm are based on constructive logic. In Lean, we support classical and constructive logic. We can load classical axiom by using import classical. When the classical extensions are loaded, the excluded middle is a theorem, and em p is a proof for p ∨ ¬ p.

  import logic.axioms.classical
  constant p : Prop
  check em p

The commands axiom and constant are essentially the same command. We provide both just to make Lean files more readable. We encourage users to use axiom only for propositions, and constant for everything else.

Similarly, a theorem is just a definition. The following command defines a new theorem called nat_trans3, and then use it to prove something else. In this example, eq.symm is the symmetry theorem.

  import data.nat
  open nat

  theorem nat_trans3 (a b c d : nat) (H1 : a = b) (H2 : c = b) (H3 : c = d) : a = d :=
  eq.trans (eq.trans H1 (eq.symm H2)) H3

  -- Example using nat_trans3
  constants x y z w : nat
  axiom Hxy : x = y
  axiom Hzy : z = y
  axiom Hzw : z = w
  check nat_trans3 x y z w Hxy Hzy Hzw

The theorem nat_trans3 has 7 parameters, it takes for natural numbers a, b, c and d, and three proofs showing that a = b, c = b and c = d, and returns a proof that a = d.

The theorem nat_trans3 is somewhat inconvenient to use because it has 7 parameters. However, the first four parameters can be inferred from the last 3. We can use _ as a placeholder that instructs Lean to synthesize this expression. The synthesis process is based on type inference, and it is the most basic form of automation provided by Lean. In the example above, we can use check nat_trans3 _ _ _ _ Hxy Hzy Hzw.

Lean also supports implicit arguments. We mark implicit arguments using curly braces instead of parenthesis. In the following example, we define the theorem nat_trans3i using implicit arguments.

  import data.nat
  open nat

  theorem nat_trans3i {a b c d : nat} (H1 : a = b) (H2 : c = b) (H3 : c = d) : a = d :=
  eq.trans (eq.trans H1 (eq.symm H2)) H3

  -- Example using nat_trans3
  constants x y z w : nat
  axiom Hxy : x = y
  axiom Hzy : z = y
  axiom Hzw : z = w
  check nat_trans3i Hxy Hzy Hzw

It is identical to nat_trans3, the only difference is the use of curly braces. Lean will (try to) infer the implicit arguments. The idea behind implicit arguments is quite simple, we are just instructing Lean to automatically insert the placeholders _ for us.

Sometimes, Lean will not be able to infer the parameters automatically. The annotation @f instructs Lean that we want to provide the implicit arguments for f explicitly. The theorems eq.refl, eq.trans and eq.symm all have implicit arguments.

  import logic
  check @eq.refl
  check @eq.symm
  check @eq.trans

We can also instruct Lean to display all implicit arguments when it prints expressions. This is useful when debugging non-trivial problems.

  import data.nat
  open nat

  constants a b c : nat
  axiom H1 : a = b
  axiom H2 : b = c
  check eq.trans H1 H2

  set_option pp.implicit true
  -- Now, Lean will display all implicit arguments
  check eq.trans H1 H2

In the previous example, the check command stated that eq.trans H1 H2 has type @eq ℕ a c. The expression a = c is just notational convenience.

We have seen many occurrences of Type. In Lean, the type of nat and Prop is Type. What is the type of Type?

  check Type

Lean reports Type : Type, is it Lean inconsistent? Now, it is not. Internally, Lean maintains a hierarchy of Types. We say each one of them lives in a universe. Lean is universe polymorphic, and by default all universes are hidden from the user. Like implicit arguments, we can instruct Lean to display the universe levels explicitly.

  set_option pp.universes true
  check Type

In the command above, Lean reports that Type.{l_1} that lives in universe l_1 has type Type.{succ l_1}. That is, its type lives in the universe l_1 + 1.

Definitions such as eq.refl, eq.symm and eq.trans are all universe polymorphic.

  import logic
  set_option pp.universes true
  check @eq.refl
  check @eq.symm
  check @eq.trans

Whenever we declare a new constant, Lean automatically infers the universe parameters. We can also provide the universe levels explicitly.

  import logic

  definition id.{l} {A : Type.{l}} (a : A) : A := a

  check id true

The universes can be explicitly provided for each constant and Type by using the notation .{ ... }. Unlike other systems, Lean does not have universe cumulativity. That is, the type Type.{i} is not an element of Type.{j} for j > i.

Propositional logic

To manipulate formulas with a richer logical structure, it is important to master the notation Lean uses for building composite logical expressions out of basic formulas using logical connectives. The logical connectives (and, or, not, etc) are defined in the file logic.lean. This file also defines notational convention for writing formulas in a natural way. Here is a table showing the notation for the so called propositional (or Boolean) connectives.

Ascii	Unicode	Definition
true		true
false		false
not	¬	not
/\	∧	and
‌\/	∨	or
->	→
<->	↔	iff

true and false are logical constants to denote the true and false propositions. Logical negation is a unary operator just like arithmetical negation on numbers. The other connectives are all binary operators. The meaning of the operators is the usual one. The table above makes clear that Lean supports unicode characters. We can use Ascii or/and unicode versions. Here is a simple example using the connectives above.

  import logic
  constants p q : Prop
  check p → q → p ∧ q
  check ¬p → p ↔ false
  check p ∨ q → q ∨ p
  -- Ascii version
  check p -> q -> p /\ q
  check not p -> p <-> false
  check p \/ q -> q \/ p

Depending on the platform, Lean uses unicode characters by default when printing expressions. The following commands can be used to change this behavior.

  import logic
  set_option pp.unicode false
  constants p q : Prop
  check p → q → p ∧ q
  set_option pp.unicode true
  check p → q → p ∧ q

Note that, it may seem that the symbols -> and → are overloaded, and Lean uses them to represent implication and the type of functions. Actually, they are not overloaded, they are the same symbols. In Lean, the Proposition p → q expression is also the type of the functions that given a proof for p, returns a proof for q. This is very convenient for writing proofs.

  import logic
  constants p q : Prop
  -- Hpq is a function that takes a proof for p and returns a proof for q
  axiom Hpq : p → q
  -- Hq is a proof/certificate for p
  axiom Hp  : p
  -- The expression Hpq Hp is a proof/certificate for q
  check Hpq Hp

In composite expressions, the precedences of the various binary connectives are in order of the above table, with and being the strongest and iff the weakest. For example, a ∧ b → c ∨ d ∧ e means (a ∧ b) → (c ∨ (d ∧ e)). All of them are right-associative. So, p ∧ q ∧ r means p ∧ (q ∧ r). The actual precedence and fixity of all logical connectives is defined in the Lean logic definition file. Finally, not, and, or and iff are the actual names used when defining the Boolean connectives. They can be used as any other function. Lean supports currying and true is a function from Prop to Prop.

Functions

There are many variable-binding constructs in mathematics. Lean expresses all of them using just one abstraction, which is a converse operation to function application. Given a variable x, a type A, and a term t that may or may not contain x, one can construct the so-called lambda abstraction fun x : A, t, or using unicode notation λ x : A, t. Here is some simple examples.

  import data.nat
  open nat

  check fun x : nat, x + 1
  check fun x y : nat, x + 2 * y
  check fun x y : Prop, not (x ∧ y)
  check λ x : nat, x + 1
  check λ (x : nat) (p : Prop), x = 0 ∨ p

In many cases, Lean can automatically infer the type of the variable. Actually, In all examples above, the type can be inferred automatically.

  import data.nat
  open nat

  check fun x, x + 1
  check fun x y, x + 2 * y
  check fun x y, not (x ∧ y)
  check λ x, x + 1
  check λ x p, x = 0 ∨ p

However, Lean will complain that it cannot infer the type of the variable x in fun x, x because any type would work in this example.

The following example shows how to use lambda abstractions in function applications

  import data.nat
  open nat
  check (fun x y, x + 2 * y) 1
  check (fun x y, x + 2 * y) 1 2
  check (fun x y, not (x ∧ y)) true false

Lambda abstractions are also used to create proofs for propositions of the form A → B. This should be natural since we can "view" A → B as the type of functions that given a proof for A returns a proof for B. For example, a proof for p → p is just fun H : p, H (the identity function).

  import logic
  constant p : Prop
  check fun H : p, H

Definitional equality

The command eval t computes a normal form for the term t. In Lean, we say two terms are definitionally equal if the have the same normal form. For example, the terms (λ x : nat, x + 1) a and a + 1 are definitionally equal. The Lean type/proof checker uses the normalizer when checking types/proofs. So, we can prove that two definitionally equal terms are equal using just eq.refl. Here is a simple example.

  import data.nat
  open nat

  theorem def_eq_th (a : nat) : ((λ x : nat, x + 1) a) = a + 1 := eq.refl (a+1)

Provable equality

In the previous examples, we have used nat_trans3 x y z w Hxy Hzy Hzw to show that x = w. In this case, x and w are not definitionally equal, but they are provably equal in the environment that contains nat_trans3 and axioms Hxy, Hzy and Hzw.

Proving

The Lean standard library contains basic theorems for creating proof terms. The basic theorems are useful for creating manual proofs. The are also the basic building blocks used by all automated proof engines available in Lean. The theorems can be broken into three different categories: introduction, elimination, and rewriting. First, we cover the introduction and elimination theorems for the basic Boolean connectives.

And (conjunction)

The expression and.intro H1 H2 creates a proof for a ∧ b using proofs H1 : a and H2 : b. We say and.intro is the and-introduction operation. In the following example we use and.intro for creating a proof for p → q → p ∧ q.

  import logic
  constants p q : Prop
  check fun (Hp : p) (Hq : q), and.intro Hp Hq

The expression and.elim_left H creates a proof a from a proof H : a ∧ b. Similarly and.elim_right H is a proof for b. We say they are the left/right and-eliminators.

  import logic
  constants p q : Prop
  -- Proof for p ∧ q → p
  check fun H : p ∧ q, and.elim_left H
  -- Proof for p ∧ q → q
  check fun H : p ∧ q, and.elim_right H

Now, we prove p ∧ q → q ∧ p with the following simple proof term.

  import logic
  constants p q : Prop
  check fun H : p ∧ q, and.intro (and.elim_right H) (and.elim_left H)

Note that the proof term is very similar to a function that just swaps the elements of a pair.

(disjunction)

The expression or.intro_left b H1 creates a proof for a ∨ b using a proof H1 : a. Similarly, or.intro_right a H2 creates a proof for a ∨ b using a proof H2 : b. We say they are the left/right or-introduction.

  import logic
  constants p q : Prop
  -- Proof for p → p ∨ q
  check fun H : p, or.intro_left q H
  -- Proof for q → p ∨ q
  check fun H : q, or.intro_right p H

The or-elimination rule is slightly more complicated. The basic idea is the following, we can prove c from a ∨ b, by showing we can prove c by assuming a or by assuming b. It is essentially a proof by cases. or.elim Hab Hac Hbc takes three arguments Hab : a ∨ b, Hac : a → c and Hbc : b → c and produces a proof for c. In the following example, we use or.elim to prove that p v q → q ∨ p.

  import logic
  constants p q : Prop
  check fun H : p ∨ q,
           or.elim H
              (fun Hp : p, or.intro_right q Hp)
              (fun Hq : q, 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. Moreover, Lean provides or.inr and or.inl as shorthands for or.intro_right _ and or.intro_left _. These two shorthands are extensively used in the Lean standard library.

  import logic
  constants p q : Prop
  check fun H : p ∨ q,
           or.elim H
              (fun Hp : p, or.inr Hp)
              (fun Hq : q, or.inl Hq)

Not (negation)

not_intro H produces a proof for ¬ a from H : a → false. That is, we obtain ¬ a if we can derive false from a. The expression absurd Ha Hna produces a proof for some b from Ha : a and Hna : ¬ a. That is, we can deduce anything if we have a and ¬ a. We now use not_intro and absurd to produce a proof term for (a → b) → ¬b → ¬a.

  import logic
  constants a b : Prop
  check fun (Hab : a → b) (Hnb : ¬ b),
            not.intro (fun Ha : a, absurd (Hab Ha) Hnb)

In the standard library, not a is actually just an abbreviation for a → false. Thus, we don't really need to use not_intro explicitly.

  import logic
  constants a b : Prop
  check fun (Hab : a → b) (Hnb : ¬ b),
            (fun Ha : a, Hnb (Hab Ha))

Now, here is the proof term for ¬a → b → (b → a) → c

  import logic
  constants a b c : Prop
  check fun (Hna : ¬ a) (Hb : b) (Hba : b → a),
            absurd (Hba Hb) Hna

Iff (if-and-only-if)

The expression iff.intro H1 H2 produces a proof for a ↔ b from H1 : a → b and H2 : b → a. iff.elim_left H produces a proof for a → b from H : a ↔ b. Similarly, iff.elim_right H produces a proof for b → a from H : a ↔ b. Here is the proof term for a ∧ b ↔ b ∧ a

  import logic
  constants a b : Prop
  check iff.intro
          (fun H : a ∧ b, and.intro (and.elim_right H) (and.elim_left H))
          (fun H : b ∧ a, and.intro (and.elim_right H) (and.elim_left H))

In Lean, we can use assume instead of fun to make proof terms look more like proofs found in text books.

  import logic
  constants a b : Prop
  check iff.intro
          (assume H : a ∧ b, and.intro (and.elim_right H) (and.elim_left H))
          (assume H : b ∧ a, and.intro (and.elim_right H) (and.elim_left H))

True and False

The expression trivial is a proof term for true, and false_elim a H produces a proof for a from H : false.

Rewrite rules

WARNING: We did not port this section to Lean 0.2 yet

The Lean kernel also contains many theorems that are meant to be used as rewriting/simplification rules. The conclusion of these theorems is of the form t = s or t ↔ s. For example, and_id a is proof term for a ∧ a ↔ a. The Lean simplifier can use these theorems to automatically create proof terms for us. The expression (by simp [rule-set]) is similar to _, but it tells Lean to synthesize the proof term using the simplifier using the rewrite rule set named [rule-set]. In the following example, we create a simple rewrite rule set and use it to prove a theorem that would be quite tedious to prove by hand.

  -- import module that defines several tactics/strategies including "simp"
  import tactic
  -- create a rewrite rule set with name 'simple'
  rewrite_set simple
  -- add some theorems to the rewrite rule set 'simple'
  add_rewrite and_id and_truer and_truel and_comm and.assoc and_left_comm iff_id : simple
  theorem th1 (a b : Bool) : a ∧ b ∧ true ∧ b ∧ true ∧ b ↔ a ∧ b
  := (by simp simple)

In Lean, we can combine manual and automated proofs in a natural way. We can manually write the proof skeleton and use the by construct to invoke automated proof engines like the simplifier for filling the tedious steps. Here is a very simple example.

   theorem th2 (a b : Prop) : a ∧ b ↔ b ∧ a
   := iff.intro
        (fun H : a ∧ b, (by simp simple))
        (fun H : b ∧ a, (by simp simple))

Dependent functions and quantifiers

Lean supports dependent functions. In type theory, they are also called dependent product types or Pi-types. The idea is quite simple, suppose we have a type A : Type, and a family of types B : A → Type which assigns to each a : A a type B a. So a dependent function is a function whose range varies depending on its arguments. In Lean, the dependent functions is written as forall a : A, B a, Pi a : A, B a, ∀ x : A, B a, or Π x : A, B a. We usually use forall and ∀ for propositions, and Pi and Π for everything else. In the previous examples, we have seen many examples of dependent functions. The theorems eq.refl, eq.trans and eq.symm, and the equality are all dependent functions.

The universal quantifier is just a dependent function. In Lean, if we have a family of types B : A → Prop, then ∀ x : A, B a has type Prop. This features complicates the Lean set-theoretic model, but it improves usability. Several theorem provers have a forall elimination (aka instantiation) proof rule. In Lean (and other systems based on proposition as types), this rule is just function application. In the following example we add an axiom stating that f x is 0 forall x. Then we instantiate the axiom using function application.

  import data.nat
  open nat

  constant f : nat → nat
  axiom fzero : ∀ x, f x = 0
  check fzero 1
  constant a : nat
  check fzero a

Since we instantiate quantifiers using function application, it is natural to create proof terms for universal quantifiers using lambda abstraction. In the following example, we create a proof term showing that for all x and y, f x = f y.

  import data.nat
  open nat

  constant f : nat → nat
  axiom fzero : ∀ x, f x = 0
  check λ x y, eq.trans (fzero x) (eq.symm (fzero y))

We can view the proof term above as a simple function or "recipe" for showing that f x = f y for any x and y. The function "invokes" fzero for creating proof terms for f x = 0 and f y = 0. Then, it uses symmetry eq.symm to create a proof term for 0 = f y. Finally, transitivity is used to combine the proofs for f x = 0 and 0 = f y.

In Lean, the existential quantifier can be written as exists x : A, B x or ∃ x : A, B x. Actually both versions are just notational convenience for Exists (fun x : A, B x). That is, the existential quantifier is actually a constant defined in the file logic.lean. This file also defines the exists_intro and exists_elim. To build a proof for ∃ x : A, B x, we should provide a term w : A and a proof term Hw : B w to exists_intro. We say w is the witness for the existential introduction. In previous examples, nat_trans3i Hxy Hzy Hzw was a proof term for x = w. Then, we can create a proof term for ∃ a : nat, a = w using

  import data.nat
  open nat

  theorem nat_trans3i {a b c d : nat} (H1 : a = b) (H2 : c = b) (H3 : c = d) : a = d :=
  eq.trans (eq.trans H1 (eq.symm H2)) H3

  constants x y z w : nat
  axiom Hxy : x = y
  axiom Hzy : z = y
  axiom Hzw : z = w

  theorem ex_a_eq_w : exists a, a = w  := exists_intro x (nat_trans3i Hxy Hzy Hzw)
  check ex_a_eq_w

Note that exists_intro also has implicit arguments. For example, Lean has to infer the implicit argument P : A → Bool, a predicate (aka function to Prop). This creates complications. For example, suppose we have Hg : g 0 0 = 0 and we invoke exists_intro 0 Hg. There are different possible values for P. Each possible value corresponds to a different theorem: ∃ x, g x x = x, ∃ x, g x x = 0, ∃ x, g x 0 = x, etc. Lean uses the context where exists_intro occurs to infer the users intent. In the example above, we were trying to prove the theorem ∃ a, a = w. So, we are implicitly telling Lean how to choose P. In the following example, we demonstrate this issue. We ask Lean to display the implicit arguments using the option pp.implicit. We see that each instance of exists_intro 0 Hg has different values for the implicit argument P.

  import data.nat
  open nat

  check @exists_intro
  constant g : nat → nat → nat
  axiom Hg : g 0 0 = 0
  theorem gex1 : ∃ x, g x x = x := exists_intro 0 Hg
  theorem gex2 : ∃ x, g x 0 = x := exists_intro 0 Hg
  theorem gex3 : ∃ x, g 0 0 = x := exists_intro 0 Hg
  theorem gex4 : ∃ x, g x x = 0 := exists_intro 0 Hg
  set_option pp.implicit true  -- display implicit arguments
  check gex1
  check gex2
  check gex3
  check gex4

We can view exists_intro (aka existential introduction) as an information hiding procedure. We are "hiding" what is the witness for some fact. The existential elimination performs the opposite operation. The exists_elim theorem allows us to prove some proposition B from ∃ x : A, B x if we can derive B using an "abstract" witness w and a proof term Hw : B w.

  import logic
   check @exists_elim

In the following example, we define even a as ∃ b, a = 2*b, and then we show that the sum of two even numbers is an even number.

  import data.nat
  open nat

  definition even (a : nat) := ∃ b, a = 2*b
  theorem EvenPlusEven {a b : nat} (H1 : even a) (H2 : even b) : even (a + b) :=
  exists_elim H1 (fun (w1 : nat) (Hw1 : a = 2*w1),
  exists_elim H2 (fun (w2 : nat) (Hw2 : b = 2*w2),
    exists_intro (w1 + w2)
      (calc a + b  =  2*w1 + b      : {Hw1}
              ...  =  2*w1 + 2*w2   : {Hw2}
              ...  =  2*(w1 + w2)   : eq.symm !mul.distr_left)))

The example above also uses calculational proofs to show that a + b = 2*(w1 + w2). The calc construct is just syntax sugar for creating proofs using transitivity and substitution.

In Lean, we can use obtain _, from _, _ as syntax sugar for exists_elim. With this macro we can write the example above in a more natural way

  import data.nat
  open nat
  definition even (a : nat) := ∃ b, a = 2*b
  theorem EvenPlusEven {a b : nat} (H1 : even a) (H2 : even b) : even (a + b) :=
  obtain (w1 : nat) (Hw1 : a = 2*w1), from H1,
  obtain (w2 : nat) (Hw2 : b = 2*w2), from H2,
    exists_intro (w1 + w2)
      (calc a + b  =  2*w1 + b      : {Hw1}
              ...  =  2*w1 + 2*w2   : {Hw2}
              ...  =  2*(w1 + w2)   : eq.symm !mul.distr_left)