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 propositions-as-types 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
• well-founded 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 non-constructors 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 right-hand side of the => are subterms of the patterns on the left-hand 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 course-of-values 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 course-of-values 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 course-of-values 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]

Well-Founded Recursion and Induction

When structural recursion cannot be used, we can prove termination using well-founded recursion. We need a well-founded 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 well-founded 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 well-founded relation on a type α, we should have a principle of well-founded 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 well-founded 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 left-hand-side 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 well-founded 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 well-founded 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 well-founded relation is derived (if possible) by selecting an argument and then using typeclass resolution to synthesize a well-founded relation for this argument's type.

• If termination_by is specified, it maps the arguments of the function to a type α and type class resolution is again used. Recall that, the default instance for β × γ is a lexicographic order based on the well-founded relations for β and γ.

• The default well-founded 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 well-founded relation. If decreasing_tactic fails, the error message includes the remaining goal ... |- G. Note that, the decreasing_tactic uses assumption. So, you can include a have-expression to prove goal G. You can also provide your own tactic using decreasing_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 well-founded 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 left-hand 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 pattern-matching 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 match-with 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 pattern-matching 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

1. 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.

2. 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 that reverse (reverse xs) = xs for any list xs).

3. Define your own function to carry out course-of-value recursion on the natural numbers. Similarly, see if you can figure out how to define WellFounded.fix on your own.

4. 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.

5. Consider the following type of arithmetic expressions. The idea is that var n is a variable, vₙ, and const n is the constant whose value is 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))

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.