# 2. Types and Terms¶

Lean’s foundational framework is a version of a logical system known as the Calculus of Inductive Constructions, or CIC. Programming in Lean amounts to writing down expressions in the system, and then evaluating them. You should keep in mind that, as a full-blown foundation for mathematics, the CIC is much more than a programming language. One can define all kinds of mathematical objects: number systems, ranging from the natural numbers to the complex numbers; algebraic structures, from semigroups to categories and modules over an arbitrary ring; limits, derivatives, and integrals, and other components of real and complex analysis; vector spaces and matrices; measure spaces; and much more. This provides an environment in which one can define data types alongside other mathematical objects, and write programs alongside mathematical proofs.

Terms in the Calculus of Inductive Constructions are therefore used to represent mathematical objects, programs, data types, assertions, and proofs. In the CIC, every term has a type, which indicates what sort of object it and how it behaves computationally. This chapter is a quick tour of some of the terms we can write in Lean. For a more detailed and exhaustive account, see Theorem Proving in Lean.

## 2.1. Some Basic Types¶

In Lean:

• #check can be used a check the type of an expression.
• #print can be used to print information about an identifier, for example, the definition of a defined constant.
• #reduce can be used to normalize a symbolic expression.
• #eval can be used to run the bytecode-block evaluator on any closed term that has a computational interpretation.

Lean’s standard library defines a number of data types, such as nat, int, list, and bool.

#check nat
#print nat

#check int
#print int

#check list
#print list

#check bool
#print bool

You can use the unicode-block symbols and for nat and int, respectively. The first can be entered with \N or \nat, and the second can be entered with \Z or \int.

The library includes standard operations on these types:

#check 3 + 6 * 9
#eval 3 + 6 * 9

#check 1 :: 2 :: 3 :: [4, 5] ++ [6, 7, 8]
#eval 1 :: 2 :: 3 :: [4, 5] ++ [6, 7, 8]

#check tt && (ff || tt)
#eval tt && (ff || tt)

By default, a numeral denotes a natural number. You can always specify an intended type t for an expression e by writing (e : t). In that case, Lean does its best to interpret the expression as an object of the given type, and raises an error if it does not succeed.

#check (3 : )
#check (3 : ) + 6 * 9
#check (3 + 6 * 9 : )

#eval (3 + 6 * 9 : )

We can also declare variables ranging over elements and types.

variables m n k :
variables u v w :
variable  α : Type
variables l₁ l₂ : list
variables s₁ s₂ : list α
variable  a : α

#check m + n * k
#check u + v * w
#check m :: l₁ ++ l₂
#check s₁ ++ a :: s₂

The standard library adopts the convention of using the Greek letters α, β, and γ to range over types. You can type these with \a, \b, and \g, respectively. You can type subscripts with \0, \1, \2, and so on.

Lean will insert coercions automatically:

#check v + m

The presence of a coercion is indicated by Lean’s output, v + ↑m : . Since Lean infers types sequentially as it processes an expression, you need to indicate the coercion manually if you write the arguments in the other order:

#check m + v

You can type the up arrow by writing \u. This is notation for a generic coercion function, and Lean finds the appropriate one using type classes, as described below. The notations +, *, ++ similarly denote functions defined generically on any type that supports the relevant operations:

#check @has_mul.mul
#print has_mul.mul

#check @append
#print append

Here, the @ symbol before the name of the function indicates that Lean should display arguments that are usually left implicit. These are called, unsurprisingly, implicit arguments. In the examples above, type class resolution finds the relevant operations, which are declared in the relevant namespaces.

#check nat.mul
#check list.append
#check list.cons

When generic functions and notations are available, however, it is usually better to use them, because Lean’s automation is designed to work well with generic functions and facts. Incidentally, when infix notation is defined for a binary operation, Lean’s parser will let you put the notation in parentheses to refer to the operation in prefix form:

#check (+)
#check (*)
#check ()

Lean knows about Cartesian products and pairs:

variables α β : Type
variables (a₁ a₂ : α) (b : β) (n : )
variables (p : α × β) (q : α × )

#check α × β
#check (a₁, a₂)
#check (n, b)
#check p.1
#check p.2

#reduce (n, b).1
#reduce (2, 3).1
#eval (2, 3).1

It interprets tuples as iterated products, associated to the right:

variables α β : Type
variables (a₁ a₂ : α) (b : β) (n : )

#check (n, a₁, b)
#reduce (n, a₁, b).2
#reduce (n, a₁, b).2.2

Lean also knows about subtypes and option types, which are described in the next chapter.

## 2.2. Defining Functions¶

In Lean, one can define a new constant with the definition command, which can be abbreviated to def.

definition foo :  := 3

def bar :  := 2 + 2

As with the #check command, Lean first attempts to elaborate the given expression, which is to say, fill in all the information that is left implicit. After that, it checks to make sure that the expression has the stated type. Assuming it succeeds, it creates a new constant with the given name and type, associates it to the expression after the :=, and stores it in the environment.

The type of functions from α to β is denoted α β. We have already seen that a function f is applied to an element x in the domain type by writing f x.

variables α β : Type
variables (a₁ a₂ : α) (b : β) (n : )
variables f :   α
variables g : α  β

#check f n
#check g a₁
#check g a₂ b
#check f (g a₂ b)
#check g (f (g a₂ b))

Conversely, functions are introduced using λ abstraction.

variables (α : Type) (n : ) (i : )

#check λ x : , x + 3
#check λ x, x + 3
#check λ x, x + n
#check λ x, x + i
#check λ x y, x + y + 1
#check λ x : α, x

As the examples make clear, you can leave out the type of the abstracted variable when it can be inferred. The following two definitions mean the same thing:

def foo :    := λ x : , x + 3
def bar := λ x, x + 3

Instead of using a lambda, you can abstract variables by putting them before the colon:

def foo (x y : ) :  := x + y + 3
def bar (x y) := x + y + 3

You can even test a definition without adding it to the environment, using the example command:

example (x y) := x + y + 3

When variables have been declared, functions implicitly depend on the variables mentioned in the definition:

variables (α : Type) (x : α)
variables m n :

def foo := x
def bar := m + n + 3
def baz (k) := m + k + 3

#check foo
#check bar
#check baz

Evaluating expressions involving abstraction and application has the expected behavior:

#reduce (λ x, x + 3) 2
#eval (λ x, x + 3) 2

def foo (x : ) :  := x + 3

#reduce foo 2
#eval foo 2

Both expressions evaluate to 5.

In the CIC, types are just certain kinds of objects, so functions can depend on types. For example, the following defines a polymorphic identity function:

def id (α : Type) (x : α) : α := x

#check id  3
#eval id  3

#check id

Lean indicates that the type of id is Π α : Type, α α. This is an example of a pi type, also known as a dependent function type, since the type of the second argument to id depends on the first.

It is generally redundant to have to give the first argument to id explicitly, since it can be inferred from the second argument. Using curly braces marks the argument as implicit.

def id {α : Type} (x : α) : α := x

#check id 3
#eval id 3

#check id

In case an implicit argument follows the last given argument in a function application, Lean inserts the implicit argument eagerly and tries to infer it. Using double curly braces {{}}, or the unicode-block equivalents obtained with \{{ and \}}, tells the parser to be more conservative about inserting the argument. The difference is illustrated below.

def id₁ {α : Type} (x : α) : α := x
def id₂ α : Type (x : α) : α := x

#check (id₁ :   )
#check (id₂ : Π α : Type, α  α)

In the next section, we will see that Lean supports a hierarchy of type universes, so that the following definition of the identity function is more general:

universe u
def id {α : Type u} (x : α) := x

If you #check @list.append, you will see that, similarly, the append function takes two lists of elements of any type, where the type can occur in any type universe.

Incidentally, subsequent arguments to a dependent function can depend on arbitrary parameters, not just other types:

variable vec :   Type
variable foo : Π {n : }, vec n  vec n
variable v : vec 3

#check foo v

This is precisely the sense in which dependent type theory is dependent.

The CIC also supports recursive definitions on inductively defined types.

open nat

def exp (x : ) :
| 0      := 1
| (succ n) := exp n * (succ n)

We will provide lots of examples of those in the next chapter.

## 2.3. Defining New Types¶

In the version of the Calculus of Inductive Constructions implemented by Lean, we start with a sequence of type universes, Sort 0, Sort 1, Sort 2, Sort 3, … The universe Sort 0 is called Prop and has special properties that we will describe later. Type u is a syntactic sugar for Sort (u+1). For each u, an element t : Type u is itself a type. If you execute the following,

universe u
#check Type u

you will see that each Type u itself has type Type (u+1). The notation Type is shorthand for Type 0, which is a shorthand for Sort 1.

In addition to the type universes, the Calculus of Inductive Constructions provides two means of forming new types:

• pi types
• inductive types

Lean provides an additional means of forming new types:

• quotient types

We discussed pi types in the last section. Quotient types provide a means of defining a new type given a type and an equivalence relation on that type. They are used in the standard library to define multisets, which are represented as lists that are considered the same when one is a permutation of another.

Inductive types are surprisingly useful. The natural numbers are defined inductively:

inductive nat : Type
| zero : nat
| succ : nat  nat

So is the type of lists of elements of a given type α:

universe u

inductive list (α : Type u) : Type u
| nil  : list
| cons : α  list  list

The booleans form an inductive type, as do, indeed, any finitely enumerated type:

inductive bool : Type
| tt : bool
| ff : bool

inductive Beatle : Type
| John   : Beatle
| Paul   : Beatle
| George : Beatle
| Ringo  : Beatle

So are the type of binary trees, and the type of countably branching trees in which every node has children indexed by the type of natural numbers:

inductive binary_tree : Type
| empty : binary_tree
| cons  : binary_tree  binary_tree  binary_tree

inductive nat_tree : Type
| empty : nat_tree
| sup   : (  nat_tree)  nat_tree

What these examples all have in common is that the associated types are built up freely and inductively by the given constructors. For example, we can build some binary trees:

#check binary_tree.empty
#check binary_tree.cons (binary_tree.empty) (binary_tree.empty)

If we open the namespace binary_tree, we can use shorter names:

open binary_tree

#check cons empty (cons (cons empty empty) empty)

In the Lean library, the identifier empty is used as a generic notation for things like the empty set, so opening the binary_tree namespaces means that the constant is overloaded. If you write #check empty, Lean will complain about the overload; you need to say something like #check (empty : binary_tree) to disambiguate.

The inductive command axiomatically declares all of the following:

• A constant, to denote the new type.
• The associated constructors.
• A corresponding eliminator.

The latter gives rise to the principles of recursion and induction that we will encounter in the next two chapters.

We will not give a precise specification of the inductive data types allowed by Lean, but only note here that the description is fairly small and straightforward, and can easily be given a set-theoretic interpretation. Lean also allows mutual inductive types and nested inductive types. As an example, in the definition below, the type under definition appears as a parameter to the list type:

inductive tree (α : Type) : Type
| node : α  list tree  tree

Such definitions are not among Lean’s axoimatic primitives; rather, they are compiled down to more primitive constructions.

## 2.4. Records and Structures¶

When computer scientists bundle data together, they tend to call the result a record. When mathematicians do the same, they call it a structure. Lean uses the keyword structure to introduce inductive definitions with a single constructor.

structure color : Type :=
mk :: (red : ) (green : ) (blue : ) (name : string)

Here, mk is the constructor (if omitted, Lean assumes it is mk by default), and red, green, blue, and name project the four values that are used to construct an element of color.

def purple := color.mk 150 0 150 "purple"

#eval color.red purple
#eval color.green purple
#eval color.blue purple
#eval color.name purple

Because records are so important, Lean provides useful notation for dealing with them. For example, when the type of the record can be inferred, Lean allows the use of anonymous constructors , entered as \< and \>, or the ascii equivalents (| and |). Similarly, one can use the notation .1, .2, and so on for the projections.

def purple : color := 150, 0, 150, "purple"

#eval purple.1
#eval purple.2
#eval purple.3
#eval purple.4

Alternatively, one can use the notation . to extract the relevant projections:

#eval purple.red
#eval purple.green
#eval purple.blue
#eval purple.name

When the type of the record can be inferred, you can also use the following notation to build an instance, explicitly naming each component:

def purple : color :=
{ red := 150, blue := 0, green := 150, name := "purple" }

You can also use the with keyword for record update, that is, to define an instance of a new record by modifying an existing one:

def mauve := { purple with green := 100, name := "mauve" }

#eval mauve.red
#eval mauve.green

Lean provides extensive support for reasoning generically about algebraic structures, in particular, allowing the inheritance and sharing of notation and facts. Chief among these is the use of class inference, in a manner similar to that used by functional programming languages like Haskell. For example, the Lean library declares the structures has_one and has_mul to support the generic notation 1 and * in structures which have a one and binary multiplication:

universe u
variables {α : Type u}

class has_one (α : Type u) := (one : α)
class has_mul (α : Type u) := (mul : α  α  α)

The class command not only defines a structure (in the cases above, each storing only one piece of data), but also marks them as targets for class inference. The symbol * is notation for the identifier has_mul.mul, and if you check the type of has_mul.mul, you will see there is an implicit argument for an element of has_mul:

#check @has_mul.mul

The sole element of the has_mul structure is the relevant multiplication, which should be inferred from the type α of the arguments. Given an expression a * b where a and b have type α, Lean searches through instances of has_mul that have been declared to the system, in search of one that matches the type α. When it finds such an instance, it uses that as the argument to mul.

With has_mul and has_one in place, some of the most basic objects of the algebraic hierarchy are defined as follows:

universe u
variables {α : Type u}

class semigroup (α : Type u) extends has_mul α :=
(mul_assoc :  a b c : α, a * b * c = a * (b * c))

class comm_semigroup (α : Type u) extends semigroup α :=
(mul_comm :  a b : α, a * b = b * a)

class monoid (α : Type u) extends semigroup α, has_one α :=
(one_mul :  a : α, 1 * a = a) (mul_one :  a : α, a * 1 = a)

There are a few things to note here. First, these definitions are introduced as class definitions also. This marks them as eligible for class inference, enabling Lean to find the semigroup, comm_semigroup, or monoid structure associated to a type, α, when necessary. The extends keyword does two things: it defines the new structure by adding the given fields to those of the structures being extended, and it declares any instance of the new structure to be an instance of the previous ones. Finally, notice that the new elements of these structures are not data, but, rather, properties that the data is assumed to satisfy. It is a consequence of the encoding of propositions and proofs in dependent type theory that we can treat assumptions like associativity and commutativity in a manner similar to data. We will discuss this encoding in a later chapter.

Because any monoid is an instance of has_one and has_mul, Lean can interpret 1 and * in any monoid.

variables (M : Type) [monoid M]
variables a b : M

#check a * 1 * b

The declaration [monoid M] declares a variable ranging over the monoid structure, but leaves it anonymous. The variable is automatically inserted in any definition that depends on M, and is marked for class inference. We can now define operations generically. For example, the notion of squaring an element makes sense in any structure with a multiplication.

universe u
def square {α : Type u} [has_mul α] (x : α) : α := x * x

Because monoid is an instance of has_mul, we can then use the generic squaring operation in any monoid.

variables (M : Type) [monoid M]
variables a b : M

#check square a * square b

## 2.5. Mathematics and Computation¶

Lean aims to support both mathematical abstraction alongside pragmatic computation, allowing both to interact in a common foundational framework. Some users will be interested in viewing Lean as a programming language, and making sure that every assertion has direct computational meaning. Others will be interested in treating Lean as a system for reasoning about abstract mathematical objects and assertions, which may not have straightforward computational interpretations. Lean is designed to be a comfortable environment for both kinds of users.

But Lean is also designed to support users who want to maintain both world views at once. This includes mathematical users who, having developed an abstract mathematical theory, would then like to start computing with the mathematical objects in a verified way. It also includes computer scientists and engineers who, having written a program or modeled a piece of hardware or software in Lean, would like to verify claims about it against a background mathematical theory of arithmetic, analysis, dynamical systems, or stochastic processes.

Lean employs a number of carefully chosen devices to support a clean and principled unification of the two worlds. Chief among these is the inclusion of a type Prop of propositions, or assertions. If p is an element of type Prop, you can think of an element t : p as representing evidence that p is true, or a proof of p, or simply the fact that p holds. The element t, however, does not bear any computational information. In contrast, if α is an element of Type u for any u greater than 0 and t : α, then t contains data, and can be evaluated.

Lean allows us to to define nonconstructive functions using familiar classical principles, provided we mark the associated definitions as noncomputable.

open classical
local attribute [instance] prop_decidable

noncomputable def choose (p :   Prop) :  :=
if h : ( n : , p n) then some h else 0

noncomputable def inverse (f :   ) (n : ) :  :=
if h : ( m : , f m = n) then some h else 0

In this example, declaring the type class instance prop_decidable allows us to use a classical definition by cases, depending on whether an arbitrary proposition is true or false. Given an arbitrary predicate p on the natural numbers, choose p returns an n satisfying p n if there is one, and 0 otherwise. For example, p n may assert that n code-blocks a halting computation sequence for some Turing machine, on a given input. In that case, choose p magically decides whether or not such a computation exists, and returns one if it doesn’t. The second definition makes a best effort to define an inverse to a function f from the natural numbers to the natural numbers, mapping each n to some m such that f m = n, and zero otherwise.

Lean cannot (and does not even try) to generate bytecode for noncomputable functions. But expressions t : α, where α is a type of data, can contain subexpressions that are elements of Prop, and these can refer to nonconstructive objects. During the extraction of bytecode, these elements are simply ignored, and do not contribute to the computational content of t.

For that reason, abstract elements in Lean’s library can have computational refinements. For example, for every type, α, there is another type, set α, of sets of elements of α and some sets satisfy the property of being finite. Saying that a set is finite is equivalent to saying that there exists a list that contains exactly the same elements. But this statement is a proposition, which means that it is impossible to extract such a list from the mere assertion that it exists. For that reason, the standard library also defines a type finset α, which is better suited to computation. An element s : finset α is represented by a list of elements of α without duplicates. Using quotient types, we can arrange that lists that differ up to permutation are considered equal, and a defining principle of quotient types allows us to define a function on finset α in terms of any list that represents it, provided that we show that our definition is invariant under permutations of the list. Computationally, an element of finset α is just a list. Everything else is essentially a contract that we commit ourselves to obeying when working with elements of finset α. The contract is important to reasoning about the results of our computations and their properties, but it plays no role in the computation itself.

As another example of the interaction between propositions and data, consider the fact that we do not always have algorithms that determine whether a proposition is true (consider, for example, the proposition that a Turing machine halts). In many cases, however, we do. For example, assertions m = n and m < n about natural numbers, and Boolean combinations of these, can be evaluated. Propositions like this are said to be decidable. Lean’s library uses class inference to infer the decidability, and when it succeeds, you can use a decidable property in an ifthenelse conditional statement. Computationally, what is going on is that class inference finds the relevant procedure, and the bytecode evaluator uses it.

One side effect of the choice of CIC as a foundation is that all functions we define, computational or not, are total. Once again, dependent type theory offers various mechanisms that we can use to restrict the range of applicability of a function, and some will be described later on.