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Lambda calculus (also written as λ-calculus) is a in mathematical logic for expressing based on function abstraction and application using variable and substitution. It is a universal model of computation that can be used to simulate any . It was introduced by the mathematician in the 1930s as part of his research into the foundations of mathematics.

Lambda calculus consists of constructing lambda terms and performing reduction operations on them. In the simplest form of lambda calculus, terms are built using only the following rules:

1. $x$: A variable is a character or string representing a parameter.
2. $(\lambda x.M)$: A lambda abstraction is a function definition, taking as input the bound variable $x$ (between the λ and the punctum/dot .) and returning the body $M$.
3. $(M\ N)$: An application, applying a function $M$ to an argument $N$. Both $M$ and $N$ are lambda terms.

The reduction operations include:

• $(\lambda x.Mx)\rightarrow(\lambda y.My)$ , renaming the bound variables in the expression. Used to avoid .
• $((\lambda x.M)\ N)\rightarrow (Mx:=N)$ , replacing the bound variables with the argument expression in the body of the abstraction.

If De Bruijn indexing is used, then α-conversion is no longer required as there will be no name collisions. If repeated application of the reduction steps eventually terminates, then by the Church–Rosser theorem it will produce a β-normal form.

Variable names are not needed if using a universal lambda function, such as Iota and Jot, which can create any function behavior by calling it on itself in various combinations.

Explanation and applications
Lambda calculus is Turing complete, that is, it is a universal model of computation that can be used to simulate any . Its namesake, the Greek letter lambda (λ), is used in lambda expressions and lambda terms to denote binding a variable in a function.

Lambda calculus may be untyped or typed. In typed lambda calculus, functions can be applied only if they are capable of accepting the given input's "type" of data. Typed lambda calculi are weaker than the untyped lambda calculus, which is the primary subject of this article, in the sense that typed lambda calculi can express less than the untyped calculus can. On the other hand, typed lambda calculi allow more things to be proven. For example, in the simply typed lambda calculus it is a theorem that every evaluation strategy terminates for every simply typed lambda-term, whereas evaluation of untyped lambda-terms need not terminate (see below). One reason there are many different typed lambda calculi has been the desire to do more (of what the untyped calculus can do) without giving up on being able to prove strong theorems about the calculus.

Lambda calculus has applications in many different areas in , , ,

(1988). 9789067653879, Foris Publications. .
(2024). 9781402059575, Springer. .
and .
(2024). 9780521780988, Cambridge University Press. .
.
Lambda calculus has played an important role in the development of the theory of programming languages. Functional programming languages implement lambda calculus. Lambda calculus is also a current research topic in .

History
The lambda calculus was introduced by mathematician in the 1930s as part of an investigation into the foundations of mathematics. The original system was shown to be in 1935 when and J. B. Rosser developed the Kleene–Rosser paradox.

Subsequently, in 1936 Church isolated and published just the portion relevant to computation, what is now called the untyped lambda calculus. In 1940, he also introduced a computationally weaker, but logically consistent system, known as the simply typed lambda calculus.

Until the 1960s when its relation to programming languages was clarified, the lambda calculus was only a formalism. Thanks to and other linguists' applications in the semantics of natural language, the lambda calculus has begun to enjoy a respectable place in both linguistics

(1990). 9789027722454, Springer. .
and computer science.

Origin of the λ symbol
There is some uncertainty over the reason for Church's use of the Greek letter (λ) as the notation for function-abstraction in the lambda calculus, perhaps in part due to conflicting explanations by Church himself. According to Cardone and Hindley (2006):
By the way, why did Church choose the notation “λ”? In an he stated clearly that it came from the notation “$\hat\left\{x\right\}$” used for class-abstraction by Whitehead and Russell, by first modifying “$\hat\left\{x\right\}$” to “$\land x$” to distinguish function-abstraction from class-abstraction, and then changing “$\land$” to “λ” for ease of printing.

This origin was also reported in Rosser,. On the other hand, in his later years Church told two enquirers that the choice was more accidental: a symbol was needed and λ just happened to be chosen.

has also addressed this question in various public lectures.Dana Scott, " Looking Backward; Looking Forward", Invited Talk at the Workshop in honour of Dana Scott’s 85th birthday and 50 years of domain theory, 7–8 July, FLoC 2018 (talk 7 July 2018). The relevant passage begins at 32:50. (See also this extract of a May 2016 talk at the University of Birmingham, UK.) Scott recounts that he once posed a question about the origin of the lambda symbol to Church's former student and son-in-law John W. Addison Jr., who then wrote his father-in-law a postcard:
Dear Professor Church,

Russell had the , Hilbert had the . Why did you choose lambda for your operator?

According to Scott, Church's entire response consisted of returning the postcard with the following annotation: "eeny, meeny, miny, moe".

Informal description

Motivation
Computable functions are a fundamental concept within computer science and mathematics. The lambda calculus provides simple semantics for computation which are useful for formally studying properties of computation. The lambda calculus incorporates two simplifications that make its semantics simple. The first simplification is that the lambda calculus treats functions "anonymously;" it does not give them explicit names. For example, the function
$\operatorname\left\{square\_sum\right\}\left(x, y\right) = x^2 + y^2$
can be rewritten in anonymous form as
$\left(x, y\right) \mapsto x^2 + y^2$
(which is read as "a of and is to $x^2 + y^2$"). Similarly, the function
$\operatorname\left\{id\right\}\left(x\right) = x$
can be rewritten in anonymous form as
$x \mapsto x$
where the input is simply mapped to itself.

The second simplification is that the lambda calculus only uses functions of a single input. An ordinary function that requires two inputs, for instance the $\operatorname{square\_sum}$ function, can be reworked into an equivalent function that accepts a single input, and as output returns another function, that in turn accepts a single input. For example,

$\left(x, y\right) \mapsto x^2 + y^2$
can be reworked into
$x \mapsto \left(y \mapsto x^2 + y^2\right)$
This method, known as , transforms a function that takes multiple arguments into a chain of functions each with a single argument.

Function application of the $\operatorname{square\_sum}$ function to the arguments (5, 2), yields at once

$((x, y) \mapsto x^2 + y^2)(5, 2)$
$= 5^2 + 2^2$
$= 29$,
whereas evaluation of the curried version requires one more step
$\Bigl(\bigl(x \mapsto (y \mapsto x^2 + y^2)\bigr)(5)\Bigr)(2)$
$= (y \mapsto 5^2 + y^2)(2)$ // the definition of $x$ has been used with $5$ in the inner expression. This is like β-reduction.
$= 5^2 + 2^2$ // the definition of $y$ has been used with $2$. Again, similar to β-reduction.
$= 29$
to arrive at the same result.

The lambda calculus
The lambda calculus consists of a language of lambda terms, that are defined by a certain formal syntax, and a set of transformation rules for manipulating the lambda terms. These transformation rules can be viewed as an equational theory or as an operational definition.

As described above, having no names, all functions in the lambda calculus are anonymous functions. They only accept one input variable, so is used to implement functions of several variables.

Lambda terms
The syntax of the lambda calculus defines some expressions as valid lambda calculus expressions and some as invalid, just as some strings of characters are valid C programs and some are not. A valid lambda calculus expression is called a " lambda term".

The following three rules give an inductive definition that can be applied to build all syntactically valid lambda terms:

• variable is itself a valid lambda term.
• if is a lambda term, and is a variable, then $\left(\lambda x.t\right)$ is a lambda term (called an abstraction);
• if and are lambda terms, then $\left(t$  $s\right)$ is a lambda term (called an application).
Nothing else is a lambda term. Thus a lambda term is valid if and only if it can be obtained by repeated application of these three rules. However, some parentheses can be omitted according to certain rules. For example, the outermost parentheses are usually not written. See §Notation, below for when to include parentheses

An ''abstraction'' $\lambda x.t$ denotes an § anonymous function that takes a single input  and returns . For example, $\lambda x.\left(x^2+2\right)$ is an abstraction for the function $f\left(x\right) = x^2 + 2$ using the term $x^2+2$ for . The name $f\left(x\right)$ is superfluous when using abstraction.

$\left(\lambda x.t\right)$ binds the variable in the term . The definition of a function with an abstraction merely "sets up" the function but does not invoke it. See §Notation below for usage of parentheses

An ''application'' $t$  $s$ represents the application of a function  to an input , that is, it represents the act of calling function  on input  to produce $t\left(s\right)$.


There is no concept in lambda calculus of variable declaration. In a definition such as $\lambda x.\left(x+y\right)$ (i.e. $f\left(x\right) = \left(x + y\right)$), in lambda calculus is a variable that is not yet defined. The abstraction $\lambda x.\left(x+y\right)$ is syntactically valid, and represents a function that adds its input to the yet-unknown .

Parentheses may be used and might be needed to disambiguate terms. For example,

1. $\lambda x.\left(\left(\lambda x.x\right)x\right)$ which is of form $\lambda x.B$ — an abstraction, and
2. $\left(\lambda x.\left(\lambda x.x\right)\right)$ $x$ which is of form $M$  $N$ — an application.

The examples 1 and 2 denote different terms; except for the scope of the parentheses they would be the same. But example 1 is a function definition, while example 2 is function application. Lambda variable is a placeholder in both examples.

Here, example 1 defines a function $\lambda x.B$, where $B$ is $\left(\lambda x.x\right)x$, an anonymous function $\left(\lambda x.x\right)$, with input $x$; while example 2, $M$ $N$, is M applied to N, where $M$ is the lambda term $\left(\lambda x.\left(\lambda x.x\right)\right)$ being applied to the input $N$ which is $x$. Both examples 1 and 2 would evaluate to the identity function $\lambda x.x$.

Functions that operate on functions
In lambda calculus, functions are taken to be 'first class values', so functions may be used as the inputs, or be returned as outputs from other functions.

For example, the lambda term $\lambda x.x$ represents the identity function, $x \mapsto x$. Further, $\lambda x.y$ represents the constant function $x \mapsto y$, the function that always returns $y$, no matter the input. As an example of a function operating on functions, the function composition can be defined as $\lambda f. \lambda g. \lambda x. \left(f \left( g x\right)\right)$.

There are several notions of "equivalence" and "reduction" that allow lambda terms to be "reduced" to "equivalent" lambda terms.

Alpha equivalence
A basic form of equivalence, definable on lambda terms, is alpha equivalence. It captures the intuition that the particular choice of a bound variable, in an abstraction, does not (usually) matter. For instance, $\lambda x.x$ and $\lambda y.y$ are alpha-equivalent lambda terms, and they both represent the same function (the identity function). The terms $x$ and $y$ are not alpha-equivalent, because they are not bound in an abstraction. In many presentations, it is usual to identify alpha-equivalent lambda terms.

The following definitions are necessary in order to be able to define β-reduction:

Free variables
The free variables
of a term are those variables not bound by an abstraction. The set of free variables of an expression is defined inductively:

• The free variables of $x$ are just $x$
• The of free variables of $\lambda x.t$ is the set of free variables of $t$, but with $x$ removed
• The of free variables of $t$ $s$ is the union of the set of free variables of $t$ and the set of free variables of $s$.

For example, the lambda term representing the identity $\lambda x.x$ has no free variables, but the function $\lambda x. y$ $x$ has a single free variable, $y$.

Capture-avoiding substitutions
Suppose $t$, $s$ and $r$ are lambda terms, and $x$ and $y$ are variables. The notation $tx$ indicates substitution of $r$ for $x$ in $t$ in a capture-avoiding manner. This is defined so that:
• $xx = r$ ; with $r$ substituted for $x$, $x$ becomes $r$
• $yx = y$ if $x \neq y$ ; with $r$ substituted for $x$, $y$ (which is not $x$) remains $y$
• $\left(t$ $s\right)x = \left(tx\right)\left(sx\right)$ ; substitution distributes to both sides of an application
• $\left(\lambda x.t\right)x = \lambda x.t$ ; a variable bound by an abstraction is not subject to substitution; substituting such variable leaves the abstraction unchanged
• $\left(\lambda y.t\right)x = \lambda y.\left(tx\right)$ if $x \neq y$ and $y$ does not appear among the free variables of $r$ ($y$ is said to be "" for $r$) ; substituting a variable which is not bound by an abstraction proceeds in the abstraction's body, provided that the abstracted variable $y$ is "fresh" for the substitution term $r$.

For example, $\left(\lambda x.x\right)y = \lambda x.\left(xy\right) = \lambda x.x$, and $\left(\left(\lambda x.y\right)x\right)x = \left(\left(\lambda x.y\right)x\right)\left(xx\right) = \left(\lambda x.y\right)y$.

The freshness condition (requiring that $y$ is not in the free variables of $r$) is crucial in order to ensure that substitution does not change the meaning of functions.

For example, a substitution that ignores the freshness condition could lead to errors: $\left(\lambda x.y\right)y = \lambda x.\left(yy\right) = \lambda x.x$. This erroneous substitution would turn the constant function $\lambda x.y$ into the identity $\lambda x.x$.

In general, failure to meet the freshness condition can be remedied by alpha-renaming first, with a suitable fresh variable. For example, switching back to our correct notion of substitution, in $\left(\lambda x.y\right)y$ the abstraction can be renamed with a fresh variable $z$, to obtain $\left(\lambda z.y\right)y = \lambda z.\left(yy\right) = \lambda z.x$, and the meaning of the function is preserved by substitution.

β-reduction
The β-reduction rule states that an application of the form $\left( \lambda x . t\right) s$ reduces to the term $t$. The notation $\left( \lambda x . t \right) s \to t$ is used to indicate that $\left( \lambda x .t \right) s$ β-reduces to $t$. For example, for every $s$, $\left( \lambda x . x \right) s \to x = s$. This demonstrates that $\lambda x . x$ really is the identity. Similarly, $\left( \lambda x . y \right) s \to y = y$, which demonstrates that $\lambda x . y$ is a constant function.

The lambda calculus may be seen as an idealized version of a functional programming language, like Haskell or . Under this view, β-reduction corresponds to a computational step. This step can be repeated by additional β-reductions until there are no more applications left to reduce. In the untyped lambda calculus, as presented here, this reduction process may not terminate. For instance, consider the term $\Omega = \left(\lambda x . xx\right)\left( \lambda x . xx \right)$. Here $\left( \lambda x . xx\right)\left( \lambda x . xx\right) \to \left( xx \right) = \left( x \right)\left( x \right) = \left( \lambda x . xx\right)\left( \lambda x . xx \right)$. That is, the term reduces to itself in a single β-reduction, and therefore the reduction process will never terminate.

Another aspect of the untyped lambda calculus is that it does not distinguish between different kinds of data. For instance, it may be desirable to write a function that only operates on numbers. However, in the untyped lambda calculus, there is no way to prevent a function from being applied to , strings, or other non-number objects.

Formal definition

Definition
Lambda expressions are composed of:
• variables v1, v2, ...;
• the abstraction symbols λ (lambda) and . (dot);
• parentheses ().

The set of lambda expressions, , can be defined inductively:

1. If x is a variable, then
2. If x is a variable and then
3. If then

Instances of rule 2 are known as abstractions and instances of rule 3 are known as applications.

(1984). 9780444875082, North Holland. .
ftp://ftp.cs.ru.nl/pub/CompMath.Found/ErrataLCalculus.pdf.

Notation
To keep the notation of lambda expressions uncluttered, the following conventions are usually applied:
• Outermost parentheses are dropped: M N instead of ( M N).
• Applications are assumed to be left associative: M N P may be written instead of (( M N) P).
• When all variables are single-letter, the space in applications may be omitted: MNP instead of M N P.
• The body of an abstraction extends as far right as possible: λ x. M N means λ x.( M N) and not (λ x. M) N.
• A sequence of abstractions is contracted: λ xyz. N is abbreviated as λ xyz. N.

Free and bound variables
The abstraction operator, λ, is said to bind its variable wherever it occurs in the body of the abstraction. Variables that fall within the scope of an abstraction are said to be bound. In an expression λ x. M, the part λ x is often called binder, as a hint that the variable x is getting bound by prepending λ x to M. All other variables are called free. For example, in the expression λ y. x x y, y is a bound variable and x is a free variable. Also a variable is bound by its nearest abstraction. In the following example the single occurrence of x in the expression is bound by the second lambda: λ x. yx. z x).

The set of free variables of a lambda expression, M, is denoted as FV( M) and is defined by recursion on the structure of the terms, as follows:

1. FV( x) = { x}, where x is a variable.
2. FV(λ x. M) = FV( M) \ { x}.

An expression that contains no free variables is said to be closed. Closed lambda expressions are also known as combinators and are equivalent to terms in combinatory logic.

Reduction
The meaning of lambda expressions is defined by how expressions can be reduced.

There are three kinds of reduction:

• α-conversion: changing bound variables;
• β-reduction: applying functions to their arguments;
• η-reduction: which captures a notion of extensionality.

We also speak of the resulting equivalences: two expressions are α-equivalent, if they can be α-converted into the same expression. β-equivalence and η-equivalence are defined similarly.

The term redex, short for reducible expression, refers to subterms that can be reduced by one of the reduction rules. For example, (λ x. M) N is a β-redex in expressing the substitution of N for x in M. The expression to which a redex reduces is called its reduct; the reduct of (λ x. M) N is M x.

If x is not free in M, λ x. M x is also an η-redex, with a reduct of M.

α-conversion
α-conversion (-conversion), sometimes known as α-renaming, allows bound variable names to be changed. For example, α-conversion of λ x. x might yield λ y. y. Terms that differ only by α-conversion are called α-equivalent. Frequently, in uses of lambda calculus, α-equivalent terms are considered to be equivalent.

The precise rules for α-conversion are not completely trivial. First, when α-converting an abstraction, the only variable occurrences that are renamed are those that are bound to the same abstraction. For example, an α-conversion of λ xx. x could result in λ yx. x, but it could not result in λ yx. y. The latter has a different meaning from the original. This is analogous to the programming notion of variable shadowing.

Second, α-conversion is not possible if it would result in a variable getting captured by a different abstraction. For example, if we replace x with y in λ xy. x, we get λ yy. y, which is not at all the same.

In programming languages with static scope, α-conversion can be used to make name resolution simpler by ensuring that no variable name masks a name in a containing scope (see α-renaming to make name resolution trivial).

In the De Bruijn index notation, any two α-equivalent terms are syntactically identical.

Substitution
Substitution, written M x, is the process of replacing all free occurrences of the variable x in the expression M with expression N. Substitution on terms of the lambda calculus is defined by recursion on the structure of terms, as follows (note: x and y are only variables while M and N are any lambda expression):

x x = N
y x = y, if xy
( M1 M2) x = M1 x M2 x
x. M) x = λ x. M
y. M) x = λ y.( M x), if xy and y ∉ FV( N) See above for the FV

To substitute into an abstraction, it is sometimes necessary to α-convert the expression. For example, it is not correct for (λ x. y) y to result in λ x. x, because the substituted x was supposed to be free but ended up being bound. The correct substitution in this case is λ z. x, α-equivalence. Substitution is defined uniquely up to α-equivalence. See Capture-avoiding substitutions above

β-reduction
β-reduction ( reduction) captures the idea of function application. β-reduction is defined in terms of substitution: the β-reduction of (λ x. M) N is M x.

For example, assuming some encoding of 2, 7, ×, we have the following β-reduction: (λ n. n × 2) 7 → 7 × 2.

β-reduction can be seen to be the same as the concept of local reducibility in natural deduction, via the Curry–Howard isomorphism.

η-reduction
η-reduction ( reduction) expresses the idea of ,Luke Palmer (29 Dec 2010) Haskell-cafe: What's the motivation for η rules? which in this context is that two functions are the same if and only if they give the same result for all arguments. η-reduction converts between λ x. f x and f whenever x does not appear free in f.

η-reduction can be seen to be the same as the concept of local completeness in natural deduction, via the Curry–Howard isomorphism.

Normal forms and confluence
For the untyped lambda calculus, β-reduction as a is neither strongly normalising nor weakly normalising.

However, it can be shown that β-reduction is confluent when working up to α-conversion (i.e. we consider two normal forms to be equal if it is possible to α-convert one into the other).

Therefore, both strongly normalising terms and weakly normalising terms have a unique normal form. For strongly normalising terms, any reduction strategy is guaranteed to yield the normal form, whereas for weakly normalising terms, some reduction strategies may fail to find it.

Encoding datatypes
The basic lambda calculus may be used to model , booleans, data structures, and recursion, as illustrated in the following sub-sections i, ii, iii, and § iv.

Arithmetic in lambda calculus
There are several possible ways to define the in lambda calculus, but by far the most common are the , which can be defined as follows:
and so on. Or using the alternative syntax presented above in Notation:

A Church numeral is a higher-order function—it takes a single-argument function , and returns another single-argument function. The Church numeral is a function that takes a function as argument and returns the -th composition of , i.e. the function composed with itself times. This is denoted and is in fact the -th power of (considered as an operator); is defined to be the identity function. Such repeated compositions (of a single function ) obey the laws of exponents, which is why these numerals can be used for arithmetic. (In Church's original lambda calculus, the formal parameter of a lambda expression was required to occur at least once in the function body, which made the above definition of impossible.)

One way of thinking about the Church numeral , which is often useful when analysing programs, is as an instruction 'repeat n times'. For example, using the and functions defined below, one can define a function that constructs a (linked) list of n elements all equal to x by repeating 'prepend another x element' n times, starting from an empty list. The lambda term is

By varying what is being repeated, and varying what argument that function being repeated is applied to, a great many different effects can be achieved.

We can define a successor function, which takes a Church numeral and returns by adding another application of , where '(mf)x' means the function 'f' is applied 'm' times on 'x':

Because the -th composition of composed with the -th composition of gives the -th composition of , addition can be defined as follows:
can be thought of as a function taking two natural numbers as arguments and returning a natural number; it can be verified that

and
are β-equivalent lambda expressions. Since adding to a number can be accomplished by adding 1 times, an alternative definition is:
; A note (accessed 2017) at the original location suggests that the authors consider the work originally referenced to have been superseded by a book.
Similarly, multiplication can be defined as
Alternatively
since multiplying and is the same as repeating the add function times and then applying it to zero. Exponentiation has a rather simple rendering in Church numerals, namely
The predecessor function defined by for a positive integer and is considerably more difficult. The formula
can be validated by showing inductively that if T denotes , then for . Two other definitions of are given below, one using conditionals and the other using pairs. With the predecessor function, subtraction is straightforward. Defining
,
yields  when  and  otherwise.


Logic and predicates
By convention, the following two definitions (known as Church booleans) are used for the boolean values and :
Then, with these two lambda terms, we can define some logic operators (these are just possible formulations; other expressions could be equally correct):
We are now able to compute some logic functions, for example:

:
:
and we see that is equivalent to .

A predicate is a function that returns a boolean value. The most fundamental predicate is , which returns if its argument is the Church numeral , but if its argument were any other Church numeral:

The following predicate tests whether the first argument is less-than-or-equal-to the second:
,
and since , if and , it is straightforward to build a predicate for numerical equality.

The availability of predicates and the above definition of and make it convenient to write "if-then-else" expressions in lambda calculus. For example, the predecessor function can be defined as:

which can be verified by showing inductively that is the add − 1 function for > 0.

Pairs
A pair (2-tuple) can be defined in terms of and , by using the Church encoding for pairs. For example, encapsulates the pair (,), returns the first element of the pair, and returns the second.

A linked list can be defined as either NIL for the empty list, or the of an element and a smaller list. The predicate tests for the value . (Alternatively, with , the construct obviates the need for an explicit NULL test).

As an example of the use of pairs, the shift-and-increment function that maps to can be defined as

which allows us to give perhaps the most transparent version of the predecessor function:

Additional programming techniques
There is a considerable body of programming idioms for lambda calculus. Many of these were originally developed in the context of using lambda calculus as a foundation for programming language semantics, effectively using lambda calculus as a low-level programming language. Because several programming languages include the lambda calculus (or something very similar) as a fragment, these techniques also see use in practical programming, but may then be perceived as obscure or foreign.

Named constants
In lambda calculus, a library would take the form of a collection of previously defined functions, which as lambda-terms are merely particular constants. The pure lambda calculus does not have a concept of named constants since all atomic lambda-terms are variables, but one can emulate having named constants by setting aside a variable as the name of the constant, using abstraction to bind that variable in the main body, and apply that abstraction to the intended definition. Thus to use to mean N (some explicit lambda-term) in M (another lambda-term, the "main program"), one can say
M N
Authors often introduce , such as , to permit writing the above in the more intuitive order
N M
By chaining such definitions, one can write a lambda calculus "program" as zero or more function definitions, followed by one lambda-term using those functions that constitutes the main body of the program.

A notable restriction of this is that the name be not defined in N, for N to be outside the scope of the abstraction binding ; this means a recursive function definition cannot be used as the N with . The construction would allow writing recursive function definitions.

Recursion and fixed points
is the definition of a function using the function itself. A definition containing itself inside itself, by value, leads to the whole value being of infinite size. Other notations which support recursion natively overcome this by referring to the function definition by name. Lambda calculus cannot express this: all functions are anonymous in lambda calculus, so we can't refer by name to a value which is yet to be defined, inside the lambda term defining that same value. However, a lambda expression can receive itself as its own argument, for example in  . Here E should be an abstraction, applying its parameter to a value to express recursion.

Consider the function recursively defined by

.

In the lambda expression which is to represent this function, a parameter (typically the first one) will be assumed to receive the lambda expression itself as its value, so that calling it – applying it to an argument – will amount to recursion. Thus to achieve recursion, the intended-as-self-referencing argument (called here) must always be passed to itself within the function body, at a call point:

:: with   to hold, so   and

The self-application achieves replication here, passing the function's lambda expression on to the next invocation as an argument value, making it available to be referenced and called there.

This solves it but requires re-writing each recursive call as self-application. We would like to have a generic solution, without a need for any re-writes:

:: with   to hold, so   and
where
:: so that

Given a lambda term with first argument representing recursive call (e.g. here), the fixed-point combinator will return a self-replicating lambda expression representing the recursive function (here, ). The function does not need to be explicitly passed to itself at any point, for the self-replication is arranged in advance, when it is created, to be done each time it is called. Thus the original lambda expression is re-created inside itself, at call-point, achieving .

In fact, there are many possible definitions for this operator, the simplest of them being:

In the lambda calculus,   is a fixed-point of , as it expands to:

Now, to perform our recursive call to the factorial function, we would simply call ,  where n is the number we are calculating the factorial of. Given n = 4, for example, this gives:

Every recursively defined function can be seen as a fixed point of some suitably defined function closing over the recursive call with an extra argument, and therefore, using , every recursively defined function can be expressed as a lambda expression. In particular, we can now cleanly define the subtraction, multiplication and comparison predicate of natural numbers recursively.

Standard terms
Certain terms have commonly accepted names:
(2024). 9783319089171

is the identity function.  and  form complete combinator calculus systems that can express any lambda term - see

the next section. is , the smallest term that has no normal form. is another such term.
is standard and defined above, and can also be defined as , so that .  and  defined above are commonly abbreviated as  and .


Abstraction elimination
If N is a lambda-term without abstraction, but possibly containing named constants (combinators), then there exists a lambda-term T(, N) which is equivalent to N but lacks abstraction (except as part of the named constants, if these are considered non-atomic). This can also be viewed as anonymising variables, as T(, N) removes all occurrences of from N, while still allowing argument values to be substituted into the positions where N contains an . The conversion function T can be defined by:
T(, ) := I
T(, N) := K N if is not free in N.
T(, M N) := S T(, M) T(, N)
In either case, a term of the form T(, N) P can reduce by having the initial combinator I, K, or S grab the argument P, just like β-reduction of N P would do. I returns that argument. K throws the argument away, just like N would do if has no free occurrence in N. S passes the argument on to both subterms of the application, and then applies the result of the first to the result of the second.

The combinators B and C are similar to S, but pass the argument on to only one subterm of an application ( B to the "argument" subterm and C to the "function" subterm), thus saving a subsequent K if there is no occurrence of in one subterm. In comparison to B and C, the S combinator actually conflates two functionalities: rearranging arguments, and duplicating an argument so that it may be used in two places. The W combinator does only the latter, yielding the B, C, K, W system as an alternative to SKI combinator calculus.

Typed lambda calculus
A typed lambda calculus is a typed formalism that uses the lambda-symbol ($\lambda$) to denote anonymous function abstraction. In this context, types are usually objects of a syntactic nature that are assigned to lambda terms; the exact nature of a type depends on the calculus considered (see Kinds of typed lambda calculi). From a certain point of view, typed lambda calculi can be seen as refinements of the untyped lambda calculus but from another point of view, they can also be considered the more fundamental theory and untyped lambda calculus a special case with only one type.Types and Programming Languages, p. 273, Benjamin C. Pierce

Typed lambda calculi are foundational programming languages and are the base of typed functional programming languages such as ML and Haskell and, more indirectly, typed imperative programming languages. Typed lambda calculi play an important role in the design of for programming languages; here typability usually captures desirable properties of the program, e.g. the program will not cause a memory access violation.

Typed lambda calculi are closely related to mathematical logic and via the Curry–Howard isomorphism and they can be considered as the internal language of classes of , e.g. the simply typed lambda calculus is the language of Cartesian closed categories (CCCs).

Reduction strategies
Whether a term is normalising or not, and how much work needs to be done in normalising it if it is, depends to a large extent on the reduction strategy used. Common lambda calculus reduction strategies include:
(2024). 9780262162098, . .
(2024). 9783540003267
Normal order
The leftmost, outermost redex is always reduced first. That is, whenever possible the arguments are substituted into the body of an abstraction before the arguments are reduced.
Applicative order
The leftmost, innermost redex is always reduced first. Intuitively this means a function's arguments are always reduced before the function itself. Applicative order always attempts to apply functions to normal forms, even when this is not possible.
Full β-reductions
Any redex can be reduced at any time. This means essentially the lack of any particular reduction strategy—with regard to reducibility, "all bets are off".

Weak reduction strategies do not reduce under lambda abstractions:

Call by value
A redex is reduced only when its right hand side has reduced to a value (variable or abstraction). Only the outermost redexes are reduced.
Call by name
As normal order, but no reductions are performed inside abstractions. For example, is in normal form according to this strategy, although it contains the redex .

Strategies with sharing reduce computations that are "the same" in parallel:

Optimal reduction
As normal order, but computations that have the same label are reduced simultaneously.
Call by need
As call by name (hence weak), but function applications that would duplicate terms instead name the argument, which is then reduced only "when it is needed".

Computability
There is no algorithm that takes as input any two lambda expressions and outputs or depending on whether one expression reduces to the other. More precisely, no computable function can the question. This was historically the first problem for which undecidability could be proven. As usual for such a proof, computable means computable by any model of computation that is . In fact computability can itself be defined via the lambda calculus: a function F: NN of natural numbers is a computable function if and only if there exists a lambda expression f such that for every pair of x, y in N, F( x)= y if and only if f  =β ,  where and are the corresponding to x and y, respectively and =β meaning equivalence with β-reduction. See the Church–Turing thesis for other approaches to defining computability and their equivalence.

Church's proof of uncomputability first reduces the problem to determining whether a given lambda expression has a normal form. Then he assumes that this predicate is computable, and can hence be expressed in lambda calculus. Building on earlier work by Kleene and constructing a Gödel numbering for lambda expressions, he constructs a lambda expression that closely follows the proof of Gödel's first incompleteness theorem. If is applied to its own Gödel number, a contradiction results.

Complexity
The notion of computational complexity for the lambda calculus is a bit tricky, because the cost of a β-reduction may vary depending on how it is implemented.
(1991). 9783540543961, Springer-Verlag.
To be precise, one must somehow find the location of all of the occurrences of the bound variable in the expression , implying a time cost, or one must keep track of the locations of free variables in some way, implying a space cost. A naïve search for the locations of in is O( n) in the length n of . were an early approach that traded this time cost for a quadratic space usage. More generally this has led to the study of systems that use explicit substitution.

In 2014, it was shown that the number of β-reduction steps taken by normal order reduction to reduce a term is a reasonable time cost model, that is, the reduction can be simulated on a Turing machine in time polynomially proportional to the number of steps.

(2014). 9781450328869
This was a long-standing open problem, due to size explosion, the existence of lambda terms which grow exponentially in size for each β-reduction. The result gets around this by working with a compact shared representation. The result makes clear that the amount of space needed to evaluate a lambda term is not proportional to the size of the term during reduction. It is not currently known what a good measure of space complexity would be.

An unreasonable model does not necessarily mean inefficient. Optimal reduction reduces all computations with the same label in one step, avoiding duplicated work, but the number of parallel β-reduction steps to reduce a given term to normal form is approximately linear in the size of the term. This is far too small to be a reasonable cost measure, as any Turing machine may be encoded in the lambda calculus in size linearly proportional to the size of the Turing machine. The true cost of reducing lambda terms is not due to β-reduction per se but rather the handling of the duplication of redexes during β-reduction. It is not known if optimal reduction implementations are reasonable when measured with respect to a reasonable cost model such as the number of leftmost-outermost steps to normal form, but it has been shown for fragments of the lambda calculus that the optimal reduction algorithm is efficient and has at most a quadratic overhead compared to leftmost-outermost. In addition the BOHM prototype implementation of optimal reduction outperformed both and Haskell on pure lambda terms.

Lambda calculus and programming languages
As pointed out by 's 1965 paper "A Correspondence between ALGOL 60 and Church's Lambda-notation", sequential procedural programming languages can be understood in terms of the lambda calculus, which provides the basic mechanisms for procedural abstraction and procedure (subprogram) application.

Anonymous functions
For example, in Python the "square" function can be expressed as a lambda expression as follows:

(lambda x: x**2)

The above example is an expression that evaluates to a first-class function. The symbol lambda creates an anonymous function, given a list of parameter names, x – just a single argument in this case, and an expression that is evaluated as the body of the function, x**2. Anonymous functions are sometimes called lambda expressions.

For example, Pascal and many other imperative languages have long supported passing as to other subprograms through the mechanism of function pointers. However, function pointers are not a sufficient condition for functions to be first class datatypes, because a function is a first class datatype if and only if new instances of the function can be created at run-time. And this run-time creation of functions is supported in , and , and more recently in Scala, Eiffel ("agents"), C# ("delegates") and C++11, among others.

Parallelism and concurrency
The Church–Rosser property of the lambda calculus means that evaluation (β-reduction) can be carried out in any order, even in parallel. This means that various nondeterministic evaluation strategies are relevant. However, the lambda calculus does not offer any explicit constructs for parallelism. One can add constructs such as Futures to the lambda calculus. Other have been developed for describing communication and concurrency.

Semantics
The fact that lambda calculus terms act as functions on other lambda calculus terms, and even on themselves, led to questions about the semantics of the lambda calculus. Could a sensible meaning be assigned to lambda calculus terms? The natural semantics was to find a set D isomorphic to the function space DD, of functions on itself. However, no nontrivial such D can exist, by constraints because the set of all functions from D to D has greater cardinality than D, unless D is a .

In the 1970s, showed that if only were considered, a set or D with the required property could be found, thus providing a for the lambda calculus. Written 1969, widely circulated as an unpublished manuscript.

This work also formed the basis for the denotational semantics of programming languages.

Variations and extensions
These extensions are in the :
• Typed lambda calculus – Lambda calculus with typed variables (and functions)
• – A typed lambda calculus with type-variables
• Calculus of constructions – A typed lambda calculus with as first-class values

These are extensions of lambda calculus that are not in the lambda cube:

• Binary lambda calculus – A version of lambda calculus with binary I/O, a binary encoding of terms, and a designated universal machine.
• Lambda-mu calculus – An extension of the lambda calculus for treating

These formal systems are variations of lambda calculus:

These formal systems are related to lambda calculus:

• Combinatory logic – A notation for mathematical logic without variables
• SKI combinator calculus – A computational system based on the S, K and I combinators, equivalent to lambda calculus, but reducible without variable substitutions

See also
• Applicative computing systems – Treatment of objects in the style of the lambda calculus
• Cartesian closed category – A setting for lambda calculus in
• Categorical abstract machine – A model of computation applicable to lambda calculus
• , programming language
• Curry–Howard isomorphism – The formal correspondence between programs and proofs
• De Bruijn index – notation disambiguating alpha conversions
• De Bruijn notation – notation using postfix modification functions
• Deductive lambda calculus – The consideration of the problems associated with considering lambda calculus as a .
• – Study of certain posets giving denotational semantics for lambda calculus
• Evaluation strategy – Rules for the evaluation of expressions in programming languages
• Explicit substitution – The theory of substitution, as used in β-reduction
• Functional programming
• – A kind of constructive logical formula such that proofs are lambda terms
• Kleene–Rosser paradox – A demonstration that some form of lambda calculus is inconsistent
• Knights of the Lambda Calculus – A semi-fictional organization of LISP and Scheme hackers
• – An abstract machine to interpret call-by-name in lambda calculus
• Lambda calculus definition – Formal definition of the lambda calculus.
• – An expression closely related to an abstraction.
• Minimalism (computing)
• – Transformation of formulæ in formal systems
• – A designed for the lambda calculus
• Scott–Curry theorem – A theorem about sets of lambda terms
• To Mock a Mockingbird – An introduction to combinatory logic
• Universal Turing machine – A formal computing machine that is equivalent to lambda calculus
• – An esoteric functional programming language based on combinatory logic

Further reading
• Abelson, Harold & Gerald Jay Sussman. Structure and Interpretation of Computer Programs. The MIT Press. .
• Introduction to Lambda Calculus.
• Barendregt, Hendrik Pieter, The Impact of the Lambda Calculus in Logic and Computer Science. The Bulletin of Symbolic Logic, Volume 3, Number 2, June 1997.
• Barendregt, Hendrik Pieter, The Type Free Lambda Calculus pp1091–1132 of Handbook of Mathematical Logic, North-Holland (1977)
• Cardone and Hindley, 2006. History of Lambda-calculus and Combinatory Logic . In Gabbay and Woods (eds.), Handbook of the History of Logic, vol. 5. Elsevier.
• Church, Alonzo, An unsolvable problem of elementary number theory, American Journal of Mathematics, 58 (1936), pp. 345–363. This paper contains the proof that the equivalence of lambda expressions is in general not decidable.
• ()
• Kleene, Stephen, A theory of positive integers in formal logic, American Journal of Mathematics, 57 (1935), pp. 153–173 and 219–244. Contains the lambda calculus definitions of several familiar functions.
• , A Correspondence Between ALGOL 60 and Church's Lambda-Notation, Communications of the ACM, vol. 8, no. 2 (1965), pages 89–101. Available from the ACM site. A classic paper highlighting the importance of lambda calculus as a basis for programming languages.
• Larson, Jim, An Introduction to Lambda Calculus and Scheme. A gentle introduction for programmers.
• (2013). 9780486280295, Courier Corporation.
• Schalk, A. and Simmons, H. (2005) '' An introduction to λ-calculi and arithmetic with a decent selection of exercises. Notes for a course in the Mathematical Logic MSc at Manchester University.
• A paper giving a formal underpinning to the idea of 'meaning-is-use' which, even if based on proofs, it is different from proof-theoretic semantics as in the Dummett–Prawitz tradition since it takes reduction as the rules giving meaning.
• Hankin, Chris, An Introduction to Lambda Calculi for Computer Scientists,

Monographs/textbooks for graduate students
• Sørensen, Morten Heine and Urzyczyn, Paweł (2006), Lectures on the Curry–Howard isomorphism, Elsevier, is a recent monograph that covers the main topics of lambda calculus from the type-free variety, to most typed lambda calculi, including more recent developments like pure type systems and the . It does not cover extensions.
• covers lambda calculi from a practical type system perspective; some topics like dependent types are only mentioned, but subtyping is an important topic.

Documents

Notes
Some parts of this article are based on material from FOLDOC, used with .

External links

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