Lambda Calculus

Your Favorite Language

Probably has lots of features:

  • Assignment (x = x + 1)
  • Booleans, integers, characters, strings, …
  • Conditionals
  • Loops
  • return, break, continue
  • Functions
  • Recursion
  • References / pointers
  • Objects and classes
  • Inheritance
  • …

Which ones can we do without?

What is the smallest universal language?


















What is computable?

Before 1930s

Informal notion of an effectively calculable function:

can be computed by a human with pen and paper, following an algorithm





1936: Formalization

What is the smallest universal language?

Alan Turing



Alonzo Church














The Next 700 Languages

Peter Landin

Whatever the next 700 languages turn out to be, they will surely be variants of lambda calculus.

Peter Landin, 1966















The Lambda Calculus

Has one feature:

  • Functions











No, really:

  • Assignment (x = x + 1)
  • Booleans, integers, characters, strings, …
  • Conditionals
  • Loops
  • return, break, continue
  • Functions
  • Recursion
  • References / pointers
  • Objects and classes
  • Inheritance
  • Reflection











More precisely, all you can do is:

  • define a function
  • call a function











Describing a Programming Language

  • Syntax: what do programs look like?
  • Semantics: what do programs mean?
    • operational semantics: how do programs execute step-by-step?











Syntax: What Programs Look Like


E ::= x
    | \x -> E
    | E1 E2


Programs are expressions E (also called λ-terms) of one of three kinds:

  • Variable
    • x, y, z
  • Abstraction (aka nameless function definition)
    • \x -> E
    • x is the formal parameter, E is the body
    • “for any x compute E”
  • Application (aka function call)
    • E1 E2
    • E1 is the function, E2 is the argument
    • in your favorite language: E1(E2)

(Here each of E, E1, E2 can itself be a variable, abstraction, or application)











Example Expressions

apple               -- Variable named "apple"

apple banana        -- Application of variable "apple"
                    -- to variable "banana"

\x -> x             -- The identity function
                    -- ("for any x compute x")
                    
(\x -> x) apple     -- Application of the identity function
                    -- to variable "apple"                    

\x -> (\y -> y)     -- A function that returns the identity function
 
\f -> f (\x -> x)   -- A function that applies its argument 
                    -- to the identity function













QUIZ

Which of the following terms are syntactically incorrect?

A. \(\x -> x) -> y

B. \x -> x x

C. \x -> x (y x)

D. A and C

E. all of the above












Examples

\x -> x             -- The identity function
                    -- ("for any x compute x")

\x -> (\y -> y)     -- A function that returns the identity function
 
\f -> f (\x -> x)   -- A function that applies its argument 
                    -- to the identity function


How do I define a function with two arguments?

  • e.g. a function that takes x and y and returns y?













\x -> (\y -> y)     -- A function that returns the identity function
                    -- OR: a function that takes two arguments
                    -- and returns the second one!













How do I apply a function to two arguments?

  • e.g. apply \x -> (\y -> y) to apple and banana?













((\x -> (\y -> y)) apple) banana -- first apply to apple,
                                 -- then apply the result to banana












Syntactic Sugar



instead of we write
\x -> (\y -> (\z -> E)) \x -> \y -> \z -> E
\x -> \y -> \z -> E \x y z -> E
(((E1 E2) E3) E4) E1 E2 E3 E4 



\x y -> y     -- A function that that takes two arguments
              -- and returns the second one...
              
(\x y -> y) apple banana -- ... applied to two arguments













Semantics : What Programs Mean


How do I “run” / “execute” a λ-term?


Think of middle-school algebra:

-- Simplify expression:

  (x + 2)*(3x - 1)
 => -- RULE: mult. polynomials
  3x^2 - x + 6x - 2
 => -- RULE: add monomials
  3x^2 + 5x - 2 -- no more rules to apply


Execute = rewrite step-by-step following simple rules, until no more rules apply













Rewrite Rules of Lambda Calculus


  1. α-step (aka renaming formals)
  2. β-step (aka function call)


But first we have to talk about scope







Semantics: Scope of a Variable

The part of a program where a variable is visible

In the expression \x -> E

  • x is the newly introduced variable

  • E is the scope of x

  • any occurrence of x in \x -> E is bound (by the binder \x)


For example, x is bound in:

  \x -> x
  \x -> (\y -> x)
  \x -> (\x -> x)   -- by which binder is x bound?



An occurrence of x in E is free if it’s not bound by an enclosing abstraction


For example, x is free in:

  x y                -- no binders at all!  
  \y -> x y          -- no \x binder
  (\x -> \y -> y) x  -- x is outside the scope of the \x binder;
                     -- intuition: it's not "the same" x












QUIZ

In the expression (\x -> x) x, is x bound or free?

A. bound

B. free

C. first occurrence is bound, second is free

D. first occurrence is bound, second and third are free

E. first two occurrences are bound, third is free













EXERCISE: Free Variables

A variable x is free in E if there exists a free occurrence of x in E


We can formally define the set of all free variables in a term like so:

FV(x)       = ???
FV(E1 E2)   = ???
FV(\x -> E) = ???













Closed Expressions

If E has no free variables it is said to be closed

  • Closed expressions are also called combinators



What is the shortest closed expression?













Rewrite Rules of Lambda Calculus


  1. α-step (aka renaming formals)
  2. β-step (aka function call)













Semantics: β-Reduction


  (\x -> E1) E2   =b>   E1[x := E2]


where E1[x := E2] means “E1 with all free occurrences of x replaced with E2”



Computation by search-and-replace:

  • If you see an abstraction applied to an argument, take the body of the abstraction and replace all free occurrences of the formal by that argument

  • Abstraction applied to an argument is called a redex (REDucible EXpression)

  • We say that (\x -> E1) E2 β-steps to E1[x := E2]













Examples


(\x -> x) apple     
=b> apple

Is this right? Ask Elsa!



(\f -> f (\x -> x)) (give apple)
=b> ???













QUIZ


(\x -> (\y -> y)) apple
=b> ???

A. apple

B. \y -> apple

C. \x -> apple

D. \y -> y

E. \x -> y













QUIZ


(\x -> x (\x -> x)) apple
=b> ???

A. apple (\x -> x)

B. apple (\apple -> apple)

C. apple (\x -> apple)

D. apple

E. \x -> x













A Tricky One


(\x -> (\y -> x)) y
=b> \y -> y

Is this right?













Something is Fishy


(\x -> (\y -> x)) y
=b> \y -> y

Is this right?

Problem: the free y in the argument has been captured by \y!

Solution: make sure that all free variables of the argument are different from the binders in the body.













Capture-Avoiding Substitution

We have to fix our definition of β-reduction:

  (\x -> E1) E2   =b>   E1[x := E2]


where E1[x := E2] means “E1 with all free occurrences of x replaced with E2”

  • E1 with all free occurrences of x replaced with E2, as long as no free variables of E2 get captured
  • undefined otherwise


Formally:

x[x := E']            = E'
y[x := E']            = y            -- assuming x /= y
(E1 E2)[x := E']      = (E1[x := E']) (E2[x := E'])
(\x -> E)[x := E']    = \x -> E      -- why do we leave `E` alone?
(\y -> E)[x := E'] 
  | not (y in FV(E')) = \y -> E[x := E']
  | otherise          = undefined    -- wait, but what do we do then???












Rewrite Rules of Lambda Calculus


  1. α-step (aka renaming formals)
  2. β-step (aka function call)













Semantics: α-Renaming


  \x -> E   =a>   \y -> E[x := y]
    where not (y in FV(E))


  • Intuition: we can rename a formal parameter and replace its occurrences in the body

  • Here E[x := y] is capture-avoiding substitution

  • We say that \x -> E α-steps to \y -> E[x := y]



Example:

\x -> x   =a>   \y -> y   =a>    \z -> z

All these expressions are α-equivalent




What’s wrong with these?

-- (A)
\f -> f x    =a>   \x -> x x
-- (B)
(\x -> \y -> y) y   =a>   (\x -> \z -> z) z
-- (C)
\x -> (\y -> x y)   =a>    \y -> (\y -> y y)














Tricky Example Revisited


(\x -> (\y -> x)) y
=a> ???



To avoid getting confused, you can always rename formals, so that different variables have different names!













Normal Forms

Recall that a redex is a λ-term of the form

(\x -> E1) E2

A λ-term is in normal form if it contains no redexes.














QUIZ

Which of the following term are not in normal form (i.e. contains a redex)?

A. x y

B. (\x -> x) y

C. x (\y -> y) z

D. x ((\y -> y) z)

E. B and D













Semantics: Evaluation

A λ-term E evaluates to E' if

  1. There is a sequence of steps
E =?> E_1 =?> ... =?> E_N =?> E'

where each =?> is either =a> or =b> and N >= 0

  1. E' is in normal form







Examples of Evaluation

(\x -> x) apple
  =b> apple


(\f -> (\x -> x) f) (\x -> x)
  =?> ???













QUIZ

What does the following term evaluate to?

(\x -> x x) (\x -> x)

A. x

B. (\x -> x) (\x -> x)

C. \x -> x

D. \x -> x x

E. cannot be evaluated













EXERCISE: Non-Terminating Evaluation

Can you come up with an expression E such that the evaluation of (\x -> x x) E loops, i.e.:

eval loop : 
  (\x -> x x) E
  =b> (\x -> x x) E
  =b> (\x -> x x) E
  =b> ...

ELSA: https://elsa.goto.ucsd.edu/index.html

Click here to try this exercise








Non-Terminating Evaluation

Some programs loop back to themselves…

… and never reduce to a normal form!

This combinator is called Ω







Elsa shortcuts

Named λ-terms:

let ID = \x -> x  -- abbreviation for \x -> x



To substitute name with its definition, use a =d> step:

ID apple
  =d> (\x -> x) apple    -- expand definition
  =b> apple              -- beta-reduce



Evaluation:

  • e1 =*> e2: e1 reduces to e2 in 0 or more steps
    • where each step is =a>, =b>, or =d>
  • e1 =~> e2: e1 evaluates to e2 and e2 is in normal form











Programming in λ-calculus

Real languages have lots of features

  • Booleans
  • Records (structs, tuples)
  • Numbers
  • Functions [we got those]
  • Recursion

Lets see how to encode all of these features with the λ-calculus.













λ-calculus: Booleans


How can we encode Boolean values (TRUE and FALSE) as functions?


Well, what do we do with a Boolean b?













Make a binary choice

  • if b then E1 else E2




Booleans: API

We need to define three functions

let TRUE  = ???
let FALSE = ???
let ITE   = \b x y -> ???  -- if b then x else y

such that

ITE TRUE apple banana =~> apple
ITE FALSE apple banana =~> banana

(Here, let NAME = E means NAME is an abbreviation for E)













Booleans: Implementation

let TRUE  = \x y -> x        -- Returns its first argument
let FALSE = \x y -> y        -- Returns its second argument
let ITE   = \b x y -> b x y  -- Applies condition to branches
                             -- (redundant, but improves readability)













Example: Branches step-by-step

eval ite_true:
  ITE TRUE egg ham
  =d> (\b x y -> b    x   y)  TRUE egg ham    -- expand def ITE  
  =b>   (\x y -> TRUE x   y)       egg ham    -- beta-step
  =b>     (\y -> TRUE egg y)           ham    -- beta-step
  =b>            TRUE egg ham                 -- expand def TRUE
  =d>     (\x y -> x) egg ham                 -- beta-step
  =b>     (\y -> egg)     ham                 -- beta-step
  =b> egg







Example: Branches step-by-step

Now you try it!

eval ite_false:
  ITE FALSE egg ham

  -- fill the steps in!

  =b> ham

























EXERCISE: Boolean Operators

ELSA: https://elsa.goto.ucsd.edu/index.html

Click here to try this exercise

Now that we have ITE it’s easy to define other Boolean operators:

let NOT = \b     -> ???
let OR  = \b1 b2 -> ???
let AND = \b1 b2 -> ???

When you are done, you should get the following behavior:

eval ex_not_t:
  NOT TRUE =*> FALSE
  
eval ex_not_f:
  NOT FALSE =*> TRUE 
  
eval ex_or_ff:
  OR FALSE FALSE =*> FALSE

eval ex_or_ft:
  OR FALSE TRUE =*> TRUE
  
eval ex_or_ft:
  OR TRUE FALSE =*> TRUE

eval ex_or_tt:
  OR TRUE TRUE =*> TRUE
  
eval ex_and_ff:
  AND FALSE FALSE =*> FALSE

eval ex_and_ft:
  AND FALSE TRUE =*> FALSE
  
eval ex_and_ft:
  AND TRUE FALSE =*> FALSE

eval ex_and_tt:
  AND TRUE TRUE =*> TRUE












Programming in λ-calculus

  • Booleans [done]
  • Records (structs, tuples)
  • Numbers
  • Functions [we got those]
  • Recursion













λ-calculus: Records

Let’s start with records with two fields (aka pairs)

What do we do with a pair?

  1. Pack two items into a pair, then
  2. Get first item, or
  3. Get second item.















Pairs : API

We need to define three functions

let PAIR = \x y -> ???    -- Make a pair with elements x and y 
                          -- { fst : x, snd : y }
let FST  = \p -> ???      -- Return first element 
                          -- p.fst
let SND  = \p -> ???      -- Return second element
                          -- p.snd

such that

eval ex_fst: 
  FST (PAIR apple banana) =~> apple

eval ex_snd:
  SND (PAIR apple banana) =~> banana













Pairs: Implementation

A pair of x and y is just something that lets you pick between x and y! (i.e. a function that takes a boolean and returns either x or y)

let PAIR = \x y -> (\b -> ITE b x y)
let FST  = \p -> p TRUE   -- call w/ TRUE, get first value
let SND  = \p -> p FALSE  -- call w/ FALSE, get second value













EXERCISE: Triples

How can we implement a record that contains three values?

ELSA: https://elsa.goto.ucsd.edu/index.html

Click here to try this exercise

let TRIPLE = \x y z -> ???
let FST3   = \t -> ???
let SND3   = \t -> ???
let THD3   = \t -> ???

eval ex1:
  FST3 (TRIPLE apple banana orange)
  =~> apple

eval ex2:
  SND3 (TRIPLE apple banana orange)
  =~> banana 

eval ex3:
  THD3 (TRIPLE apple banana orange)
  =~> orange













Programming in λ-calculus

  • Booleans [done]
  • Records (structs, tuples) [done]
  • Numbers
  • Functions [we got those]
  • Recursion













λ-calculus: Numbers

Let’s start with natural numbers (0, 1, 2, …)

What do we do with natural numbers?

  • Count: 0, inc
  • Arithmetic: dec, +, -, *
  • Comparisons: ==, <=, etc













Natural Numbers: API

We need to define:

  • A family of numerals: ZERO, ONE, TWO, THREE, …
  • Arithmetic functions: INC, DEC, ADD, SUB, MULT
  • Comparisons: IS_ZERO, EQ

Such that they respect all regular laws of arithmetic, e.g.

IS_ZERO ZERO       =~> TRUE
IS_ZERO (INC ZERO) =~> FALSE
INC ONE            =~> TWO
...













Natural Numbers: Implementation

Church numerals: a number N is encoded as a combinator that calls a function on an argument N times

let ONE   = \f x -> f x
let TWO   = \f x -> f (f x)
let THREE = \f x -> f (f (f x))
let FOUR  = \f x -> f (f (f (f x)))
let FIVE  = \f x -> f (f (f (f (f x))))
let SIX   = \f x -> f (f (f (f (f (f x)))))
...

QUIZ: Church Numerals

Which of these is a reasonable encoding of ZERO ?

  • A: let ZERO = \f x -> x

  • B: let ZERO = \f x -> f

  • C: let ZERO = \f x -> f x

  • D: let ZERO = \x -> x

  • E: None of the above




Does this function look familiar?












λ-calculus: Increment

-- Call `f` on `x` one more time than `n` does
let INC   = \n -> (\f x -> ???)













Example:

eval inc_zero :
  INC ZERO
  =d> (\n f x -> f (n f x)) ZERO
  =b> \f x -> f (ZERO f x)
  =*> \f x -> f x
  =d> ONE













EXERCISE

Fill in the implementation of ADD so that you get the following behavior

Click here to try this exercise

let ZERO = \f x -> x
let ONE  = \f x -> f x
let TWO  = \f x -> f (f x)
let INC  = \n f x -> f (n f x)

let ADD  = fill_this_in 

eval add_zero_zero: 
  ADD ZERO ZERO =~> ZERO

eval add_zero_one: 
  ADD ZERO ONE =~> ONE

eval add_zero_two: 
  ADD ZERO TWO =~> TWO 

eval add_one_zero: 
  ADD ONE ZERO =~> ONE

eval add_one_zero: 
  ADD ONE ONE =~> TWO

eval add_two_zero: 
  ADD TWO ZERO =~> TWO













λ-calculus: Addition

--  Call `f` on `x` exactly `n + m` times
let ADD = \n m -> n INC m




Example:

eval add_one_zero :
  ADD ONE ZERO
  =~> ONE













QUIZ

How shall we implement MULT?

A. let MULT = \n m -> n ADD m

B. let MULT = \n m -> n (ADD m) ZERO

C. let MULT = \n m -> m (ADD n) ZERO

D. let MULT = \n m -> n (ADD m ZERO)

E. let MULT = \n m -> (n ADD m) ZERO













λ-calculus: Multiplication

--  Call `f` on `x` exactly `n * m` times
let MULT = \n m -> n (ADD m) ZERO




Example:

eval two_times_three :
  MULT TWO ONE
  =~> TWO













Programming in λ-calculus

  • Booleans [done]
  • Records (structs, tuples) [done]
  • Numbers [done]
  • Functions [we got those]
  • Recursion













λ-calculus: Recursion


I want to write a function that sums up natural numbers up to n:

\n -> ...          -- 0 + 1 + 2 + ... + n











QUIZ

Is this a correct implementation of SUM?

let SUM = \n -> ITE (ISZ n) 
            ZERO 
            (ADD n (SUM (DEC n)))

A. Yes

B. No











No!

  • Named terms in Elsa are just syntactic sugar
  • To translate an Elsa term to λ-calculus: replace each name with its definition
\n -> ITE (ISZ n) 
        ZERO 
        (ADD n (SUM (DEC n))) -- But SUM is not a thing!



Recursion:

  • Inside this function I want to call the same function on DEC n



Looks like we can’t do recursion, because it requires being able to refer to functions by name, but in λ-calculus functions are anonymous.

Right?













λ-calculus: Recursion

Think again!



Recursion:

  • Inside this function I want to call the same function on DEC n
  • Inside this function I want to call a function on DEC n
  • And BTW, I want it to be the same function



Step 1: Pass in the function to call “recursively”

let STEP = 
  \rec -> \n -> ITE (ISZ n) 
                  ZERO 
                  (ADD n (rec (DEC n))) -- Call some rec





Note:

  • STEP rec ZERO =*> ZERO
  • STEP rec ONE =*> ADD ONE (rec ZERO)
  • STEP rec TWO =*> ADD TWO (rec ONE)
  • STEP rec THREE =*> ADD THREE (rec TWO)

So:

    STEP (STEP (STEP (STEP f))) THREE
=*> ADD THREE (STEP (STEP (STEP f)) TWO)
=*> ADD THREE (ADD TWO (STEP (STEP f) ONE))
=*> ADD THREE (ADD TWO (ADD ONE (STEP f ZERO)))
=*> ADD THREE (ADD TWO (ADD ONE ZERO))
--  ^ Exactly what we want!






Step 2: Do something clever to apply STEP to itself, i.e so that the function passed as rec becomes STEP rec













λ-calculus: Fixpoint Combinator

Wanted: a combinator FIX such that FIX STEP calls STEP with itself as the first argument:

FIX STEP
=*> STEP (FIX STEP)


(In math: a fixpoint of a function f(x) is a point x, such that f(x) = x)





Once we have it, we can define:

let SUM = FIX STEP

Then by property of FIX we have:

SUM =*> STEP SUM -- (1)
eval sum_three:
                             SUM    THREE   -- (1)
  =*>                   STEP SUM    THREE   -- (1)
  =*>             STEP (STEP SUM)   THREE   -- (1)
  =*>       STEP (STEP (STEP SUM))  THREE   -- (1)
  =*> STEP (STEP (STEP (STEP SUM))) THREE   -- we've seen this before
  =*> ADD THREE (ADD TWO (ADD ONE ZERO))



How should we define FIX???













The Y combinator

Remember Ω?

(\x -> x x) (\x -> x x)
=b> (\x -> x x) (\x -> x x)

This is self-replicating code! We need something like this but a bit more involved…





The Y combinator discovered by Haskell Curry:

let FIX   = \stp -> (\x -> stp (x x)) (\x -> stp (x x))



How does it work?

eval fix_step:
  FIX STEP
  =d> (\stp -> (\x -> stp (x x)) (\x -> stp (x x))) STEP
  =b> (\x -> STEP (x x)) (\x -> STEP (x x))
  =b> STEP ((\x -> STEP (x x)) (\x -> STEP (x x)))
  --       ^^^^^^^^^^ this is FIX STEP ^^^^^^^^^^^






That’s all folks!