Notes (HPFP 01/31): All You Need is Lambda

1 All You Need is Lambda

1.3 What is a Function?

Function: A relation between an input set and output set such that each input has only one ouput. Personally, I think its more intuitive to imagine a function as a transformation that changes one type of thing (the input set) into another type of thing (the output set). I like to imagine a vegetable juicer: Put carrots in, get carrot juice out. Put spinach in, get spinach juice out. What is the function of a vegetable juicer? It transforms vegetables into vegetable juice. The set of things that can go into a vegetable juicer are its input set (or domain) and the set of things that can come out are its output set (or codomain).

Computable functions: If a function is a transformation between two sets of things, and we can build machines which do transformations, then any function for which we can build a machine is called a computable function. Not all functions are computable.

Turing Machines: A simple model of a computer based on manipulating symbols on a tape according to some rules and an internal state. Despite its simplicity, a Turing Machine can be built that executes any computable function.

Lambda Calculus: A simple model of computation based on building, applying, and evaluating functions. A function built using the Lambda Calculus is called a lambda expression, and for any computable function, a lambda calculus can be constructed which evaluates that function.

parameter: An input to a function.

“functions are first-class”: Functions can be parameters.

referential transparency: The property that the same function with the same parameters returns the same output. All functions are referentially transparent over all their parameters, but in some situations it can be difficult to tell what all a functions’ parameters are. Suppose you turn a dozen carrots into carrot juice with a vegetable juicer, don’t clean the juicer afterwards. You’ll drink your carrot juice, but the next person to use the juicer will have carrot flavored juice, even if they wanted celery and ginger. The juicer function has hidden paramters, i.e. the residue of previous inputs. Functions like this can be described as stateful, because they have some internal or hidden parameters whose state can affect the output of the whole function.

purity: A synonym for referential transparency.

Functional programming: Building machines (programs) that evaluate computable functions using the lambda calculus.

1.4 The Structure of lambda terms

abstraction: A lamda expression that represents a function. It can be written down with the folowing notation: \(\lambda x.x\). Everything between the \(\lambda\) and the \(.\) is called the head, and the symbol in the head names the parameter of the function. After the \(.\) is the body, and describes what to do with the parameter, when the abstraction is applied. Terms that occur in both the head and the body are called bound variables, and symbols that only appear in the body are called unbound, or free variables.

currying: Properly speaking, all abstractions have only single parameters. Functions with multiple paramters are expressed as a nested strucure of single- parameter functions. For example \(\lambda xy.xy\) is more properly written as \(\lambda x.(\lambda y.xy)\).

application: A lambda expression can be applied to some input like so: \(((\lambda x.x) N)\). Following the reductions steps will evaluate the function described by \(\lambda x.x\) with input parameter N.

1.5 Beta reduction

alpha equivalence: Within an abstraction, the specific symbols in the head may be replaced by other symbols as long as the replacement is consistent and total. For example, in the abstraction \(\lambda x.x\) the term \(x\) in the head may be replaced with \(y\), so long as all instance of \(x\) in the body are also replaced with \(y\). Thus, \(\lambda x.x\), \(\lambda y.y\), and any other expression of the form \(\lambda n.n\) (for some \(n\)) are alpha equivalent.

beta reduction: An abstraction is evaluated by replacing all its bound variables with the expression the abstraction is evaluated against (its input), and then removing the head of the abstraction. For example, \(((\lambda x.x) N)\) would be evaluated by replacing all \(x\)’s in the body with \(N\) (yielding \(\lambda x.N\)) and then removing the head for the final output of \(N\).

Intermission: Equivalence Exercises (p.13)

  1. \xy.xz is equivalent to \, by alpha equivalence of x with m and y with n.
  2. \xy.xxy is equivalent to \a.(\b.aab), by currying and alpha equivalence.
  3. \xyz.zx is equivalent to \

1.7 Evaluation is simplification

beta normal form: When an expression cannot be further reduced through beta reduction (i.e. application of abstractions). This signals the end of the evaluation.

combinator: A lambda term with no free variables. \(\lambda x.x\) is a combinator, \(\lambda x.xy\) is not.

divergence: If an expression can never reach beta normal form, it is said to diverge. For example, \((\lambda x.xx)(\lambda x.xx)\) diverges. This corresponds to non-terminating function (an infinite loop).

1.11 Chapter Exercises (p.17)


  1. \ is a combinator.
  2. \xy.zx is not a combinator, z is free.
  3. \xyz.xy(zx) is a combinator.
  4. \xyz.xy(zxy) is a combinator
  5. \xy.xy(zxy) is not a combinator, z is free

Normal form or diverge?

  1. \ is Normal
  2. (\z.zz)(\y.yy) diverges
  3. (\ is Normal

Beta Reduce

  1. (\\wv.w)


    (\\wv.w) ->
    (\bc.cbz)z(\wv.w) ->
    (\c.czz)(\wv.w) ->
    (\wv.w)zz ->
    (\v.z)z ->
  2. (\x.\y.xyy)(\a.a)b


    (\x.\y.xyy)(\a.a)b ->
    (\y.(\a.a)yy)b ->
    (\a.a)bb ->
  3. (\y.y)(\x.xx)(\z.zq)


    (\y.y)(\x.xx)(\z.zq) ->
    (\x.xx)(\z.zq) ->
    (\z.zq)(\z.zq) ->
    (\z.zq)q ->
    (\z.zq)q ->
  4. (\z.z)(\z.zz)(\z.zy)


    (\z.z)(\z.zz)(\z.zy) ->
    (\z.zz)(\z.zy) ->
    (\z.zy)(\z.zy) ->
    (\z.zy)y ->
  5. (\x.\y.xyy)(\y.y)y


    (\x.\y.xyy)(\y.y)y ->
  6. (\a.aa)(\


    (\a.aa)(\ ->
    (\\ ->
    (\ ->
  7. (\xyz.xz(yz))(\x.z)(\x.a)


    (\xyz.xz(yz))(\x.z)(\x.a) ->

1.14 Follow-up Resources

  1. Raul Rojas. A Tutorial Introduction to the Lambda Calculus

    Notes: A Tutorial Introduction to the Lambda Calculus (Rojas)

  2. Henk Barendregt; Erik Barendsen. Introduction to Lambda Calculus

    Notes: Introduction to Lambda Calculus by Henk Barendregt & Erik Barendsen

  3. Jean-Yves Girard; P. Taylor; Yves Lafon. Proofs and Types

    Notes: Proofs And Types by Jean-Yves Girard Barendsen