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Haskell Programming Tutorial: Recursive Functions on Lists

This article provides a Haskell programming guide on recursive functions on lists.
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© University of Glasgow

Computing with lists

  • There are two approaches to working with lists:
    • Write functions to do what you want, using recursive definitions that traverse the list structure.
    • Write combinations of the standard list processing functions.
  • The second approach is preferred, but the standard list processing functions do need to be defined, and those definitions use the first approach (recursive definitions).
  • We’ll cover both methods.

Recursion on lists

    • A list is built from the empty list \([]\) and the function \(cons\; :: \; a\rightarrow [a] \rightarrow [a]\). In Haskell, the function \(cons\) is actually written as the operator \((:)\) , in other words : is pronounced as cons.
 
    • Every list must be either
        • \([]\) or
       
        • \((x : xs)\) for some \(x\) (the head of the list) and \(xs\) (the tail)
       
       
 
 

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where \((x : xs)\) is pronounced as \(x\, \mathit{cons}\, xs\)
 
 
    • The recursive definition follows the structure of the data:
        • Base case of the recursion is \([]\).
       
        • Recursion (or induction) case is \((x : xs)\).
       
       
 
 

Some examples of recursion on lists

 

Recursive definition of length

 
The length of a list can be computed recursively as follows:
  
length :: [a] -> Int -- function type
length [] = 0 -- base case
length (x:xs) = 1 + length xs -- recursion case
 

Recursive definition of filter

 
 
    • filter is given a predicate (a function that gives a Boolean result) and a list, and returns a list of the elements that satisfy the predicate.
 
  
filter :: (a->Bool) -> [a] -> [a]
 
Filtering is useful for the “generate and test” programming paradigm.
  
filter (<5) [3,9,2,12,6,4] -- > [3,2,4]
 
The library definition for filter is shown below. This relies on guards.
  
filter :: (a -> Bool) -> [a] -> [a]
filter pred [] = []
filter pred (x:xs)| pred x = x : filter pred xs| otherwise = filter pred xs
 

Computations over lists

 
 
    • Many computations that would be for/while loops in an imperative language are naturally expressed as list computations in a functional language.
 
    • There are some common cases: 
        • Perform a computation on each element of a list: \(map\)
       
        • Iterate over a list, from left to right: \(foldl\)
       
        • Iterate over a list, from right to left: \(foldr\)
       
       
 
    • It’s good practice to use these three functions when applicable
 
    • And there are some related functions that we’ll see later
 
 

Function composition

 
 
    • We can express a large computation by “chaining together” a sequence of functions that perform smaller computations
 
 
 
    1. Start with an argument of type \(a\)
 
    1. Apply a function \(g :: a \to b\) to it, getting an intermediate result of type \(b\)
 
    1. Then apply a function \(f :: b \to c\) to the intermediate result, getting the final result of type \(c\)
 
 
 
    • The entire computation (first \(g\), then \(f\)) is written as \(f \circ g\).
 
    • This is traditional mathematical notation; just remember that in \(f \circ g\), the functions are used in right to left order.
 
    • Haskell uses . as the function composition operator  
      (.) :: (b->c) -> (a->b) -> a -> c
(f . g) x = f (g x)
       
 
 

Performing an operation on every element of a list: map

 
 
    • map applies a function to every element of a list  
      map f [x0,x1,x2] -- > [f x0, f x1, f x2]
       
 
 

Composition of maps

 
 
    • map is one of the most commonly used tools in your functional toolkit
 
    • A common style is to define a set of simple computations using map, and to compose them.  
      map f (map g xs) = map (f . g) xs
       
 
 
This theorem is frequently used, in both directions.
 

Recursive definition of map

  
map :: (a -> b) -> [a] -> [b]
map _ [] = []
map f (x:xs) = f x : map f xs
 

Folding a list (reduction)

 
 
    • An iteration over a list to produce a singleton value is called a fold
 
    • There are several variations: folding from the left, folding from the right, several variations having to do with “initialisation”, and some more advanced variations.
 
    • Folds may look tricky at first, but they are extremely powerful, and they are used a lot! And they aren’t actually very complicated.
 
 

Left fold: foldl

 
 
    • foldl is fold from the left
 
    • Think of it as an iteration across a list, going left to right.
 
    • A typical application is \(foldl\, f\, z\, xs\)
 
    • The \(z :: b\) is an initial value
 
    • The \(xs :: [a]\) argument is a list of values which we combine systematically using the supplied function \(f\)
 
    • A useful intuition: think of the \(z :: b\) argument as an “accumulator”.
 
    • The function \(f\) takes the current value of the accumulator and a list element, and gives the new value of the accumulator.  
      foldl :: (b->a->b) -> b -> [a] -> b
       
 
 

Examples of foldl with function notation

\[\begin{aligned}
\mathtt{foldl\,f\,z\,[]} &\rightsquigarrow & z\\
\mathtt{foldl\,f\,z\,[x0]} & \rightsquigarrow & f\,z\,x0\\
\mathtt{foldl\,f\,z\,[x0,x1]} & \rightsquigarrow & f\,(f\,z\,x0)\,x1\\
\mathtt{foldl\,f\,z\,[x0,x1,x2]} & \rightsquigarrow & f\,(f\,(f\,z\,x0)\,x1)\, x2\end{aligned}\]

Examples of foldl with infix notation

 
In this example, + denotes an arbitrary operator for f; it isn’t supposed to mean specifically addition.
  
foldl (+) z [] -- > z
foldl (+) z [x0] -- > z + x0
foldl (+) z [x0,x1] -- > (z + x0) + x1
foldl (+) z [x0,x1,x2] -- > ((z + x0) + x1) + x2
 

Recursive definition of foldl

  
foldl :: (b -> a -> b) -> b -> [a] -> b
foldl f z0 xs0 = lgo z0 xs0wherelgo z [] = zlgo z (x:xs) = lgo (f z x) xs
 

Right fold: foldr

 
 
    • Similar to \(foldl\), but it works from right to left
 
  
foldr :: (a -> b -> b) -> b -> [a] -> b
 

Examples of foldr with function notation

\[\begin{aligned}
\mathtt{foldr\,f\, z\, [] } & \rightsquigarrow & z\\
\mathtt{foldr\, f\, z\, [x0] } & \rightsquigarrow & f\, x0\, z\\
\mathtt{foldr\, f\, z\, [x0,x1] } & \rightsquigarrow & f\, x0\, (f\, x1\, z)\\
\mathtt{foldr\, f\, z\, [x0,x1,x2] } & \rightsquigarrow & f\, x0\, (f\, x1\, (f\, x2\, z))\end{aligned}\]

Examples of foldr with operator notation

  
foldr (+) z [] -- > z
foldr (+) z [x0] -- > x0 + z
foldr (+) z [x0,x1] -- > x0 + (x1 + z)
foldr (+) z [x0,x1,x2] -- > x0 + (x1 + (x2 + z))
 

Recursive definition of foldr

  
foldr :: (a -> b -> b) -> b -> [a] -> b
foldr k z = gowherego [] = zgo (y:ys) = y `k` go ys
 

Relationship between foldr and list structure

 
We have seen that a list [x0,x1,x2] can also be written as
  
 x0 : x1 : x2 : []
 
Folding \(cons\) (:) over a list using the empty list [] as accumulator gives:
  
foldr (:) [] [x0,x1,x2]-- >x0 : x1 : x2 : []
 
This is identical to constructing the list using (:) and [] ! We can formalise this relationship as follows:
\[foldr \; cons \; [] \; xs \; = \; xs\]

Some applications of folds

  
sum xs = foldr (+) 0 xs
product xs = foldr (*) 1 xs
 
We can actually “factor out” the \(xs\) that appears at the right of each side of the equation, and write:
  
sum = foldr (+) 0
product = foldr (*) 1
(This is sometimes called “point free” style because you’re programming solely with the functions; the data isn’t mentioned directly.)
© University of Glasgow

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