In this post I solve the Minimum Coin Exchange problem programmatically using Haskell. I will compare the performance of the naive implementation to that using dynamic programming.
The problem
The minimum coin exchange problem is generally asked: if we want to make change for \(N\) cents, and we have infinite supply of each of \(S=\{S_{0},S_{2},\ldots ,S_{m1}\}\) valued coins, what is the least amount of coins we need to make the change?
The solution can be found in a recursive manner where at each step of the recursion we have two options:

we use the \(S_m\) valued coin in the change and we try to find the change for \(N  S_m\) cents with the same set of coins.

we decide not to use the \(S_m\) valued coin in the change: we keep on looking for a change of \(N\) cents with \(m1\) coins
At each step we need to choose the option that uses less coins. It is expected that this process will end after finite number of steps because either the number of coins or the money to change decreases at each step.
On the algorithmist.com we find a succinct recursive formula to describe this process
the arguments of the \(\min\) function correspond to the two options described above. The \(+1\) in the first argument shows that choosing that option will increase the number of coins in the change. The recursive formula is completed with the base cases:
If the result of \(C(N,m)\) is either \(0\) or \(\infty\) then it is impossible make change for \(N\) with the given coins. The case (1) applies when we successfully changed \(N\) with the given coins. Case (2) means that our smallest valued coin is larger than the amount for which the change is requested. Finally (3) represents the situation where we cannot pick any more coin to complete the change.
For simplicity, we will use a fixed set of coins: \([25, 20, 10, 5, 1]\). For example, the minimum number of coins needed for 40 cents using the formulation above is given by \(C(40, 4)\). Also note that we only compute the number of coins, we don’t give the exact denominators to be used in the change.
Foundations
As we use a fixed set of coins we hardcode the available coin denominators:
 Available coin denominators.
coins :: [Int]
coins = [25, 20, 10, 5, 1]
We will write a function with the following signature:
change :: Int  amount
> Int  index of the last available coin
> Change  the number of coins
change
takes to arguments: the amount to change, the index of the last
available coin and it returns the number of coins in the change.
Taking a list index as the second argument is, of course, very error prone: our
program will crash if we try to address an element that is not present in the
coins
list. We ignore this deficency in order to stay as close as possible
to the theoretical formulation of the problem.
Since there are cases where the change is impossible, we choose the result type
Change
as
type Change = Maybe Int
This is more expressive than using infinity as a sentinel value when the change is not possible.
With this preparation we are ready to implement the first version of change
.
Naive implementation
 Naive implementation using the recursive formula from:
 http://www.algorithmist.com/index.php/MinCoin_Change
change :: Int > Int > Change
change n m
 n == 0 = Just 0
 n < 0 = Nothing
 n >= 1 && m <= 0 = Nothing
 otherwise = minOf left right
where
left = (+1) `fmap` change (n  sm) m
right = change n (m  1)
sm = coins !! m
This implementation is almost maps onetoone to the recursive formula above.
The first three guard expression handle the three base cases. In the last
clause the function calls itself to solve the two subproblems which I
called left
and right
.
Choosing Maybe
to represent the result Change
forces us to deviate from the
clean mathematical formulation at two places:
 we use
fmap
to add1
to the result of theleft
subproblem  we use our own
minOf
function instead of the builtinmin
function
The first point is easy: we cannot add an Int
to a Maybe Int
because their
types don’t match. Since Maybe Int
is a functor so we can use fmap
to lift
the addition into the context of Maybe
.
As for the second point, let’s see the implementation of minOf
:
import Control.Applicative ((<>))
minOf :: Change > Change > Change
minOf (Just i) (Just j) = Just (min i j)
minOf c1 c2 = c1 <> c2
The function takes two Change
values: if both Maybe
contain Int
values
we choose the smaller one. Otherwise we try to keep the one that has a value
in it using <>
. This behaves similarly to logical OR. We can easily test
this in ghci
:
Prelude> import Control.Applicative
Prelude Control.Applicative> Just 1 <> Nothing  keeps the first
Just 1
Prelude Control.Applicative> Nothing <> Just 2  keeps the second
Just 2
Prelude Control.Applicative> Nothing <> Nothing  returns Nothing
Nothing
Prelude Control.Applicative> Just 1 <> Just 2  prefers the first
Just 1
This implementation of minOf
gives us the right behavior when we select the
smaller between the results of the left
and right
subproblems.
The builtin min
can actually operate on Maybe Int
values. We just cannot
use it here because it returns Nothing
if any of its argument is Nothing
.
This would terminate our recursive function without exploring the whole
solution space.
We could almost directly implement the terse mathematical solution as a recursive function. Overall our function is short and readable, but before we open the champagne and celebrate let’s see how our solution performs.
Performance of the naive solution
We can use the microbenchmarking library criterion to measure the running
time of the naive change
implementation. Let’s see how the running time
depends on the amount to change. The following table shows the approximate
time of computing the change for 40, 100, 150 and 200 cents.
Amount [cents]  Running time [ms] 

40  0.026 
100  0.344 
150  1.420 
200  4.025 
The running time of the naive solution scales polynomially with the number of cents. Let’s try to improve this!
Implementation using dynamic programming
The recursive method of the minimum coin exchange problem combines the solutions of subproblems with smaller amounts to change. We could optimize our naive solution using dynamic programming. The idea is that every time we solve a subproblem we memorize its solution. The next time the same subproblem occurs, instead of recomputing its solution we look up the previously solved solution.
We write a new function changeD
which represents a stateful computation. The
state is a map from problem parameters to its solution. In our case, a map
from pair of integers (the denominator index and the amount) to a Change
value. We call this computation Dyn
:
import qualified Data.Map.Strict as M
import Control.Monad.Trans.State
type Dyn = State (M.Map (Int, Int) Change)
Using this, we can write changeD
which, returns a Dyn
computation resulting
in a Change
:
changeD :: Int > Int > Dyn Change
changeD n m
 n == 0 = return $ Just 0
 n < 0 = return Nothing
 n >= 1 && m <= 0 = return Nothing
 otherwise = do
left < memorize n (m  1)
right < memorize (n  sm) m
return $ minOf left (fmap (+1) right)
where
sm = coins !! m
The code looks very much like the naive solution but, since we’re operating in
the Dyn
context, we are using the do
notation. The memorize
computation
implements the storing and recalling the subproblems’ solution:
memorize :: Int > Int > Dyn Change
memorize n m = do
val < do
elem < gets $ M.lookup (n, m)  try to recall the solution
case elem of
Just x > return x  return previously stored solution
Nothing > changeD n m  compute the solution
modify $ M.insert (n, m) val  store the solution
return val
This function is a literal implementation of the dynamic programming method. We try to look up the solution in the state: if the subproblem has already been solved we return the solution, otherwise we solve the subproblem and store its solution in the state.
The final step is to provide change
in a form identical to the naive solution:
change :: Int > Int > Change
change m n = evalState (changeD m n) M.empty
We execute the Dyn
computation using evalState
by providing an initial
empty state. This function provides exactly the same interface as the naive
version above. The two implementations can be used interchangeably. Let’s
which of the two implementations is worth using.
Naive vs dynamic
The table below compares the running times of the two implementations as a function of the amount to change.
Amount [cents]  Running time (naive) [ms]  Running time (dynamic) [ms] 

40  0.026  0.117 
100  0.344  0.354 
150  1.420  0.556 
200  4.025  0.814 
The dynamic programming version scales linearly with the amount to change. The difference in running time becomes significant for amounts larger than 100 cents. As always, this speedup didn’t come for free: we traded running speed for storage space.
Summary
The minimum coin exchange problem is a classic example demonstrating dynamic
programming. We implemented a solution by naively transcribing the proposed
recursive formula almost literally to Haskell. We then used dynamic
programming to improve time complexity of the naive solution. The dynamic
programming method was encapsulated in a Dyn
computation where the solutions
to already solved subproblems are stored in a map.
The code for both implementations can be found here.