Coin change

Coin Change is the problem of finding the number of ways of making changes for a particular amount of cents, $$n$$, using a given set of denominations $$d_1 \ldots d_m$$. It is a general case of Integer Partition, and can be solved with dynamic programming. (The Min-Coin Change is a common variation of this problem.)

Overview
The problem is typically asked as: If we want to make change for $$N$$ cents, and we have infinite supply of each of $$S = \{ S_1, S_2, \ldots, S_m \}$$ valued coins, how many ways can we make the change? (For simplicity's sake, the order does not matter.)

It is more precisely defined as:

Given an integer $$N$$ and a set of integers $$S = \{ S_1, S_2, \ldots, S_m \}$$, how many ways can one express $$N$$ as a linear combination of $$S = \{ S_1, S_2, \ldots, S_m \}$$ with non-negative coefficients?

Mathematically, how many solutions are there to $$N = \sum_{k = 1 \ldots m}{ x_k S_k }$$ where $$x_k \geq 0, k \in \{ 1 \ldots m \} $$

For example, for $$N = 4, S = \{1, 2, 3\}$$, there are four solutions: $$\{1, 1, 1, 1\}, \{1, 1, 2\} , \{2, 2\} , \{1, 3\}$$.

Other common variations on the problem include decision-based question, such as:

Is there a solution for $$N = \sum_{k = 1 \ldots m}{ x_k S_k }$$ where $$x_k \geq 0, k \in \{ 1 \ldots m \} $$ (Is there a solution for integer $$N$$ and a set of integers $$S = \{ S_1, S_2, \ldots, S_m \}$$?)

Is there a solution for $$N = \sum_{k = 1 \ldots m}{ x_k S_k }$$ where $$x_k \geq 0, k \in \{ 1 \ldots m \}, \sum_{k = 1 \ldots m}{x_k} \leq T $$ (Is there a solution for integer $$N$$ and a set of integers $$S = \{ S_1, S_2, \ldots, S_m \}$$ such that $$\sum_{k = 1 \ldots m}{x_k} \leq T $$ - using at most $$T$$ coins)

Recursive Formulation
We are trying to count the number of distinct sets.

Since order does not matter, we will impose that our solutions (sets) are all sorted in non-decreasing order (Thus, we are looking at sorted-set solutions: collections).

For a particular $$N$$ and $$S = \{ S_1, S_2, \ldots, S_m \}$$ (now with the restriction that $$S_1 < S_2 < \ldots < S_m$$, our solutions can be constructed in non-decreasing order), the set of solutions for this problem, $$C( N, m )$$, can be partitioned into two sets:


 * There are those sets that do not contain any $$S_m$$ and
 * Those sets that contain at least 1 $$S_m$$

This partitioning will essentially break the initial problem into two subproblems:


 * 1) If $$N < S_m$$ (that is, a solution does not contain $$S_m$$), then we can solve the subproblem of $$N$$ with $$S = \{ S_1, S_2, \ldots, S_{m-1} \}$$, or the solutions of $$C( N, m - 1 )$$.
 * 2) If $$N \geq S_m$$ (that is, a solution does in fact contain $$S_m$$), then we are using at least one $$S_m$$, thus we are now solving the subproblem of $$N - S_m$$, $$S = \{ S_1, S_2, \ldots, S_m \}$$.  This is $$C( N - S_m, m )$$.

Thus, we can formulate the following:

$$C( N, m ) = C( N, m - 1 ) + C( N - S_m, m )$$

with the base cases:


 * $$C( N, m ) = 1, N = 0$$ (one solution -- we have no money, exactly one way to solve the problem - by choosing no coin change, or, more precisely, to choose coin change of 0)
 * $$C( N, m ) = 0, N \leq 0$$ (no solution -- negative sum of money)
 * $$C( N, m ) = 0, N \geq 1, m \leq 0$$ (no solution -- we have money, but no change available)

Dynamic Programming
Note that the recursion satisfies the weak ordering $$R(n,m) < R(x,y) \iff n \leq x, m \leq y, (n,m) \ne (x,y)$$. As a result, this satisfies the optimal-substructure property of dynamic programming.

The result can be computed in $$O(nm)$$ time - the above pseudocode can easily be modified to contain memoization. It can be also rewritten as:

func count( n, m )

for i from 0 to n   for j from 0 to m      if i equals 0 table[i,j] = 1 else if j equals 0 table [i,j] = 0 else if S_j greater than i        table[ i, j ] = table[ i, j - 1 ] else table[ i, j ] = table[ i - S_j, j ] + table[ i, j - 1 ]

return table[ n, m ]