I am trying to solve a classical coin-change (dynamic) problem.
To find number of all unique combinations to get a sum from infinite denominations of coins using dynamic approach, i used this method:
/*
n - number of coins
arr[] - coin denominations
x - total sum
dp[] - array to store number of combinations at index i.
*/
for (int j = 0; j < n; j++)
for (int i = 1; i <= x; i++)
if (arr[j] <= i)
dp[i] = (long) ((dp[i] + dp[i - arr[j]]) % (1e9 + 7));
This gives me all unique possible combinations
count:
Eg:
Input:
n=3 x=9
Coins: 2 3 5
Output:
3
So far ,all good.
But i observed that just by interchanging the loops in above snippet, i get all the possible permutations
.
for (int i = 1; i <= x; i++)
for (int j = 0; j < n; j++)
if (arr[j] <= i)
dp[i] = (long) ((dp[i] + dp[i - arr[j]]) % (1e9 + 7));
This gives me all unique possible permutations
count:
Eg:
Input:
3 9
2 3 5
Output:
8
With debugging and going through each iteration, i mapped a pattern that was formed, but didn't understand the reason behind why i am getting permutations.
Can any one explain me this iteratively. Any help will be appreciated.
Thanks
Both questions can be found here:
The first code with outer loop by coins updates number of ways to compose values dp[]
with new coin at every round of outer loop. So after k-th round we have dp[]
array filled with combinations of k
coins only, and the rest of coins is not used yet. If we will store combinations themselves for sorted coin array, we will see only ordered ones like 1 1 5
, and 5 never will go before 1. That is why combinations.
The second code at m-th round of outer loop fills m-th cell dp[m]
using all possible coins. So we count for m=7
both 1 1 5
and 1 5 1
and 5 1 1
variants. That is why all permutations are counted here.
In addition for comment: we can make 2d array, where dp[x][c]
contains number of permutations with sum x
, ending with coin a[c]
. Note that in this case we have to join counts of permutations with sum x-a[c]
. For reference - 1d and 2d Python code.
def coins1(a, n): #permutations
count = [1]+[0]*n
for x in range(1, n + 1):
for c in a:
if (x-c >= 0):
count[x] += count[x-c]
return count[n]
def coins11(a, n): #permutations 2d
m = len(a)
count = [[1] + [0]*(m-1)] + [[0]*m for i in range(n)]
for x in range(1, n + 1):
for c in range(m):
if x>=a[c]:
count[x][c] += sum(count[x-a[c]])
return sum(count[n])