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Merge pull request TheAlgorithms#280 from abhishek-987/newBranch
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added Dynamic Programming Problems
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yanglbme authored Aug 1, 2019
2 parents a3ddfcd + 0b8d66b commit c071083
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51 changes: 51 additions & 0 deletions Dynamic Programming/Coin-Change.cpp
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#include <iostream>
#include<climits>
using namespace std;

// Function to find the Minimum number of coins required to get Sum S
int findMinCoins(int arr[], int n, int N)
{
// dp[i] = no of coins required to get a total of i
int dp[N + 1];

// 0 coins are needed for 0 sum

dp[0] = 0;


for (int i = 1; i <= N; i++)
{
// initialize minimum number of coins needed to infinity
dp[i] = INT_MAX;
int res = INT_MAX;

// do for each coin
for (int c = 0; c < n; c++)
{
if (i - arr[c] >= 0) // check if coins doesn't become negative by including it
res = dp[i - arr[c]];

// if total can be reached by including current coin c,
// update minimum number of coins needed dp[i]
if (res != INT_MAX)
dp[i] = min(dp[i], res + 1);
}
}

// The Minimum No of Coins Required for N = dp[N]
return dp[N];
}

int main()
{
// No of Coins We Have
int arr[] = { 1, 2, 3, 4 };
int n = sizeof(arr) / sizeof(arr[0]);

// Total Change Required
int N = 15;

cout << "Minimum Number of Coins Required "<< findMinCoins(arr, n, N) << "\n";

return 0;
}
62 changes: 62 additions & 0 deletions Dynamic Programming/Matrix-Chain-Multiplication.cpp
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#include <iostream>
#include <climits>
using namespace std;

#define MAX 10

// dp table to store the solution for already computed sub problems
int dp[MAX][MAX];

// Function to find the most efficient way to multiply the given sequence of matrices
int MatrixChainMultiplication(int dim[], int i, int j)
{
// base case: one matrix
if (j <= i + 1)
return 0;

// stores minimum number of scalar multiplications (i.e., cost)
// needed to compute the matrix M[i+1]...M[j] = M[i..j]
int min = INT_MAX;

// if dp[i][j] is not calculated (calculate it!!)

if (dp[i][j] == 0)
{
// take the minimum over each possible position at which the
// sequence of matrices can be split

for (int k = i + 1; k <= j - 1; k++)
{
// recur for M[i+1]..M[k] to get a i x k matrix
int cost = MatrixChainMultiplication(dim, i, k);

// recur for M[k+1]..M[j] to get a k x j matrix
cost += MatrixChainMultiplication(dim, k, j);

// cost to multiply two (i x k) and (k x j) matrix
cost += dim[i] * dim[k] * dim[j];

if (cost < min)
min = cost; // store the minimum cost
}
dp[i][j] = min;
}

// return min cost to multiply M[j+1]..M[j]
return dp[i][j];
}

// main function
int main()
{
// Matrix i has Dimensions dim[i-1] & dim[i] for i=1..n
// input is 10 x 30 matrix, 30 x 5 matrix, 5 x 60 matrix
int dim[] = { 10, 30, 5, 60 };
int n = sizeof(dim) / sizeof(dim[0]);

// Function Calling: MatrixChainMultiplications(dimensions_array, starting, ending);

cout << "Minimum cost is " << MatrixChainMultiplication(dim, 0, n - 1) << "\n";

return 0;
}

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