package com.thealgorithms.dynamicprogramming;
/**
* The MatrixChainRecursiveTopDownMemoisation class implements the matrix-chain
* multiplication problem using a top-down recursive approach with memoization.
*
* <p>Given a chain of matrices A1, A2, ..., An, where matrix Ai has dimensions
* pi-1 × pi, this algorithm finds the optimal way to fully parenthesize the
* product A1A2...An in a way that minimizes the total number of scalar
* multiplications required.</p>
*
* <p>This implementation uses a memoization technique to store the results of
* subproblems, which significantly reduces the number of recursive calls and
* improves performance compared to a naive recursive approach.</p>
*/
public final class MatrixChainRecursiveTopDownMemoisation {
private MatrixChainRecursiveTopDownMemoisation() {
}
/**
* Calculates the minimum number of scalar multiplications needed to multiply
* a chain of matrices.
*
* @param p an array of integers representing the dimensions of the matrices.
* The length of the array is n + 1, where n is the number of matrices.
* @return the minimum number of multiplications required to multiply the chain
* of matrices.
*/
static int memoizedMatrixChain(int[] p) {
int n = p.length;
int[][] m = new int[n][n];
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
m[i][j] = Integer.MAX_VALUE;
}
}
return lookupChain(m, p, 1, n - 1);
}
/**
* A recursive helper method to lookup the minimum number of multiplications
* for multiplying matrices from index i to index j.
*
* @param m the memoization table storing the results of subproblems.
* @param p an array of integers representing the dimensions of the matrices.
* @param i the starting index of the matrix chain.
* @param j the ending index of the matrix chain.
* @return the minimum number of multiplications needed to multiply matrices
* from i to j.
*/
static int lookupChain(int[][] m, int[] p, int i, int j) {
if (i == j) {
m[i][j] = 0;
return m[i][j];
}
if (m[i][j] < Integer.MAX_VALUE) {
return m[i][j];
} else {
for (int k = i; k < j; k++) {
int q = lookupChain(m, p, i, k) + lookupChain(m, p, k + 1, j) + (p[i - 1] * p[k] * p[j]);
if (q < m[i][j]) {
m[i][j] = q;
}
}
}
return m[i][j];
}
}