matrix multiplication algorithm time complexity

I came up with this algorithm for matrix multiplication. I read somewhere that matrix multiplication has a time complexity of o(n^2). But I think my this algorithm will give o(n^3). I don't know how to calculate time complexity of nested loops. So please correct me.

for i=1 to n for j=1 to n c[i][j]=0 for k=1 to n c[i][j] = c[i][j]+a[i][k]*b[k][j] 

6 Answers

Using linear algebra, there exist algorithms that achieve better complexity than the naive O(n³). Solvay Strassen algorithm achieves a complexity of O(n^2.807) by reducing the number of multiplications required for each 2x2 sub-matrix from 8 to 7.

The fastest known matrix multiplication algorithm is Coppersmith-Winograd algorithm with a complexity of O(n^2.3737). Unless the matrix is huge, these algorithms do not result in a vast difference in computation time. In practice, it is easier and faster to use parallel algorithms for matrix multiplication.

The naive algorithm, which is what you've got once you correct it as noted in comments, is O(n^3).

There do exist algorithms that reduce this somewhat, but you're not likely to find an O(n^2) implementation. I believe the question of the most efficient implementation is still open.

See this wikipedia article on Matrix Multiplication for more information.

The standard way of multiplying an m-by-n matrix by an n-by-p matrix has complexity O(mnp). If all of those are "n" to you, it's O(n^3), not O(n^2). EDIT: it will not be O(n^2) in the general case. But there are faster algorithms for particular types of matrices -- if you know more you may be able to do better.

In matrix multiplication there are 3 for loop, we are using since execution of each for loop requires time complexity O(n). So for three loops it becomes O(n^3)

I recently had a matrix multiplication problem in my college assignment, this is how I solved it in O(n^2).

import java.util.Scanner; public class q10 { public static int[][] multiplyMatrices(int[][] A, int[][] B) { int ra = A.length; // rows in A int ca = A[0].length; // columns in A int rb = B.length; // rows in B int cb = B[0].length; // columns in B // if columns of A is not equal to rows of B, then the two matrices, // cannot be multiplied. if (ca != rb) { System.out.println("Incorrect order, multiplication cannot be performed"); return A; } else { // AB is the product of A and B, and it will have rows, // equal to rown in A and columns equal to columns in B int[][] AB = new int[ra][cb]; int k = 0; // column number of matrix B, while multiplying int entry; // = Aij, value in ith row and at jth index for (int i = 0; i < A.length; i++) { entry = 0; k = 0; for (int j = 0; j < A[i].length; j++) { // to evaluate a new Aij, clear the earlier entry if (j == 0) { entry = 0; } int currA = A[i][j]; // number selected in matrix A int currB = B[j][k]; // number selected in matrix B entry += currA * currB; // adding to the current entry // if we are done with all the columns for this entry, // reset the loop for next one. if (j + 1 == ca) { j = -1; // put the evaluated value at its position AB[i][k] = entry; // increase the column number of matrix B as we are done with this one k++; } // if this row is done break this loop, // move to next row. if (k == cb) { j = A[i].length; } } } return AB; } } @SuppressWarnings({ "resource" }) public static void main(String[] args) { Scanner ip = new Scanner(System.in); System.out.println("Input order of first matrix (r x c):"); int ra = ip.nextInt(); int ca = ip.nextInt(); System.out.println("Input order of second matrix (r x c):"); int rb = ip.nextInt(); int cb = ip.nextInt(); int[][] A = new int[ra][ca]; int[][] B = new int[rb][cb]; System.out.println("Enter values in first matrix:"); for (int i = 0; i < ra; i++) { for (int j = 0; j < ca; j++) { A[i][j] = ip.nextInt(); } } System.out.println("Enter values in second matrix:"); for (int i = 0; i < rb; i++) { for (int j = 0; j < cb; j++) { B[i][j] = ip.nextInt(); } } int[][] AB = multiplyMatrices(A, B); System.out.println("The product of first and second matrix is:"); for (int i = 0; i < AB.length; i++) { for (int j = 0; j < AB[i].length; j++) { System.out.print(AB[i][j] + " "); } System.out.println(); } } 

}

I recently got the O(n^2) algorithm for matrix multiplication in a simple way, through vector multiplication