Sparse-matrix-multiplication晴初**nt 在2024-02-15 15:27:48 访问982.1 KB 稀疏矩阵乘法是一种优化算法,用于处理具有大量零元素的稀疏矩阵。传统的矩阵乘法算法在处理稀疏矩阵时效率较低,因为它需要进行大量的乘法和加法运算。 改进的稀疏矩阵乘法算法采用了压缩存储的方式,将稀疏矩阵转化为三元组表示。对于每个非...
returnSparseMatrixMultiplicationHelper( A , B , a , b ); } publicstaticint[][] MatrixSum(int[][] A ,int[][] B){ int[][] C =newint[A.length][A[0].length]; for(inti=0;i<C.length;i++ ) for(intj=0;j<C[0].length;j++ ) C[i][j] = A[i][j] + B[i][j]; retu...
因为给定的matrix是稀疏矩阵,所以我们要做一些对于0的预处理。 由于C[i][k] = A[i][x] * B[x][k], 0 <= x <= n 我们可以用一个HashMap,将B中每行不为0的元素保存下来。 然后遍历A,将每个不为0的元素累加到C中去。 classSolution{publicint[][]multiply(int[][]A,int[][]B){if(A==nul...
Solution {public[][] multiply(int[][] A,int[][] B) {intm =A.length;intn = A[0].length;intnB = B[0].length;int[][] C =newint[m][nB];for(i = 0; i < m; i++){for(intk = 0; k < n; k++){if(A[i][k] != 0){for(intj = 0; j< nB; j++){if(B[k][j...
LeetCode "Sparse Matrix Multiplication" Taking advantage of 'sparse' classSolution {public: vector<vector<int>> multiply(vector<vector<int>>& A, vector<vector<int>>&B) { vector<vector<int>>ret;intha =A.size();if(!ha)returnret;intwa = A[0].size();if(!wa)returnret;inthb =wa;int...
classSolution{publicint[][]multiply(int[][]A,int[][]B){introw=A.length;intcol=B[0].length;introwB=A[0].length;int[][]C=newint[row][col];for(inti=0;i<row;i++){for(intk=0;k<rowB;k++){if(A[i][k]!=0){for(intj=0;j...
Single- CPU code written in the C language is provided in Appendix A for comparison.We start with two simplifications that will undoubtedly have a dire impact on performance: first, all memory transfers between the host processor and board are completed before and after all of the computation; ...
Sparse Matrix are those most elements of the matrix to zero .This paper sparse matrix "sparse" characteristics of storage and computing can greatly save storage space and improve efficiency.Through the use of standard C++ Language Design and Implementation of the sparse matrix multiplication.关键词:...
LeetCode "Sparse Matrix Multiplication" Taking advantage of 'sparse' classSolution {public: vector<vector<int>> multiply(vector<vector<int>>& A, vector<vector<int>>&B) { vector<vector<int>>ret;intha =A.size();if(!ha)returnret;intwa = A[0].size();if(!wa)returnret;inthb =wa;int...
Double precision floating point Sparse Matrix-Vector Multiplication (SMVM) is a critical computational kernel used in iterative solvers for systems of sparse linear equations. The poor data locality exhibited by sparse matrices along with the high memory bandwidth requirements of SMVM result in poor ...