matrix-multiplication

问题 I'm trying to solve the linear equation AX=B where A,X,B are Matrices. I've tried using the np.linalg.solve function of numpy but the result seems to be wrong. Example: Matrix A [9 1 8] [3 2 5] [1 6 5] Matrix B [7 0 5] [7 8 4] [5 6 7] So to solve X, i've used: X = np.linalg.solve(A,B) The result is: X [ 1.17521368 -0.17948718 0.40598291] [ 0.20512821 -0.30769231 0.74358974] [-0.56410256 -0.15384615 1.20512821] But if i try to verify the result by multiplying A by X, the result is anything but

问题 Hey guys I am trying practice learning pointers in c++. So I am trying to multiply these arrays and am getting all 0's in my resulting matrix. if anybody could just hint to me what to look at or some advice on what is causing this that would be amazing. Here is the code: #include <stdio.h> #include<conio.h> #include <stdlib.h> #include <iostream> /* Routines called. */ int loadMatrixFromFile(char *filename, int *data); void showMatrix(int *data, int len); int makeIdent(int matrixB[5][5], int

问题 How can I use Blas to multiply a real matrix with a complex vector ? When I use functions like ccsrgemv() I get type mismatch errors? error: argument of type "float *" is incompatible with parameter of type "std::complex<float> *" 回答1: Use two matrix-vector multiplications (A * (x + iy) = A * x + i A * y). More precisely, consider your complex vector as two entangled real vectors with stride 2. BLAS lets you do this. UPDATE : actually, I did not notice that you were doing Sparse BLAS. For

问题 How can I use Blas to multiply a real matrix with a complex vector ? When I use functions like ccsrgemv() I get type mismatch errors? error: argument of type "float *" is incompatible with parameter of type "std::complex<float> *" 回答1: Use two matrix-vector multiplications (A * (x + iy) = A * x + i A * y). More precisely, consider your complex vector as two entangled real vectors with stride 2. BLAS lets you do this. UPDATE : actually, I did not notice that you were doing Sparse BLAS. For

问题 How can I use Blas to multiply a real matrix with a complex vector ? When I use functions like ccsrgemv() I get type mismatch errors? error: argument of type "float *" is incompatible with parameter of type "std::complex<float> *" 回答1: Use two matrix-vector multiplications (A * (x + iy) = A * x + i A * y). More precisely, consider your complex vector as two entangled real vectors with stride 2. BLAS lets you do this. UPDATE : actually, I did not notice that you were doing Sparse BLAS. For

问题 I have complex-valued data given in 2 channels of a matrix (one is the real, one the imaginary part, so the matrix dimensions are (height, width, 2) , since Pytorch does not have native complex data types. I now want to calculate the covariance matrix. The stripped-down numpy calculation adapted for Pytorch is this: def cov(m, y=None): if m.ndimension() > 2: raise ValueError("m has more than 2 dimensions") if y.ndimension() > 2: raise ValueError('y has more than 2 dimensions') X = m if X

问题 I have complex-valued data given in 2 channels of a matrix (one is the real, one the imaginary part, so the matrix dimensions are (height, width, 2) , since Pytorch does not have native complex data types. I now want to calculate the covariance matrix. The stripped-down numpy calculation adapted for Pytorch is this: def cov(m, y=None): if m.ndimension() > 2: raise ValueError("m has more than 2 dimensions") if y.ndimension() > 2: raise ValueError('y has more than 2 dimensions') X = m if X

问题 I'm trying to speed up a matrix multiplication algorithm by blocking the loops to improve cache performance, yet the non-blocked version remains significantly faster regardless of matrix size, block size (I've tried lots of values between 2 and 200, potenses of 2 and others) and optimization level. Non-blocked version: for(size_t i = 0; i < n; ++i) { for(size_t k = 0; k < n; ++k) { int r = a[i][k]; for(size_t j = 0; j < n; ++j) { c[i][j] += r * b[k][j]; } } } Blocked version: for(size_t kk =

问题 I want to do block matrix-matrix multiplication with the following C code.In this approach, blocks of size BLOCK_SIZE is loaded into the fastest cache in order to reduce memory traffic during calculation. void bMMikj(double **A , double **B , double ** C , int m, int n , int p , int BLOCK_SIZE){ int i, j , jj, k , kk ; register double jjTempMin = 0.0 , kkTempMin = 0.0; for (jj=0; jj<n; jj+= BLOCK_SIZE) { jjTempMin = min(jj+ BLOCK_SIZE,n); for (kk=0; kk<n; kk+= BLOCK_SIZE) { kkTempMin = min(kk

问题 I want to do block matrix-matrix multiplication with the following C code.In this approach, blocks of size BLOCK_SIZE is loaded into the fastest cache in order to reduce memory traffic during calculation. void bMMikj(double **A , double **B , double ** C , int m, int n , int p , int BLOCK_SIZE){ int i, j , jj, k , kk ; register double jjTempMin = 0.0 , kkTempMin = 0.0; for (jj=0; jj<n; jj+= BLOCK_SIZE) { jjTempMin = min(jj+ BLOCK_SIZE,n); for (kk=0; kk<n; kk+= BLOCK_SIZE) { kkTempMin = min(kk