matrix-multiplication

问题 I am wondering is there a way of calculating matrix multiplication using NumericMatrix and NumericVector class. I am wondering if there is any simple way to help me avoid the following loop to conduct this calculation. I just want to calculate X%*%beta. // assume X and beta are initialized and X is of dimension (nsites, p), // beta is a NumericVector with p elements. for(int j = 0; j < nsites; j++) { temp = 0; for(int l = 0; l < p; l++) temp = temp + X(j,l) * beta[l]; } Thank you very much in

问题 I'm trying to implement SSE version of large matrix by matrix multiplication. I'm looking for an efficient algorithm based on SIMD implementations. My desired method looks like: A(n x m) * B(m x k) = C(n x k) And all matrices are considered to be 16-byte aligned float array. I searched the net and found some articles describing 8x8 multiplication and even smaller. I really need it as efficient as possible and I don't want to use Eigen library or similar libraries. (Only SSE3 to be more

问题 I don't have enough memory to simply create a diagonal D-by-D matrix, since D is large. I keep getting an 'out of memory' error. Instead of performing M x D x D operations in the first multiplication, I do M x D operations, but still my code takes ages to run. Can anybody find a more effective way to perform the multiplication A'*B*A ? Here's what I've attempted so far: D=20000 M=25 A = floor(rand(D,M)*10); B = floor(rand(1,D)*10); for i=1:D for j=1:M result(i,j) = A(i,j) * B(1,j); end end

问题 Closed . This question is opinion-based. It is not currently accepting answers. Want to improve this question? Update the question so it can be answered with facts and citations by editing this post. Closed 3 years ago . What is the best matrix multiplication algorithm? What means 'the best'for me? It means the fastest and ready for todays machines. Please give links to pseudocode if you can. 回答1: BLAS is the best ready-to-use efficient matrix multiplication library. There are many different

问题 I'm trying to make a simple matrix multiplication method using multidimensional arrays ( [2][2] ). I'm kinda new at this, and I just can't find what it is I'm doing wrong. I'd really appreciate any help in telling me what it is. I'd rather not use libraries or anything like that, I'm mostly doing this to learn how it works. Thank you so much in advance. I'm declaring my arays in the main method as follows: Double[][] A={{4.00,3.00},{2.00,1.00}}; Double[][] B={{-0.500,1.500},{1.000,-2.0000}};

问题 Here Matrix multiplication using hdf5 I use hdf5 (pytables) for big matrix multiplication, but I was suprised because using hdf5 it works even faster then using plain numpy.dot and store matrices in RAM, what is the reason of this behavior? And maybe there is some faster function for matrix multiplication in python, because I still use numpy.dot for small block matrix multiplication. here is some code: Assume matrices can fit in RAM: test on matrix 10*1000 x 1000. Using default numpy(I think

问题 I'm looking for a faster and trickier way to multiply two 4x4 matrices in C. My current research is focused on x86-64 assembly with SIMD extensions. So far, I've created a function witch is about 6x faster than a naive C implementation, which has exceeded my expectations for the performance improvement. Unfortunately, this stays true only when no optimization flags are used for compilation (GCC 4.7). With -O2 , C becomes faster and my effort becomes meaningless. I know that modern compilers

问题 I was trying to figure out the fastest way to do matrix multiplication and tried 3 different ways: Pure python implementation: no surprises here. Numpy implementation using numpy.dot(a, b) Interfacing with C using ctypes module in Python. This is the C code that is transformed into a shared library: #include <stdio.h> #include <stdlib.h> void matmult(float* a, float* b, float* c, int n) { int i = 0; int j = 0; int k = 0; /*float* c = malloc(nay * sizeof(float));*/ for (i = 0; i < n; i++) {

问题 I want to do the element-wise outer product of two 2d arrays in numpy. A.shape = (100, 3) # A numpy ndarray B.shape = (100, 5) # A numpy ndarray C = element_wise_outer_product(A, B) # A function that does the trick C.shape = (100, 3, 5) # This should be the result C[i] = np.outer(A[i], B[i]) # This should be the result A naive implementation can the following. tmp = [] for i in range(len(A): outer_product = np.outer(A[i], B[i]) tmp.append(outer_product) C = np.array(tmp) A better solution

问题 I want to calculate the row-wise dot product of two matrices of the same dimension as fast as possible. This is the way I am doing it: import numpy as np a = np.array([[1,2,3], [3,4,5]]) b = np.array([[1,2,3], [1,2,3]]) result = np.array([]) for row1, row2 in a, b: result = np.append(result, np.dot(row1, row2)) print result and of course the output is: [ 26. 14.] 回答1: Check out numpy.einsum for another method: In [52]: a Out[52]: array([[1, 2, 3], [3, 4, 5]]) In [53]: b Out[53]: array([[1, 2,