matrix-multiplication

来源： https://stackoverflow.com/questions/15829223/loop-tiling-blocking-for-large-dense-matrix-multiplication

问题 I want a strange dot product for matrix multiplication in numpy. For a line [1,2,3] of matrix A and a column [4,5,6] for matrix B , I wish to use the "product" min(1+4, 2+5, 3+6) for obtaining the matrix product AB . 回答1: In [498]: A = np.arange(12).reshape(4,3) In [499]: B = np.arange(4,10).reshape(3,2) In [500]: A Out[500]: array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]]) In [501]: B Out[501]: array([[4, 5], [6, 7], [8, 9]]) Reference iterative solution: In [504]: res = np.zeros((A

问题 I would like to write in C++ Tensorflow sparse matrix dense vector (SPMv) multiplication: y = Ax The sparse matrix, A, is stored in CSR format. The usual sparsity of A is between 50-90%. The goal is to reach better or similar time than that of dense matrix dense vector (DMv) multiplication. Please note that I have already viewed the following posts: Q1 Q2 Q3. However, I still am wondering about the following: How does SPMv multiplication compare in terms of time to DMv? Since sparsity is

问题 This question is a follow up to the discussion from this answer. What is the difference between using c(... %*% ...) and sum(... * ...) in a group_by() function from dplyr ? Both of these code give the same result: #1 library(dplyr) # 1.0.0 library(tidyr) df1 %>% group_by(Date, Market) %>% group_by(Revenue = c(Quantity %*% Price), TotalCost = c(Quantity %*% Cost), Product, .add = TRUE) %>% summarise(Sold = sum(Quantity)) %>% pivot_wider(names_from = Product, values_from = Sold) #2 library

问题 This question is a follow up to the discussion from this answer. What is the difference between using c(... %*% ...) and sum(... * ...) in a group_by() function from dplyr ? Both of these code give the same result: #1 library(dplyr) # 1.0.0 library(tidyr) df1 %>% group_by(Date, Market) %>% group_by(Revenue = c(Quantity %*% Price), TotalCost = c(Quantity %*% Cost), Product, .add = TRUE) %>% summarise(Sold = sum(Quantity)) %>% pivot_wider(names_from = Product, values_from = Sold) #2 library

问题 void multiply(int a[], int row1, int col1, int b[], int row2, int col2) { int d[size]; for (int i = 0; i < row1; i++) { for (int j = 0; j < col2; j++) { int sum = 0.0; for (int k = 0; k < col2; k++) sum = sum + a[i * col1 + k] * b[k * col2 + j]; d[i * col2 + j] = sum; } } for (int i = 0; i < size; i++) { if (i % col2 == 0) { printf("\n"); } printf("%d ", d[i]); } } I have this as a function to multiple two one-dimensional arrays that are supposed to be matrices. I'm using an online compiler

问题 Given a list of nxn matrices, I want to compute the cumulative product of these matrix multiplications - i.e. given matrices M0, M1, ...,Mm I want a result R, where R[0] = M1, R[1] = M0 x M1, R[2]= M0 x M1 x M2 and so on. Obviously, you can do this via for-loops or tail recursion, but I'm coding in python, where that runs at a snails pace. In Code: def matrix_mul_cum_sum(M): #M[1..m] is an m x n x n matrix if len(M) == 0: return [] result = [M[1]] for A in M[1:]: result.append(np.mat_mul

问题 I need to multiply two big matrices and sort their columns. import numpy a= numpy.random.rand(1000000, 100) b= numpy.random.rand(300000,100) c= numpy.dot(b,a.T) sorted = [argsort(j)[:10] for j in c.T] This process takes a lot of time and memory. Is there a way to fasten this process? If not how can I calculate RAM needed to do this operation? I currently have an EC2 box with 4GB RAM and no swap. I was wondering if this operation can be serialized and I dont have to store everything in the

问题 I need to multiply two big matrices and sort their columns. import numpy a= numpy.random.rand(1000000, 100) b= numpy.random.rand(300000,100) c= numpy.dot(b,a.T) sorted = [argsort(j)[:10] for j in c.T] This process takes a lot of time and memory. Is there a way to fasten this process? If not how can I calculate RAM needed to do this operation? I currently have an EC2 box with 4GB RAM and no swap. I was wondering if this operation can be serialized and I dont have to store everything in the

问题 I need to multiply two big matrices and sort their columns. import numpy a= numpy.random.rand(1000000, 100) b= numpy.random.rand(300000,100) c= numpy.dot(b,a.T) sorted = [argsort(j)[:10] for j in c.T] This process takes a lot of time and memory. Is there a way to fasten this process? If not how can I calculate RAM needed to do this operation? I currently have an EC2 box with 4GB RAM and no swap. I was wondering if this operation can be serialized and I dont have to store everything in the