matrix-multiplication

问题 I recently moved to Python 3.5 and noticed the new matrix multiplication operator (@) sometimes behaves differently from the numpy dot operator. In example, for 3d arrays: import numpy as np a = np.random.rand(8,13,13) b = np.random.rand(8,13,13) c = a @ b # Python 3.5+ d = np.dot(a, b) The @ operator returns an array of shape: c.shape (8, 13, 13) while the np.dot() function returns: d.shape (8, 13, 8, 13) How can I reproduce the same result with numpy dot? Are there any other significant

问题 I am trying to find the most efficient implementation of 4x4 matrix (M) multiplication with a vector (u) using SSE. I mean Mu = v. As far as I understand there are two primary ways to go about this: method 1) v1 = dot(row1, u), v2 = dot(row2, u), v3 = dot(row3, u), v4 = dot(row4, u) method 2) v = u1 col1 + u2 col2 + u3 col3 + u4 col4. Method 2 is easy to implement in SSE2. Method 1 can be implement with either the horizontal add instruction in SSE3 or the dot product instruction in SSE4.

问题 I've stumbled upon the weird way (in my view) that Matlab is dealing with empty matrices. For example, if two empty matrices are multiplied the result is: zeros(3,0)*zeros(0,3) ans = 0 0 0 0 0 0 0 0 0 Now, this already took me by surprise, however, a quick search got me to the link above, and I got an explanation of the somewhat twisted logic of why this is happening. However , nothing prepared me for the following observation. I asked myself, how efficient is this type of multiplication vs

问题 Can someone tell me the Complexity of the Addition & Subtraction for the Divide & Conquer Matrix Multiplication algorithm? I know that the complexities of addition and subtraction operations of the Classic matrix multiplication are (n^3-n^2) while Strassen’s is 6n^2.81 – 6n^2... but I can't seem to find the Divide & Conquer anywhere. Just figure if anyone would know, you guys would. Thanks 回答1: This might help. See the introduction section before the Strassen's Method. 来源： https:/

问题 In numpy, I have an array of N 3x3 matrices. This would be an example of how I'm storing them (I'm abstracting away the contents): N = 10 matrices = np.ones((N, 3, 3)) I also have an array of 3-vectors, this would be an example: vectors = np.ones((N, 3)) I can't seem to figure out how to multiply those via numpy, so as to achieve something like this: result_vectors = [] for matrix, vector in zip(matrices, vectors): result_vectors.append(matrix @ vector) with the result_vector 's shape (upon

问题 i have 2 lists a=[[2,3,5],[3,6,2],[1,3,2]] b=[4,2,1] i want the output to be: c=[[8,12,20],[6,12,4],[1,3,2]] At present i am using the following code but its problem is that the computation time is very high as the number of values in my list are very large.The first list of list has 1000 list in which each list has 10000 values and the second list has 1000 values.Therefore the computation time is a problem.I want a new idea in which computation time is less.The present code is: a=[[2,3,5],[3

问题 I feel matrix operations in R is very confusing: we are mixing row and column vectors. Here we define x1 as a vector, (I assume R default vector is a column vector? but it does not show it is arranged in that way.) Then we define x2 is a transpose of x1 , which the display also seems strange for me. Finally, if we define x3 as a matrix the display seems better. Now, my question is that, x1 and x2 are completely different things (one is transpose of another), but we have the same results here.

问题 I am writing an application to multiply matrices. This works nicely as intended for matrices a and b that are nxn: for(k = 0; k < n; k++) { for(i = 0; i < n; i++) { tmp = a[i][k]; for(j = 0; j < n; j++) { c[i][j] = c[i][j] + tmp * b[k][j]; } } } If a was nxy and b was yxm (implying c to be nxm). How would I modify the above loop to work? Thanks 回答1: This should work: for(k = 0; k < y; k++) { for(i = 0; i < n; i++) { tmp = a[i][k]; for(j = 0; j < m; j++) { c[i][j] = c[i][j] + tmp * b[k][j]; }

问题 Does Chisel support matrix operations such as addition, multiplication, transposition, etc.? If not, what is the best way to implement them? How about vectors? 回答1: Chisel does not support matrix operations. It is a DSL for writing hardware generators that implement of such operations. For examples of specialized math hardware generators see: Hwacha: A hardware vector unit and DspTools: a set of math tools 回答2: Yes, you can do matrix operations in Chisel with the help of vectors. The code I

问题 I am trying to do matrix multiplication using pthreads and creating one thread for each computation of each row instead of each element. Suppose there are two matrices A[M][K],B[K][N] . Where am I going wrong ? int A[M][K]; int B[K][N]; int C[][]; void *runner (void *param); struct v { int i; int j; }; pthread_t tid[M]; for (i = 0; i < M; i++) // It should create M threads { struct v *data = (struct v *) malloc (sizeof (struct v)); data->i = i; data->j = j; pthread_create (&tid[count], &attr,