matrix-multiplication

I have two numpy arrays, like A: = array([[0, 1], [2, 3], [4, 5]]) B = array([[ 6, 7], [ 8, 9], [10, 11]]) For each row of A and B, say Ra and Rb respectively, I want to calculate transpose(Ra)*Rb. So for given value of A and B, i want following answer: array([[[ 0, 0], [ 6, 7]], [[ 16, 18], [ 24, 27]], [[ 40, 44], [ 50, 55]]]) I have written the following code to do so: x = np.outer(np.transpose(A[0]), B[0]) for i in range(1,len(A)): x = np.append(x,np.outer(np.transpose(A[i]), B[i]),axis=0) Is there any better way to do this task. You can use extend dimensions of A and B with np.newaxis/None

There are several references to this around the web, including from stackoverflow. I have an unproject method which returns x,y coordinates which are returning in range of -1 and 1. Im wondering if these values are correct. If so, then what do i do with these values, multiply b the cameras position? Reference: How to convert mouse coordinate on screen to 3D coordinate Updated I found a bug in my matrix library, where I was missing some matrix components, I think it was in the multiplication method. After fixing this, I am no longer getting the -1 to 1 range. Now my data (freshly returned from

I am currently working on a C program trying to compute Matrix Multiplication.. I have approached this task by looping through each column of the second matrix as seen below. I have set size to 1000. for(i=0;i<size;i++) { for(j=0;j<size;j++) { for(k=0;k<size;k++) { matC[i][j]+=matA[i][k]*matB[k][j]; } } } I wanted to know what problematic access pattern is in this implementation.. What makes row/column access more efficient than the other? I am trying to understand this in terms of logic from the use of Caches.. Please help me understand this. Your help is much appreciated :) If you are

I have stumbled upon a problem, which requires me to calculate the nth Tetranacci Number in O(log n). I have seen several solutions for doing this for Fibonacci Numbers I was looking to follow a similar procedure (Matrix Multiplication/Fast Doubling) to achieve this, but I am not sure how to do it exactly (take a 4 by 4 matrix and 1 by 4 in a similar fashion doesn't seem to work). With dynamic programming/general loops/any other basic idea, I am not able to achieve sub-linear runtime. Any help appreciated! From the OEIS , this is the (1,4) entry of the nth power of 1 1 0 0 1 0 1 0 1 0 0 1 1 0

I've spent a couple weeks on this issue and can't seem to find a proper solution and need some advice. I'm working on creating a Camera class using LWJGL/Java, and am using Quaternions to handle bearing (yaw), pitch and roll rotations. I'd like this camera to handle all 6 degrees of movement in 3D space, and roll. Bearing, Pitch and Roll are all quaternions. I multiply them into a 'change' quaternion, and create a translation matrix from that. I put that in a float buffer, and multiply the modelview matrix by my buffer containing the rotation matrix. I can get the bearing and pitch rotations

Strassen's algorithm for matrix multiplication just gives a marginal improvement over the conventional O(N^3) algorithm. It has higher constant factors and is much harder to implement. Given these shortcomings, is strassens algorithm actually useful and is it implemented in any library for matrix multiplication? Moreover, how is matrix multiplication implemented in libraries? Generally Strassen’s Method is not preferred for practical applications for following reasons. The constants used in Strassen’s method are high and for a typical application Naive method works better. For Sparse matrices,