matrix-multiplication

I have two 3-dimensional arrays, the first two dimensions of which represent matrices and the last one counts through a parameterspace, as a simple example take A = repmat([1,2; 3,4], [1 1 4]); (but assume A(:,:,j) is different for each j ). How can one easily perform a per- j matrix multiplication of two such matrix-arrays A and B ? C = A; % pre-allocate, nan(size(A,1), size(B,2)) would be better but slower for jj = 1:size(A, 3) C(:,:,jj) = A(:,:,jj) * B(:,:,jj); end certainly does the job, but if the third dimension is more like 1e3 elements this is very slow since it doesn't use MATLAB's

I don't quite understand what makes matrix multiplication in C#/.NET (and even Java) so slow. Take a look at this benchmark ( source ): Trying to find an updated benchmark. C#'s integer and double performance is damn close to C++ compiled with MSVC++. 87% as fast for double and 99% as fast for 32-bit integer. Pretty damn good, I'd say. But then look at matrix multiplication. The gap widens to C# being about 19% as fast. This is a pretty huge discrepancy that I don't understand. Matrix multiplication is just a bunch of simple math. How is it getting so slow? Shouldn't it be roughly as fast as

I am getting a very strange value for my (1,1) entry for my BinvA matrix I am just trying to invert B matrix and do a (B^-1)A multiplication. I understand that when I do the calculation by hand my (1,1) is supposed to be 0 but instead I get 1.11022302e-16. How can I fix it? I know floating point numbers can't be represented to full accuracy but why is this giving me such an inaccurate response and not rounding to 0 is there any way I can make it more accurate? Her is my code: import numpy as np A = np.array([[2,2],[4,-1]],np.int) A = A.transpose() B = np.array([[1,3],[-1,-1]],np.int) B = B