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Asked by Tamar Friedlander on 30 Jun 2013

I have a code in which I have to multiply many pairs of matrices. This part repeats many times so it is important to make it as efficient as possible.

Now I'm storing this as 2 3D matrices: for example:

A_Pop = rand(5,5,500); B_Pop = rand(5,5,500);

Now I'd like to multiply A(:,:,i)*B(:,:,i).

tic; for ii=1:500, C_Pop(:,:,ii) = A_Pop(:,:,ii) * B_Pop(:,:,ii); end; toc;

Elapsed time is 0.001915 seconds.

-----------------------------------------------------------------------------

Using cellfun or arrayfun was even slower than this loop.

multmat = @(x,y) x*y; tic; C = cellfun(multmat, A, B,'UniformOutput', false);toc;

Elapsed time is 0.003202 seconds.

-----------------------------------------------------------------------------

The most efficient way I found so far was to store A_Pop and B_Pop as blocks in a block-diagonal matrices, (and then store these huge matrices as sparse) and simply multiply the 2 matrices by each other:

A_block = []; B_block = []; for ii=1:500, A=rand(5,5); B=rand(5,5); A_block = blkdiag(A_block,A); B_block = blkdiag(B_block,B);end; A_bl = sparse(A_block); B_bl = sparse(B_block);

And then multiply:

tic; C_bl=A_bl*B_bl; toc

Elapsed time is 0.000425 seconds.

-----------------------------------------------------------------------------

Does anyone have an idea how to implement this more efficiently than the block-diagonal version?

That will be very helpful. Thanks, --Tamar.

Answer by the cyclist on 30 Jun 2013

Accepted answer

You could try this submission to the File Exchange: http://www.mathworks.com/matlabcentral/fileexchange/25977-mtimesx-fast-matrix-multiply-with-multi-dimensional-support

## 1 Comment

Direct link to this comment:http://www.mathworks.com/matlabcentral/answers/80692#comment_157646

This is incidental to your question, but looping over

is an unnecessarily slow and painful way to build a multi-block block diagonal sparse matrix. Better options are

(1)

(2)