How to avoid a for-loop in MATLAB, when performing a operation on each row in a very large matrix? -

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i using matlab. have very large sparse matrices, , want perform logical or bsxfun on each column of matrix. there single for loop in single operation of logical fill sparse matrix. here include sample function fake small data see trying do;

 function maskmat() graph_temp = round(rand(10,10)); tic; com_mat = round(rand(10,10)); com = round(rand(10,1)); ii=1:length(graph_temp)         com_mat(:,ii) = logical(com ~= com(ii));     %bsxfun works slower     %com_mat(:,ii) =  bsxfun(@ne,com,com(ii)); end toc; com_mat = graph_temp .* com_mat;

graph_temp , com_mat bigger around 1m rows , columns , code horribly slow for loop. there relevant question here on so, have not understood theory behind see if can apply solutions problem well.

i thinking either write mex c++ function or try sort of nested arrayfun each logical/bsxfun operation called subroutine of greater function avoid loop bottle neck.

i'm not sure followed code way. so, make sure, com_mat(ii,jj) equals com(ii) ~= com(jj)?

if try following options

com_mat = bsxfun( @ne, com, com' ); %' creates entire matrix @ once com_mat = graph_temp .* com_mat;  % masking

since dealing sparse matrices, why don't take advantage of it

[ii jj] = find( graph_temp ); [m n] = size( graph_temp ); com_mat = sparse( ii, jj, com(ii) ~= com(jj), m, n );

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