Sparse Matrix Assignment becomes very slow in Matlab

Question

I am filling a sparse matrix P (230k,290k) with values coming from a text file which I read line by line, here is the (simplified) code

while ...
            C = textscan(text_line,'%d','delimiter',',','EmptyValue', 0);
            line_number = line_number+1;
            P(line_number,:)=C{1};
end

the problem I have is that while at the beginning the

P(line_number,:)=C{1};

statement is fast, after a few thousands lines become exterely slow, I guess because Matlab need to find the memory space to allocate every time. Is there a way to pre-allocate memory with sparse matrixes? I don't think so but maybe I am missing something. Any other advise which can speed up the operation (eg having a lot of free RAM can make the difference?)

Answer 1

There's a sixth input argument to sparse that tells the number of nonzero elements in the matrix. That's used by Matlab to preallocate:

S = sparse(i,j,s,m,n,nzmax) uses vectors i , j , and s to generate an m -by- n sparse matrix such that S(i(k),j(k)) = s(k) , with space allocated for nzmax nonzeros.

So you could initiallize with

P = sparse([],[],[],230e3,290e3,nzmax);

You can make a guess about the number of nonzeros (perhaps checking file size?) and use that as nzmax . If it turns you need more nonzero elements in the end, Matlab will preallocate on the fly (slowly).

Answer 2

By far the fastest way to generate a sparse matrix wihtin matlab is to load all the values in at once, then generate the sparse matrix in one call to sparse . You have to load the data and arrange it into vectors defining the row and column indices and values for each filled cell. You can then call sparse using the S = sparse(i,j,s,m,n) syntax.