Hello,
I try to create a sparse symmetric random matrix according to some pattern using the command sprandsym. It appears to me, however, that sprandsym consumes a lot of memory during the construction of the matrix even if the final matrix needs much less memory. Obviously, I like to avoid this.
Here is a minimal example where I use two lists (each has 150 elements) to create two matrices, which at the end, by cutting away elements above a certain threshold, determine the pattern of the random matrix:
listA = readmatrix('data/listA.csv');
listB = readmatrix('data/listB.csv');
A = listA(:) - listA(:)';
B = listB(:) - listB(:)';
Pattern_A = sparse(A >= -threshold & A <= threshold & A ~= 0);
Pattern_B = sparse(B >= -threshold & B <= threshold & B ~= 0);
Pattern = kron(Pattern_A,Pattern_B);
Now, everything works fine until here and the code consumes a bit less than 3 GB. But then, the next line of the code is
During the computation of sprandsym the used memory grows up to 14 GB, but after execution of sprandsym the used memory is around 6.7 GB. This seems to indicate that only the execution of sprandsym seems to consume almost 10 GB extra memory, but I do not really understand why. Is there any explanation? And, ideally, is there any way to make it better?
If it helps, it turns out that the matrix Pattern is not extremely sparse (density of elements around 0.3). Maybe this explains it? I could reduce the density, say to 0.1, but I really like to avoid the extra use of memory during the execution of sprandsym.
Any help is greatly appreciated. Have a nice weekend!
