MATLAB: Memory usage in sparse matrix

memorysparse

Hi, Sparse matrices store only the nonzero elements and their position indices. Using sparse matrices can significantly reduce the amount of memory required for data storage. But surprisingly I noticed that my computer runs out of memory when I try something like >>sparse(zeros(80000)) or a matrix having more elements.In zeros(), as there is no non-zero element, the sparse matrix should not have used any memory in this case at all! I would really appreciate if anyone can elucidate this issue… thanks.

Best Answer

Try just:

 >> sparse(80000,80000)

to avoid the temporary allocation of a 80000^2 non-sparse zeros array.

Related Solutions

MATLAB: How to shrink the memory footprint of a sparse matrix to its minimum

A workaround to free unused memory in 'X' is to first construct a temporary sparse matrix 'Y', with the exact number of non-zero elements needed, second, copy the elements of 'X' into 'Y', and third, delete 'X'. The number of non-zero elements can be found using the function 'nnz'.

For example,

X = spalloc(1e6,1e6,1e4); 
X(1,1) = 1; 
Y = spalloc( size(X,1) , size(X,2) , nnz(X) ); % constructs the temporary sparse matrix of minimal memory 
Y(:,:) = X(:,:); % element wise assignment
clear X;

This workaround will require sufficient memory in the first instance to construct the temporary sparse matrix 'Y'.

MATLAB: What’s the reason for different memory consumption for sparse row and column vectors

% The minimum data storage requirement formula for a double m x n sparse 
% matrix with nnz non-zero elements, including the index data, is as 
% follows on a 32-bit system:
% 


% max(nnz(x),1) * (4 + 8) + (size(x,2)+1)*4;  % 32-bit
% max(nnz(x),1) * (8 + 8) + (size(x,2)+1)*8;  % 64-bit
% 
% nnz * 4 = Storing the row index of the non-zero elements
% nnz * 8 = Storing the non-zero double element values themselves
% (n+1)*4 = Storing the cumulative number of non-zero elements thru column
% 
% For 64-bit systems using 8-byte integers for the indexing you can replace
% the 4's above with 8's.

Best Answer

Related Solutions

MATLAB: How to shrink the memory footprint of a sparse matrix to its minimum

MATLAB: What’s the reason for different memory consumption for sparse row and column vectors

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