MATLAB Mathematics - SERC - Index of

6 Sparse Matrices
Manipulating Sparse Matrices
Because sparse matrices are stored in a column-major format, accessing the
matrix by columns is more efficient than by rows. Compare the time required
for adding rows of a matrix 1000 times
S = sparse(10000,10000,1);
tic;
for n = 1:1000
A = S(100,:) + S(200,:);
end;
toc
Elapsed time is 1.208162 seconds.
versus the time required for adding columns
S = sparse(10000,10000,1);
tic;
for n = 1:1000
B = S(:,100) + S(:,200);
end;
toc
Elapsed time is 0.088747 seconds.
When possible, you can transpose the matrix, perform operations on the
columns, and then retranspose the result:
S = sparse(10000,10000,1);
tic;
for n = 1:1000
A = S(100,:)' + S(200,:)';
A = A';
end;
toc
Elapsed time is 0.597142 seconds.
The time required to transpose the matrix is negligible. Note that the sparse
matrix memory requirements may prevent you from transposing a sparse
matrix having a large number of rows. This might occur even when the number
of nonzero values is small.
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