It seems like sparsity does not make a difference in the initial benchmark tests. In the plots below, the x-axis is $n$ (number of columns of $C$), and the y-axis is time. With RQ_SPARSE (d) I mean RQ_SPARSE supplied with the matrices converted to regular matrices/vectors.
Both $C$ and $Q$ are sparse here (using sprand(..., sparsity)), but $b$ does not have a lot of zeros.
Supplying dense or sparse matrices to RQ_SPARSE and RQ_EIG does not seem to make a big difference.
RQ_SPARSE seems to perform similarly to RQ_EIG
Do the matrices have to be even sparser to make a big difference?
It seems like sparsity does not make a difference in the initial benchmark tests. In the plots below, the x-axis is $n$ (number of columns of $C$), and the y-axis is time. With RQ_SPARSE (d) I mean RQ_SPARSE supplied with the matrices converted to regular matrices/vectors.
Both $C$ and $Q$ are sparse here (using sprand(..., sparsity)), but $b$ does not have a lot of zeros.
Do the matrices have to be even sparser to make a big difference?