svd_reduce(self, max_rank, to_retain=None)
This corresponds to the "Broyden Rank Reduction Inverse" algorithm described in .
Note that the SVD decomposition can be done by solving only a problem whose size is the effective rank of this matrix, which is viable even for large problems.
Maximum rank of this matrix after reduction.
Number of SVD components to retain when reduction is done (ie. rank > max_rank). Default is max_rank - 2
.
Reduce the rank of the matrix by retaining some SVD components.
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