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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.

Parameters

max_rank : int

Maximum rank of this matrix after reduction.

to_retain : int, optional

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.

Examples

See :

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them


GitHub : /scipy/optimize/_nonlin.py#712
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