iddp_rsvd(eps, m, n, matvect, matvec)

parameps

Relative precision.

typeeps

float

paramm

Matrix row dimension.

typem

int

paramn

Matrix column dimension.

typen

int

parammatvect

Function to apply the matrix transpose to a vector, with call signature :None:None:`y = matvect(x)`, where :None:None:`x` and :None:None:`y` are the input and output vectors, respectively.

typematvect

function

parammatvec

Function to apply the matrix to a vector, with call signature :None:None:`y = matvec(x)`, where :None:None:`x` and :None:None:`y` are the input and output vectors, respectively.

typematvec

function

return

Left singular vectors.

rtype

numpy.ndarray

return

Right singular vectors.

rtype

numpy.ndarray

return

Singular values.

rtype

numpy.ndarray

Compute SVD of a real matrix to a specified relative precision using random matrix-vector multiplication.

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

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GitHub : /scipy/linalg/_interpolative_backend.py#671
type: <class 'function'>
Commit: