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_algorithm_2_2(A, AT, t)

Notes

This algorithm is mainly for testing. It uses the 'ind' array in a way that is similar to its usage in algorithm 2.4. This algorithm 2.2 may be easier to test, so it gives a chance of uncovering bugs related to indexing which could have propagated less noticeably to algorithm 2.4.

Parameters

A : ndarray or other linear operator

A linear operator that can produce matrix products.

AT : ndarray or other linear operator

The transpose of A.

t : int, optional

A positive parameter controlling the tradeoff between accuracy versus time and memory usage.

Returns

g : sequence

A non-negative decreasing vector such that g[j] is a lower bound for the 1-norm of the column of A of jth largest 1-norm. The first entry of this vector is therefore a lower bound on the 1-norm of the linear operator A. This sequence has length t.

ind : sequence

The ith entry of ind is the index of the column A whose 1-norm is given by g[i]. This sequence of indices has length t, and its entries are chosen from range(n), possibly with repetition, where n is the order of the operator A.

This is Algorithm 2.2.

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/sparse/linalg/_onenormest.py#220
type: <class 'function'>
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