num::SVDResult Struct Reference

Result of a Singular Value Decomposition: A = U * diag(S) * V^T. More...

#include <svd.hpp>

Public Attributes
Matrix	U
	mxr left singular vectors (columns orthonormal)
Vector	S
	r singular values in descending order
Matrix	Vt
	rxn right singular vectors (rows orthonormal)
idx	sweeps = 0
	Jacobi sweeps (full SVD only; 0 for randomized)
bool	converged
	Whether Jacobi converged (always true for randomized)

Detailed Description

Result of a Singular Value Decomposition: A = U * diag(S) * V^T.

Definition at line 41 of file svd.hpp.

Member Data Documentation

converged

bool num::SVDResult::converged

Initial value:

=
        false

Whether Jacobi converged (always true for randomized)

Definition at line 46 of file svd.hpp.

S

Vector num::SVDResult::S

r singular values in descending order

Definition at line 43 of file svd.hpp.

Referenced by num::svd_truncated().

sweeps

idx num::SVDResult::sweeps = 0

Jacobi sweeps (full SVD only; 0 for randomized)

Definition at line 45 of file svd.hpp.

U

Matrix num::SVDResult::U

mxr left singular vectors (columns orthonormal)

Definition at line 42 of file svd.hpp.

Referenced by num::svd_truncated().

Vt

Matrix num::SVDResult::Vt

rxn right singular vectors (rows orthonormal)

Definition at line 44 of file svd.hpp.

Referenced by num::svd_truncated().

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The documentation for this struct was generated from the following file:

• /home/runner/work/numerics/numerics/include/linalg/svd/svd.hpp