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BIDIAGONALIZATION

Bidiagonalization

Bidiagonalization is one of unitary (orthogonal) matrix decompositions such that U* A V = B, where U and V are unitary (orthogonal) matrices; * denotes

Bidiagonalization

Bidiagonal matrix

the singular value decomposition (SVD) uses this method as well. Bidiagonalization allows guaranteed accuracy when using floating-point arithmetic to

Bidiagonal matrix

Bidiagonal_matrix

L-curve

Visualization method for regularization

proposed approximating the L-curve and its curvature using Lanczos bidiagonalization, forming so-called L-ribbons and curvature-ribbons that bound the

L-curve

Computational complexity of mathematical operations

Algorithmic runtime requirements for common math procedures

{\displaystyle m\times n} matrix, & one n × n {\displaystyle n\times n} matrix Bidiagonalization and QR algorithm O ( m 2 n ) {\displaystyle O{\mathord {\left(m^{2}n\right)}}}

Computational complexity of mathematical operations

Computational_complexity_of_mathematical_operations

Topics referred to by the same term

knowledge extraction and automated hypothesis generation Lanczos bidiagonalization (Lanczos algorithm) in linear algebra Lewy body dementia, an umbrella

LBD

Lanczos algorithm

Numerical eigenvalue calculation

a number of other restarted variations such as restarted Lanczos bidiagonalization. Another successful restarted variation is the Thick-Restart Lanczos

Lanczos algorithm

Lanczos_algorithm

SLEPc

cyclic matrix, that rely on EPS solvers. Specific solvers based on bidiagonalization such as Golub-Kahan-Lanczos and a thick-restarted variant. PEP is

SLEPc

List of numerical analysis topics

spaces Givens rotation Krylov subspace Block matrix pseudoinverse Bidiagonalization Cuthill–McKee algorithm — permutes rows/columns in sparse matrix to

List of numerical analysis topics

List_of_numerical_analysis_topics