A robust and efficient parallel SVD solver based on restarted Lanczos bidiagonalization

zbMath1171.65382MaRDI QIDQ836790

Vicente G. Hernández, Andrés E. Tomás, Jose E. Roman

Publication date: 8 September 2009

Published in: ETNA. Electronic Transactions on Numerical Analysis (Search for Journal in Brave)

Full work available at URL: https://eudml.org/doc/130663

zbMATH Keywords

parallel computingLanczos bidiagonalizationpartial singular value decompositionlarge sparse matrixreorthogonalizationthick restart

Mathematics Subject Classification ID

Computational methods for sparse matrices (65F50) Numerical computation of eigenvalues and eigenvectors of matrices (65F15) Numerical solutions to overdetermined systems, pseudoinverses (65F20) Orthogonalization in numerical linear algebra (65F25)

Related Items (10)

Sparse data-driven quadrature rules via \(\ell^p\)-quasi-norm minimization ⋮ Approximate partitioned method of snapshots for POD ⋮ A literature survey of matrix methods for data science ⋮ A Krylov-Schur approach to the truncated SVD ⋮ Thick-restarted joint Lanczos bidiagonalization for the GSVD ⋮ Literature survey on low rank approximation of matrices ⋮ A harmonic Lanczos bidiagonalization method for computing interior singular triplets of large matrices ⋮ An implicitly restarted Lanczos bidiagonalization method with refined harmonic shifts for computing smallest singular triplets ⋮ PRIMME_SVDS: A High-Performance Preconditioned SVD Solver for Accurate Large-Scale Computations ⋮ On Inner Iterations of Jacobi--Davidson Type Methods for Large SVD Computations

Uses Software

• SLEPc
• PETSc
• PROPACK
• JDQR
• SparseMatrix
• JDQZ

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