Computing the generalized singular values/vectors of large sparse or structured matrix pairs

DOI10.1007/s002110050175zbMath0856.65041OpenAlexW2000512102MaRDI QIDQ1923328

Hongyuan Zha

Publication date: 24 February 1997

Published in: Numerische Mathematik (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s002110050175

zbMATH Keywords

algorithmnumerical resultslinear least squares problemLanczos bidiagonalizationCS decompositiongeneralized singular valuessparse or structural matrix pair

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)

Related Items (15)

The joint bidiagonalization process with partial reorthogonalization ⋮ The Joint Bidiagonalization Method for Large GSVD Computations in Finite Precision ⋮ A Generalized CUR Decomposition for Matrix Pairs ⋮ An iterative method for Tikhonov regularization with a general linear regularization operator ⋮ Two harmonic Jacobi-Davidson methods for computing a partial generalized singular value decomposition of a large matrix pair ⋮ Two projection methods for regularized total least squares approximation ⋮ A cross-product free Jacobi-Davidson type method for computing a partial generalized singular value decomposition of a large matrix pair ⋮ A joint bidiagonalization based iterative algorithm for large scale general-form Tikhonov regularization ⋮ Thick-restarted joint Lanczos bidiagonalization for the GSVD ⋮ The Joint Bidiagonalization of a Matrix Pair with Inaccurate Inner Iterations ⋮ Tikhonov regularization via flexible Arnoldi reduction ⋮ Large sparse symmetric eigenvalue problems with homogeneous linear constraints: The Lanczos process with inner-outer iterations ⋮ On choices of formulations of computing the generalized singular value decomposition of a large matrix pair ⋮ A Jacobi-Davidson type method for the generalized singular value problem ⋮ Necessary Conditions and Tight Two-level Convergence Bounds for Parareal and Multigrid Reduction in Time

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