Singular-value decomposition via gradient and self-equivalent flows

DOI10.1016/0024-3795(92)90180-IzbMath0763.65022MaRDI QIDQ1187396

John B. Moore, Uwe R. Helmke

Publication date: 23 July 1992

Published in: Linear Algebra and its Applications (Search for Journal in Brave)

zbMATH Keywords

singular value decompositionparallel computationcomplex matricesQR algorithmgradient flows

Mathematics Subject Classification ID

Numerical computation of eigenvalues and eigenvectors of matrices (65F15) Eigenvalues, singular values, and eigenvectors (15A18) Parallel numerical computation (65Y05)

Related Items (7)

Geometry of matrix decompositions seen through optimal transport and information geometry ⋮ Dimension reduction and construction of feature space for image pattern recognition ⋮ A decomposion theorem of motion ⋮ An application of Lie groups in distributed control networks ⋮ Extending the applicability of Newton's method on Lie groups ⋮ Comparison of Two Main Approaches to Joint SVD ⋮ On matrix differential equations and abstract FG algorithm

Cites Work

• Unnamed Item
• Dynamical systems that perform the singular value decomposition
• Balanced realizations via gradient flow techniques
• The QR algorithm and scattering for the finite nonperiodic Toda lattice
• Ordinary Differential Equations and the Symmetric Eigenvalue Problem
• A new formulation of the generalized Toda lattice equations and their fixed point analysis via the momentum map
• The Generalized Toda Flow, the QR Algorithm and the Center Manifold Theory
• The Projected Gradient Method for Least Squares Matrix Approximations with Spectral Constraints
• Self-Equivalent Flows Associated Decomposition

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