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Journal of Scientific Computing

Erschienen in:

08.11.2016

Computing Singular Value Decompositions of Parameterized Matrices with Total Nonpositivity to High Relative Accuracy

verfasst von: Rong Huang, Delin Chu

Erschienen in: Journal of Scientific Computing | Ausgabe 2/2017

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Abstract

In the last years, much effort has been devoted to high relative accuracy algorithms for the singular value problem. However, such algorithms have been constructed only for a few classes of matrices with certain structure or properties. In this paper, we study a different class of matrices—parameterized matrices with total nonpositivity. We develop a new algorithm to compute singular value decompositions of such matrices to high relative accuracy. Our numerical results confirm the high relative accuracy of our algorithm.

Vorheriger Artikel Mixed-Type Galerkin Variational Principle and Numerical Simulation for a Generalized Nonlocal Elastic Model

Nächster Artikel Parallel Multi-Block ADMM with o(1 / k) Convergence

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Dr. Xiaowei Zhang did all numerical experiments in this section. The authors thank him for his kind assistance.

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Titel: Computing Singular Value Decompositions of Parameterized Matrices with Total Nonpositivity to High Relative Accuracy
verfasst von: Rong Huang
Delin Chu
Publikationsdatum: 08.11.2016
Verlag: Springer US
Erschienen in: Journal of Scientific Computing / Ausgabe 2/2017
Print ISSN: 0885-7474
Elektronische ISSN: 1573-7691
DOI: https://doi.org/10.1007/s10915-016-0315-5

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