Title

Generalizing the GSVD

Authors

Mark Gockenbach, Michigan Technological UniversityFollow

Document Type

Article

Publication Date

8-18-2016

Abstract

The generalized singular value decomposition (GSVD) of a pair of matrices is the natural tool for certain problems defined on Euclidean space, such as certain weighted least-squares problems, the result of applying Tikhonov regularization to such problems (sometimes called regularization with seminorms), and equality-constrained least-squares problems. There is an extension of the GSVD to pairs of bounded linear operators defined on Hilbert space that turns out to be a natural representation for analyzing the same problems in the infinite-dimensional setting.

Publisher's Statement

© 2016, Society for Industrial and Applied Mathematics. Article deposited here in compliance with publisher policy. Publisher's version of record: https://epubs.siam.org/doi/10.1137/15M1019453

Publication Title

SIAM Journal on Numerical Analysis

Recommended Citation

Gockenbach, M. (2016). Generalizing the GSVD. SIAM Journal on Numerical Analysis, 54(4), 2517-2540. http://doi.org/10.1137/15M1019453
Retrieved from: https://digitalcommons.mtu.edu/math-fp/14

Version

Publisher's PDF