Preconditioned conjugate gradient methods for the solution of indefinite least squares problems

DOI10.1007/s10092-011-0039-8zbMath1230.65052OpenAlexW2020199282MaRDI QIDQ652558

Qiaohua Liu, Xianjuan Li

Publication date: 14 December 2011

Published in: Calcolo (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10092-011-0039-8

zbMATH Keywords

convergencenumerical resultsconjugate gradient methodpreconditionerill-conditioned problemsindefinite least squares problemssparse QR factorization

Mathematics Subject Classification ID

Numerical solutions to overdetermined systems, pseudoinverses (65F20) Ill-posedness and regularization problems in numerical linear algebra (65F22) Preconditioners for iterative methods (65F08)

Related Items (8)

Backward error and condition number analysis for the indefinite linear least squares problem ⋮ A contribution to the conditioning theory of the indefinite least squares problems ⋮ On the partial condition numbers for the indefinite least squares problem ⋮ Splitting-based randomized iterative methods for solving indefinite least squares problem ⋮ On mixed and componentwise condition numbers for indefinite least squares problem ⋮ Incomplete hyperbolic Gram-Schmidt-based preconditioners for the solution of large indefinite least squares problems ⋮ On the condition number theory of the equality constrained indefinite least squares problem ⋮ USSOR method for solving the indefinite least squares problem

Uses Software

• VanHuffel
• CSparse

Cites Work

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• Preconditioners for least squares problems by LU factorization
• The university of Florida sparse matrix collection
• Direct Methods for Sparse Linear Systems
• A Stable and Efficient Algorithm for the Indefinite Linear Least-Squares Problem
• Solving the Indefinite Least Squares Problem by Hyperbolic QR Factorization
• An Efficient Algorithm for a Bounded Errors-in-Variables Model
• Linear estimation in Krein spaces. I. Theory

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