Double Precision is not Necessary for LSQR for Solving Discrete Linear Ill-Posed Problems

The growing availability and usage of low precision floating point formats attracts many interests of developing lower or mixed precision algorithms for scientific computing problems. In this paper we investigate the possibility of exploiting mixed precision computing in LSQR for solving discrete li...

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Bibliographic Details
Published in:	Journal of scientific computing Vol. 98; no. 3; p. 55
Main Author:	Li, Haibo
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.03.2024 Springer Nature B.V
Subjects:	Accuracy Algorithms Approximation Computation Computational Mathematics and Numerical Analysis Floating point arithmetic Ill posed problems Inverse problems Iterative methods Iterative solution Linear algebra Mathematical and Computational Engineering Mathematical and Computational Physics Mathematics Mathematics and Statistics Noise levels Number systems Regularization Regularization methods Robustness (mathematics) Theoretical 65G50 Semi-convergence 65F10 65F22 Roundoff unit Linear ill-posed problem LSQR Mixed precision Regularization
ISSN:	0885-7474, 1573-7691
Online Access:	Get full text
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