Properties of subgradient projection iteration when applying to linear imaging system

In this paper, the subgradient projection iteration is used to find an approximation solution of a weighted least-squares problem with respect to linear imaging system. Instead of an exact or approximate line search in each iteration, the step length in this paper is fixed by the weighted least-squa...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Optimization letters Jg. 13; H. 6; S. 1285 - 1297
1. Verfasser: Wang, Caifang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2019
Schlagworte:
Computational Intelligence
Mathematics
Mathematics and Statistics
Numerical and Computational Physics
Operations Research/Decision Theory
Optimization
Original Paper
Simulation
Subgradient projection algorithm
Weighted singular value decomposition
Linear system
Convergence condition
Decreasing property
ISSN:1862-4472, 1862-4480
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In this paper, the subgradient projection iteration is used to find an approximation solution of a weighted least-squares problem with respect to linear imaging system. Instead of an exact or approximate line search in each iteration, the step length in this paper is fixed by the weighted least-square function and the current iteration. Using weighted singular value decomposition, we estimate the bounds of step length. Consequently, we provide the decreasing property and the sufficient condition for convergence of the iterative algorithm. Furthermore, we perform a numerical experiment on a two dimensional image reconstruction problem to confirm the validity of this subgradient projection iteration.
ISSN:1862-4472
1862-4480
DOI:10.1007/s11590-018-1321-3