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Journal of Applied Mathematics and Computing
Erschienen in: Journal of Applied Mathematics and Computing 3/2022

13.07.2021 | Original Research

Two sufficient descent three-term conjugate gradient methods for unconstrained optimization problems with applications in compressive sensing

verfasst von: Yufeng Liu, Zhibin Zhu, Benxin Zhang

Erschienen in: Journal of Applied Mathematics and Computing | Ausgabe 3/2022

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Abstract

In this paper, we present two new three-term conjugate gradient methods which can generate sufficient descent directions for the large-scale optimization problems. Note that this property is independent of the line search used. We prove that these three-term conjugate gradient methods are global convergence under the Wolfe line search. Numerical experiments and comparisons demonstrate that the proposed algorithms are efficient approaches for test functions. Moreover, we use the proposed methods to solve the \(\ell _1-\alpha \ell _2\) regularization problem of sparse signal decoding in compressed sensing, and the results show that our methods have certain advantages over the existing solvers on such problems.
Vorheriger Artikel Correction to: On -skew cyclic codes
Nächster Artikel Iterative approximation of fixed points of a general class of non-expansive mappings in hyperbolic metric spaces

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Metadaten
Titel
Two sufficient descent three-term conjugate gradient methods for unconstrained optimization problems with applications in compressive sensing
verfasst von
Yufeng Liu
Zhibin Zhu
Benxin Zhang
Publikationsdatum
13.07.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
Journal of Applied Mathematics and Computing / Ausgabe 3/2022
Print ISSN: 1598-5865
Elektronische ISSN: 1865-2085
DOI
https://doi.org/10.1007/s12190-021-01589-8

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