An efficient modified conjugate gradient algorithm under Wolfe conditions with applications in compressive sensing

IF 2.1 2区数学 Q1 MATHEMATICS, APPLIED

Journal of Computational and Applied Mathematics Pub Date : 2024-10-28 DOI:10.1016/j.cam.2024.116335

Zhibin Zhu , Jiaqi Huang , Ying Liu , Yuehong Ding

引用次数: 0

Abstract

This paper presents a new modified conjugate gradient (NMCG) algorithm which satisfies the sufficient descent property under any line search for unconstrained optimization problems. We analyze that the algorithm is global convergence under the Wolfe line search. We use the proposed algorithm NMCG to unconstrained optimization problems to prove its effectiveness. Furthermore, we also extend it to solve image restoration and sparse signal recovery problems in compressive sensing, and the results indicate that our algorithm is effective and competitive.

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沃尔夫条件下的高效修正共轭梯度算法在压缩传感中的应用

本文提出了一种新的修正共轭梯度（NMCG）算法，该算法在无约束优化问题的任意线搜索下都满足充分下降特性。我们分析了该算法在沃尔夫线搜索下的全局收敛性。我们将所提出的 NMCG 算法用于无约束优化问题，以证明其有效性。此外，我们还将其扩展用于解决压缩传感中的图像复原和稀疏信号恢复问题，结果表明我们的算法是有效和有竞争力的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Computational and Applied Mathematics 数学-应用数学

CiteScore

5.40

自引率

4.20%

发文量

437

审稿时长

3.0 months

期刊介绍： The Journal of Computational and Applied Mathematics publishes original papers of high scientific value in all areas of computational and applied mathematics. The main interest of the Journal is in papers that describe and analyze new computational techniques for solving scientific or engineering problems. Also the improved analysis, including the effectiveness and applicability, of existing methods and algorithms is of importance. The computational efficiency (e.g. the convergence, stability, accuracy, ...) should be proved and illustrated by nontrivial numerical examples. Papers describing only variants of existing methods, without adding significant new computational properties are not of interest. The audience consists of: applied mathematicians, numerical analysts, computational scientists and engineers.