用高效共轭梯度类解决大规模无约束优化问题

IF 1.3 4区数学 Q1 MATHEMATICS

Journal of Mathematics Pub Date : 2024-02-05 DOI:10.1155/2024/5548724

Sanaz Bojari, Mahmoud Paripour

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引用次数: 0

摘要

本文的主要目标是引入一个合适的共轭梯度类来解决无约束优化问题。该类具有三个自由参数、方向为下降、满足戴-廖共轭条件等优点。在弱沃尔夫-鲍威尔线搜索技术下，证明了新类的全局收敛特性。在三组实验（包括 210 个测试问题和 11 种不同的共轭梯度方法）中，证实了所提出类别的数值效率。

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Solving Large-Scale Unconstrained Optimization Problems with an Efficient Conjugate Gradient Class

The main goal of this paper is to introduce an appropriate conjugate gradient class to solve unconstrained optimization problems. The presented class enjoys the benefits of having three free parameters, its directions are descent, and it can fulfill the Dai–Liao conjugacy condition. Global convergence property of the new class is proved under the weak-Wolfe–Powell line search technique. Numerical efficiency of the proposed class is confirmed in three sets of experiments including 210 test problems and 11 disparate conjugate gradient methods.

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来源期刊

Journal of Mathematics Mathematics-General Mathematics

CiteScore

2.50

自引率

14.30%

发文量

期刊介绍： Journal of Mathematics is a broad scope journal that publishes original research articles as well as review articles on all aspects of both pure and applied mathematics. As well as original research, Journal of Mathematics also publishes focused review articles that assess the state of the art, and identify upcoming challenges and promising solutions for the community.