Solving Unconstrained Optimization Problems with Some Three-term Conjugate Gradient Methods

IF 0.7 Q2 MATHEMATICS
Ladan Arman
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引用次数: 0

Abstract

In this paper, based on the efficient Conjugate Descent ({\tt CD}) method, two generalized {\tt CD}algorithms are proposed to solve the unconstrained optimization problems.These methods are three-term conjugate gradient methods which the generateddirections by using the conjugate gradient parameters and independent of the line searchsatisfy in the sufficient descent condition. Furthermore, under the strong Wolfe line search,the global convergence of the proposed methods are proved. Also, the preliminary numericalresults on the {\tt CUTEst} collection are presented to show effectiveness of our methods.
用三项共轭梯度法求解无约束优化问题
本文在高效共轭下降法({\tt CD})的基础上,提出了求解无约束优化问题的两种广义{\tt CD}算法。这些方法是利用共轭梯度参数生成方向的三项共轭梯度方法,不依赖于直线搜索,满足充分下降条件。在强Wolfe线搜索条件下,证明了所提方法的全局收敛性。此外,还给出了{\tt CUTEst}集合的初步数值结果,以显示我们的方法的有效性。
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
CiteScore
1.50
自引率
0.00%
发文量
11
期刊介绍: To promote research interactions between local and overseas researchers, the Department has been publishing an international mathematics journal, the Tamkang Journal of Mathematics. The journal started as a biannual journal in 1970 and is devoted to high-quality original research papers in pure and applied mathematics. In 1985 it has become a quarterly journal. The four issues are out for distribution at the end of March, June, September and December. The articles published in Tamkang Journal of Mathematics cover diverse mathematical disciplines. Submission of papers comes from all over the world. All articles are subjected to peer review from an international pool of referees.
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