基于强Wolfe线搜索的Hestenes-Stiefel共轭梯度法的鲁棒修正

Awad Abdelrahman, Osman O. O. Yousif, KH. I. Osman
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引用次数: 2

摘要

非线性共轭梯度法(CG)广泛用于求解大规模无约束优化问题。为了改进(CG)方法,最近进行了大量的研究,构建了量表并进行了修改。本文提出了一种简单的共轭梯度法修正方法。此外,建立了在Strong Wolfe线搜索下的全局收敛性和充分下降条件。数值结果表明,与其他已知的CG参数相比,该公式具有一定的竞争力。
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A Robust Modification of Hestenes–Stiefel Conjugate Gradient Method with Strong Wolfe Line Search
Nonlinear Conjugate Gradient (CG) methods are extensively used for solving large-scale unconstrained optimization problems. Numerous of studies constructed scales and modifications have been conducted recently to improve (CG) methods. In this paper, a simple modified by its conjugate gradient method was proposed. In addition to, established global convergence property and sufficient descent condition, under Strong Wolfe line search. Numerical result shows that the proposed formula is competitive when compared to other well-known (CG) parameters.
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33.30%
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