一种新的改进的具有充分下降性质的三项Hestenes-Stiefel共轭梯度法及其全局收敛性

IF 1 Q3 ENGINEERING, MULTIDISCIPLINARY
B. Baluch, Z. Salleh, Ahmad Alhawarat
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引用次数: 11

摘要

本文提出了一种改进的三项Hestenes-Stiefel (HS)方法。原始HS法是最早的共轭梯度法。虽然HS方法使用精确的线搜索实现全局收敛,但在不精确的线搜索情况下不能保证这一点。此外,HS方法通常不满足下降特性。本文改进的三项共轭梯度法具有充分的下降性质,无论线搜索的类型如何,都保证了非精确Wolfe-Powell线搜索的全局收敛性。用75个标准测试函数检验了改进的三项HS方法的数值效率。众所周知,三项共轭梯度法在数值上比两项共轭梯度法更有效。重要的是,本文量化了与两期方法相比,三期方法的性能有多好。因此,在数值结果中,我们将新的修正与一种有效的两项共轭梯度法进行了比较。我们还比较了我们的修改与最先进的三期HS方法。最后,我们得出结论,我们提出的修正是全局收敛的和数值有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A New Modified Three-Term Hestenes–Stiefel Conjugate Gradient Method with Sufficient Descent Property and Its Global Convergence
This paper describes a modified three-term Hestenes–Stiefel (HS) method. The original HS method is the earliest conjugate gradient method. Although the HS method achieves global convergence using an exact line search, this is not guaranteed in the case of an inexact line search. In addition, the HS method does not usually satisfy the descent property. Our modified three-term conjugate gradient method possesses a sufficient descent property regardless of the type of line search and guarantees global convergence using the inexact Wolfe–Powell line search. The numerical efficiency of the modified three-term HS method is checked using 75 standard test functions. It is known that three-term conjugate gradient methods are numerically more efficient than two-term conjugate gradient methods. Importantly, this paper quantifies how much better the three-term performance is compared with two-term methods. Thus, in the numerical results, we compare our new modification with an efficient two-term conjugate gradient method. We also compare our modification with a state-of-the-art three-term HS method. Finally, we conclude that our proposed modification is globally convergent and numerically efficient.
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来源期刊
Journal of Optimization
Journal of Optimization ENGINEERING, MULTIDISCIPLINARY-
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