GLOBAL CONVERGENCE OF THE TMR METHOD FOR UNCONSTRAINED OPTIMIZATION PROBLEMS

T. Bouali, M. Belloufi, R. Guefaifia
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Abstract

Conjugate gradient methods are probably the most famous iterative methods for solving large scale optimization problems in scientific and engineering computation, characterized by the simplicity of their iteration and their low memory requirements. It is well known that the search direction plays a main role in the line search method. In this paper, we propose a new search direction with the Wolfe line search technique for solving unconstrained optimization problems. Under the above line searches and some assumptions, the global convergence properties of the given methods are discussed. Numerical result shows that the proposed formula is superior and more efficient when compared to other CG coefficients. AMS Subject Classification: 90C26, 65H10
无约束优化问题的TMR方法的全局收敛性
共轭梯度法可能是科学和工程计算中解决大规模优化问题最著名的迭代方法,其特点是迭代简单,内存要求低。众所周知,搜索方向在直线搜索方法中起着主要作用。在本文中,我们提出了一个新的搜索方向,用Wolfe线搜索技术来解决无约束优化问题。在上述线性搜索和一些假设条件下,讨论了给定方法的全局收敛性。数值结果表明,与其他CG系数相比,该公式具有更高的效率和优越性。AMS受试者分类:90C26、65H10
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