A gradient-related algorithm with inexact line searches

Authors:
Zhen-Jun Shi;Jie Shen
Affiliations:
College of Operations Research and Management, Qufu Normal University (Rizhao Campus), Rizhao, Shandong 276826, PRC and Inst. of Comput. Math. and Sci./Eng. Comput., Chinese Acad. Sci., P.O. Box 2 ...;Department of Computer & Information Science, University of Michigan, Dearborn, MI
Venue:
Journal of Computational and Applied Mathematics
Year:
2004

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Abstract

In this paper, a new gradient-related algorithm for solving large-scale unconstrained optimization problems is proposed. The new algorithm is a kind of line search method. The basic idea is to choose a combination of the current gradient and some previous search directions as a new search direction and to find a step-size by using various inexact line searches. Using more information at the current iterative step may improve the performance of the algorithm. This motivates us to find some new gradient algorithms which may be more effective than standard conjugate gradient methods. Uniformly gradient-related conception is useful and it can be used to analyze global convergence of the new algorithm. The global convergence and linear convergence rate of the new algorithm are investigated under diverse weak conditions. Numerical experiments show that the new algorithm seems to converge more stably and is superior to other similar methods in many situations.