The global and superlinear convergence of a new nonmonotone MBFGS algorithm on convex objective functions

Authors:
Liying Liu;Shengwei Yao;Zengxin Wei
Affiliations:
College of Mathematics Science, Liaocheng University, 252059, PR China;College of Mathematics and Information Science, Guangxi University, 530004, PR China;College of Mathematics and Information Science, Guangxi University, 530004, PR China
Venue:
Journal of Computational and Applied Mathematics
Year:
2008

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Abstract

In this paper, a new nonmonotone MBFGS algorithm for unconstrained optimization will be proposed. Under some suitable assumptions, the global and superlinear convergence of the new nonmonotone MBFGS algorithm on convex objective functions will be established. Some numerical experiments show that this new nonmonotone MBFGS algorithm is competitive to the MBFGS algorithm and the nonmonotone BFGS algorithm.