A nonmonotone flexible filter method for minimax problems

doi:10.3969/j.issn.1000-1565.2020.06.001

Journal of Hebei University(Natural Science Edition) ›› 2020, Vol. 40 ›› Issue (6): 561-568.DOI: 10.3969/j.issn.1000-1565.2020.06.001

A nonmonotone flexible filter method for minimax problems

SU Ke, LIN Yumeng, LI Xiaochuan

Key Laboratory of Machine Learning and Computational Intelligence of Hebei Province, College of Mathematics and Information Science, Hebei University, Baoding 071002, China

Received:2019-04-14 Published:2021-01-10
Supported by:
National Natural Science Foundation of China, No.41571384, No.71433008

Abstract

Abstract: A nonmonotone flexible filter method for minimax problems is proposed. This new method has more flexibility for the acceptance of the trial step compared to the traditional filter methods, and requires less computational costs compared with the monotone-type methods. Moreover, we use a self-adaptive parameter to adjust the acceptance criteria, so that Maratos effect can be avoided to a certain degree. Under reasonable assumptions, the proposed algorithm is globally convergent. Numerical tests are presented that confirm the efficiency of the approach.

Key words: flexible filter method, minimax problem, nonmonotone, trust region, global convergence

CLC Number:

O221.2

SU Ke, LIN Yumeng, LI Xiaochuan. A nonmonotone flexible filter method for minimax problems[J]. Journal of Hebei University(Natural Science Edition), 2020, 40(6): 561-568.

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A nonmonotone flexible filter method for minimax problems

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