一种求解极大极小问题的灵活非单调滤子方法  

A nonmonotone flexible filter method for minimax problems

作　　者：苏珂[1] 林雨萌 李小川 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)

机构地区：[1]河北大学数学与信息科学学院,河北省机器学习与计算智能重点实验室,河北保定071002

出　　处：《河北大学学报（自然科学版）》2020年第6期561-568,共8页Journal of Hebei University(Natural Science Edition)

基　　金：河北省自然科学基金资助项目(A2018201172);河北省教育厅重点科研基金资助项目(ZD2015069);河北大学研究生创新项目(hbu2020ss043)。

摘　　要：求解极大极小问题的灵活非单调滤子方法与传统的滤子方法相比,对于试探步的可接受性,该方法具有更大的灵活性,而且与单调型方法相比,计算量更小.此外,还利用一个自适应参数来调整接受准则,从而在一定程度上避免了Maratos效应.在合理的假设下,该算法具有全局收敛性,并且通过数值实验验证了该方法的有效性.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 certaindegree.Under reasonable assumptions,the proposed algorithm is globally convergent.Numerical tests are presented that confirm the efficiency of the approach.

关 键 词：灵活滤子方法 极大极小问题 非单调 信赖域 全局收敛 

分 类 号：O221.2[理学—运筹学与控制论]