[1]毕培培,徐玲玲,韩德仁.带BB步长的自适应投影法解广义纳什均衡问题[J].南京师范大学学报(自然科学版),2014,37(04):31.
 Bi Peipei,Xu Lingling,Han Deren.A Self-Adaptive Projection Method with the BB-Step Sizes forSolving Generalized Nash Equilibrium Problems[J].Journal of Nanjing Normal University(Natural Science Edition),2014,37(04):31.
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带BB步长的自适应投影法解广义纳什均衡问题()
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《南京师范大学学报》(自然科学版)[ISSN:1001-4616/CN:32-1239/N]

卷:
第37卷
期数:
2014年04期
页码:
31
栏目:
数学
出版日期:
2014-12-31

文章信息/Info

Title:
A Self-Adaptive Projection Method with the BB-Step Sizes forSolving Generalized Nash Equilibrium Problems
作者:
毕培培徐玲玲韩德仁
南京师范大学数学科学学院,江苏省大规模复杂系统数值模拟重点实验室,南京 210023
Author(s):
Bi PeipeiXu LinglingHan Deren
Jiangsu Key Laboratory for NSLSCS,School of Mathematical Sciences,Nanjing Normal University,Nanjing 210023,China
关键词:
广义纳什均衡问题拟变分不等式投影法BB步长收敛性
Keywords:
generalized nash equilibrium problemquasi-variational inequality(QVI)projection methodBB-step sizesconvergence
分类号:
O242
文献标志码:
A
摘要:
广义纳什均衡问题是一种非合作博弈,其每个竞争者的策略集和目标函数都要依靠其他竞争者的策略.它在经济学、管理科学及交通运输等领域都有广泛的应用,但如何有效地求解广义纳什均衡问题仍然是备受关注的课题.本文提出了带有BB步长的自适应投影法求解广义纳什均衡问题:首先,把广义纳什均衡问题转化成拟变分不等式问题,然后把BB步长推广到求解拟变分不等式问题上,并在函数余强制条件下证明了算法的全局收敛性.数值结果进一步说明该方法的有效性.
Abstract:
The generalized Nash equilibrium problem(GNEP)is a noncooperative game in which the strategy set of each player,as well as his payoff function,depend on the rival players’ strategies.It can be widely used in economics,management sciences and traffic assignment,but how to effective solve the generalized Nash equilibrium problem is still a subject of concern.In this paper,we present a self-adaptive projection method with the BB-step sizes for solving generalized Nash equilibrium problems:First,we give the reformulation of a generalized Nash equilibrium.Then,we extend the BB-step sizes to the QVI formulation of the GNEP,we adopt them in projection methods,and prove that under the condition that the underlying function is co-coercive,the sequence generated by the method converges to a solution of the quasi-variational inequality problem globally.Some preliminary computational results are reported,which illustrate that the new method is efficient.

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备注/Memo

备注/Memo:
收稿日期:2013-09-16.
基金项目:国家自然科学基金(11071122)、江苏省自然科学基金(BK2009397)、江苏省高校自然科学研究项目(13KJD110007).
通讯联系人:徐玲玲,博士,讲师,研究方向:最优化理论与算法.E-mail:xulingling@njnu.edu.cn
更新日期/Last Update: 2014-12-31