北京航空航天大学韩德仁教授学术报告

发布者:周春宇发布时间:2022-08-03浏览次数:10


报告人:韩德仁(北京航空航天大学)

报告时间:202283日(周三)上午10:00-11:00

报告地点:腾讯会议,会议号316-769-156


报告题目:The indefinite proximal point algorithms for maximal monotone operators


报告摘要:The proximal point algorithm (PPA) has been widely used in convex optimization. Many algorithms fall into the framework of PPA. To guarantee the convergence of PPA, however, existing results conventionally need to ensure the positive definiteness of the corresponding proximal measure. In some senses, this essentially results in tiny step sizes (or over regularization) for the subproblems and thus inevitably decelerates the overall convergence speed of PPA. In this talk, we report the possibility of relaxing the positive definiteness requirement of the proximal measure in PPA, where an indefinite PPA is constructed via choosing an indefinite proximal regularization term. We show that the condition which guarantees the convergence of the proposed indefinite PPA is tight by a simple example. In addition, we show how to apply the indefinite PPA to some convex models. We repot some preliminary numerical results, which show the efficiencies of the proposed algorithms.


报告人简介:韩德仁:教授,博士生导师,现任北京航空航天大学数学科学学院院长、教育部数学类专业教指委秘书长。2002年获南京大学计算数学博士学位。从事大规模优化问题、变分不等式问题的数值方法的研究工作,发表多篇学术论文。曾获中国运筹学会青年运筹学奖,江苏省科技进步奖等奖项;主持国家自然科学基金杰出青年基金等多项项目。担任中国运筹学会常务理事、江苏省运筹学会理事长;《数值计算与计算机应用》、《Journal of the Operations Research Society of China》、《Journal of Global Optimization》编委。


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