题     目：A restricted memory quasi-Newton bundle method for nonsmooth optimization on Riemannian manifolds

报 告 人：唐春明 教授

单     位：广西大学

时     间：2022年12月8日 10:00

地     点：腾讯718-950-304

摘 要：In this talk, a restricted memory quasi-Newton bundle method for minimizing a locally Lipschitz function over a Riemannian manifold is proposed, which extends the classical ones in Euclidean space to the manifold setting. The potential second order information of the objective function is approximated by applying the Riemannian versions of the quasi-Newton updating formulas. The subgradient aggregation technique is used to avoid solving the time-consuming quadratic programming subproblem when calculating the candidate descent direction.  Moreover, a new Riemannian line search procedure is proposed to generate the stepsizes. Global convergence of the proposed method is established: if the serious iteration steps are finite, then the last serious iteration is stationary; otherwise every accumulation point of the serious iteration sequence is stationary. Finally, some preliminary numerical results show that the proposed method is promising. 

报告人简介: 唐春明，广西大学数学与信息科学学院教授，博士，博士生导师，广西运筹学会副理事长，中国运筹学会理事，广西数学会常务理事。1998-2004年本、硕就读于广西大学，2008年博士毕业于上海大学，2014年到澳大利亚新南威尔士大学访学一年。目前主要研究非光滑优化算法。主持国家自然科学基金项目4项，广西自然科学基金项目3项（含广西杰青1项）。作为主要参与者获广西自然科学奖二等奖2项。在《 European Journal of Operational Research》《Journal of Optimization Theory and Applications》《Computational Optimization and Applications》《Optimization Letters》《Optimization》《Numerical Algorithms》《IEEE Transactions on Power Systems》《中国科学：数学》等重要刊物发表论文40余篇。