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                    预告:Wah June Leong: Proximal Algorithm for Two-block Nonsmooth and Nonconvex Optimization Problems and Its Application in the Design of Sparse-enhanced Control

                    分享到:
                    2019-04-11 来源:数学与统计学院

                    报告承办单位:数学与统计学院

                    报告题目: Proximal Algorithm for Two-block Nonsmooth and Nonconvex Optimization Problems and Its Application in the Design of Sparse-enhanced Control

                    报告人姓名: Wah June Leong

                    报告人所在单位: 马来西亚博特拉大学

                    报告人职称/职务及学术头衔:副教授/博导

                    报告时间:201941210:00—11:00

                    报告地点:  金盆岭1A-406

                    报告人简介: Wah June Leong,马来西亚博特拉大学副教授,于2003年在马来西亚博特拉大学获得博士学位,2008-2009年在中国科学院数学与系统科学研?#20811;?#36827;行博士后研究,合作导师戴彧虹研究员。2015-2018年期间先后访问澳大利亚科廷大学、首尔大学、重庆师范大学、东北大学、以及中国科学院。Wah June Leong老师研究的主要方向为大规模优化问题的数值算法以及带非光滑优化的最优控制问题,已发表论文80余篇,主持马来西亚教育部和科技部项目6项,指导博士后2名,培养博士?#36864;?#22763;研究生15名。

                    报告摘要:This talk begins by introducing a proximal alternating linearized minimization algorithm for solving a broad class of nonsmooth and nonconvex minimization problems. Building on the Kurdyka-Lojasiewicz property, we derive a convergence analysis framework and establish that each bounded sequence generated by the algorithm converges to a critical point of the problem. As an illustration of the results, we give a formulation to design controllers of linear–quadratic regulator (LQR) control systems that can provide a desired trade-off between the system performance and the sparsity of the feedback matrix. The model formulation that involves nonsmooth-nonconvex l0-norm minimization problem is then solved by using our proximal algorithm.

                     

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