报告题目：PROXIMAL METHOD FOR NORM BASED SPARSE ENHANCED CONTROL PROBLEMS IN LARGE-SCALE INTERCONNECTED SYSTEMS

主 讲 人：吴昌质教授

 报告时间：2019年10月20日 8：30—10：00

报告地点：数理楼221

报告摘要：In this talk, we will study a linear quadratic optimal control problem with large-scale interconnected systems. An algorithmic framework is constructed to design controllers that provide a desired tradeoff between the system performance and the sparsity of the static feedback matrix. This is accomplished by introducing a minimization problem involving norm of the feedback matrix subject to a maximum allowable compromise in performance. To address the computational difficulty caused by the use of norm, we propose to approximate the norm by its Moreau envelope and the proximal algorithm with extrapolation is constructed to solve the approximated optimization problem. Convergence analysis based on the Kurdyka-Lojasiewicz (KL) properties is presented. Our numerical examples show that the proposed framework can obtain feedback matrices with higher sparsity when compared with the model based on the norm relaxation.

个人简历：

博士，教授，博士生导师，一直致力于智能决策分析、最优化理论及其应用研究，曾担任澳大利亚Curtin大学研究员、高级研究员、高级讲师、建筑信息模型中心副主任。先后主持国家自然科学基金项目3项，主持和作为重要参与人参加了澳大利亚国科会(Australia Research Council)项目4项，韩国国家研究委员会海外项目2项，以及其他省部级项目10余项，在国际高水平杂志发表被SCI收录的论文70多篇，担任被SCI收录杂志Journal of Industrial and Management Optimization执行编辑。