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Inexact Sequential Quadratic Optimization

with Penalty Parameter Updates within the QP Solver

王浩 助理教授

（上海科技大学）

报告时间： 2022年11月25日 (星期五) 下午4:00-5:00

腾讯会议 ID：549-659-580

报告摘要：This paper focuses on the design of sequential quadratic optimization (commonly known as SQP) methods for solving large-scale nonlinear optimization problems. The most computationally demanding aspect of such an approach is the computation of the search direction during each iteration, for which we consider the use of matrix-free methods. In particular, we develop a method that requires an inexact solve of a single QP subproblem to establish the convergence of the overall SQP method. It is known that SQP methods can be plagued by poor behavior of the global convergence mechanism. To confront this issue, we propose the use of an exact penalty function with a dynamic penalty parameter updating strategy to be employed within the subproblem solver in such a way that the resulting search direction predicts progress toward both feasibility and optimality. We present our parameter updating strategy and prove that, under reasonable assumptions, the strategy does not modify the penalty parameter unnecessarily. We close the paper with a discussion of the results of numerical experiments that illustrate the benefits of our proposed techniques.

报告人简介： 王浩博士于2015年5月在美国Lehigh University工业工程系获得博士学位，导师为Frank E. Curtis，并于2010年和2007年在北京航空航天大学数学与应用数学系分别获得理学硕士和学士学位。王浩博士于2016年3月以助理教授加入上海科技大学信息与技术学院。当前研究领域主要为惩罚算法、非精确算法、正则化问题等。

邀请人： 刘红英