报告地点：行健楼学术活动室665
邀请人：孙海琳教授
Abstract: The sequential quadratic programming (SQP) method has shown remarkable performance for addressing nonlinear optimization problems. However, it typically requires the quadratic programming (QP) subproblems to be feasible. To overcome this limitation, various approaches introducing penalizations or perturbations to the QP subproblems have been developed. In this study, we propose a novel natural SQP algorithm that iterates through a stationary point of the classical QP subproblem, specifically the minimizer closest to the feasible region. This approach ensures global convergence under standard assumptions by identifying a solution with the least constraint violation. The resulting solution minimizes the objective function within the set of minimizers for constraint violation. Furthermore, the proposed method exhibits a quadratic convergence rate. When the original problem is feasible, our assumptions and conclusions align with those of the classical SQP method. Numerical experiments validate the effectiveness and demonstrate the superior performance of the proposed algorithm.
报告人简介：付文豪，讲师，2021年博士毕业于苏州大学，中国科学院访问学者，入选江苏省“双创博士”，主持江苏省自然科学基金青年基金项目1项。主要研究领域包括非线性规划和非线性半定规划的SOP型方法、最小约束违背优化及其应用。在《Mathematical Programming Computation》、《Computational Optimization and Applications》、《Numerical Algorithms》、《Optimization》等期刊上发表文章。
