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A class of infeasible proximal bundle methods for nonsmooth nonconvex multi-objective optimization problems

孟凡云

（青岛理工大学）

报告时间：2023年6月30日 星期五 上午11:00-12:00

报告地点：沙河校区E404

报告摘要：We propose a class of infeasible proximal bundle methods for solving nonsmooth nonconvex multi-objective optimization problems. The proposed algorithms have no requirements on the feasibility of the initial points. In the algorithms, the multi-objective functions are handled directly without any scalarization procedure. To speed up the convergence of the infeasible algorithm, an acceleration technique, i.e., the penalty skill, is applied into the algorithm. The strategies are introduced to adjust the proximal parameters and penalty parameters. Under some wild assumptions, the sequence generated by infeasible proximal bundle methods converges to the globally Pareto solution of multi-objective optimization problems. Numerical results shows the good performance of the proposed algorithms.

报告人简介：孟凡云，讲师，青岛理工大学信息与控制工程学院计算机科学与技术硕士生指导教师，2017年获得大连理工大学运筹学与控制论博士学位。主要研究方向为“非光滑优化理论与算法”、“图像处理”和“深度学习”。主持完成山东省自然科学基金1项, 参与完成多项国家自然科学基金。在《Journal of Global Optimization》、《Information Sciences》、《Set-Valued Variational Analysis》、《Optimization》等国际重要期刊发表多篇论文。

邀请人：谢家新