报告题目：Some Inertial Alternating Proximal(-Like) Gradient Methods for a Class of Nonconvex Optimization Problems
报 告 人：蔡邢菊 教授 南京师范大学
报告时间：2020 年 9 月 25 日上午 08：50-09：25
报告地点：腾讯会议 ID：870 938 043
会议密码：9999
校内联系人：李欣欣 xinxinli@jlu.edu.cn
报告摘要：
We study a broad class of nonconvex nonsmooth minimization problems, whose objective function is the sum of a function of the entire variables and two nonconvex functions of each variable. For the different cases, we linearized different fart of the objective function, adopting inertial strategy to accelerate the convergence. We also propose an inertial alternating proximal-like gradient descent algorithm for the problem with abstract constraint sets whose geometry can be captured by using the domain of kernel generating distances. This algorithm can circumvent the restrictive assumption of global Lipschitz continuity of gradient. We prove that each bounded sequence generated by these algorithms globally converge to a critical point of the problem under the assumption that the underlying functions satisfy the Kurdyka-Łojasiewicz property.
报告人简介：
蔡邢菊，南京师范大学教授，硕导。主持国家面上基金、青年基金各一项，江苏省青年基金一项，国家博后特别资助一项。研究兴趣：最优化理论与算法，数值优化，交通管理中的优化，变分不等式。
