報告題目：Some Inertial Alternating Proximal(-Like) Gradient Methods for a Class of Nonconvex Optimization Problems
報 告 人：蔡邢菊 教授 南京師範大學
報告時間：2020 年 9 月 25 日上午 08：50-09：25
報告地點：騰訊會議 ID：870 938 043
會議密碼：9999
校内聯系人：李欣欣 xinxinli@jlu.edu.cn
報告摘要：
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.
報告人簡介：
蔡邢菊，南京師範大學教授，碩導。主持國家面上基金、青年基金各一項，江蘇省青年基金一項，國家博後特别資助一項。研究興趣：最優化理論與算法，數值優化，交通管理中的優化，變分不等式。
