报告题目：Stochastic alternating structure-adapted proximal gradient descent method with variance reduction for nonconvex nonsmooth optimization 报告时间：2023年6月5日（周一）下午15:00-17:00 报告地点：南一120
主讲人：韩德仁 教授 摘要：We develop a stochastic alternating structure-adapted proximal (s-ASAP) gradient descent method for solving the block optimization problems. By deploying some state-of-the-art variance reduced gradient estimators (rather than full gradient) in stochastic optimization, the s-ASAP method is applicable to nonconvex consensus optimization problems whose objectives are the sum of a finite number of Lipschitz continuous functions. The sublinear convergence rate of s-ASAP method is built upon the proximal point theory. Furthermore, the linear convergence rate of s-ASAP method can be attainable under some mild conditions on objectives, e.g., the error bound and the Kurdyka-Lojasiewicz (KL) property. Preliminary numerical simulations on some applications in image processing demonstrate the compelling performance of the proposed method.
韩德仁：教授，博士生导师，现任北京航空航天大学数学科学学院院长、教育部数学类专业教指委秘书长。2002年获南京大学计算数学博士学位。从事大规模优化问题、变分不等式问题的数值方法的研究工作，以及优化和变分不等式问题在交通规划、磁共振成像中的应用，发表多篇学术论文。曾获中国运筹学会青年科技奖，江苏省科技进步奖等奖项;主持国家自然科学基金杰出青年基金等多项项目。担任中国运筹学会常务理事；《数值计算与计算机应用》、《Journal of the Operations Research Society of China》、《Journal of Global Optimization》编委。 |
