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报告题目：Interior Quasi-subgradient Method with non-Euclidean Distances for Constrained Ouasi-convex Optimization Problems

报告人：胡耀华（深圳大学）

报告时间：2022年5月9日 19:00~20:00

报告地点：腾讯会议  会议号：554-976-5868

报告内容简介：Quasi-convex optimization is fundamental to the modeling of many practical problems in various fields such as economics,finance and industrial organization,and subgradient methods are practical iterative algorithms for solving large-scale convex or quasi-convex optimization problems. In this talk,an interior quasi-subgradient method is proposed based on the proximal distance to solve constrained nondifferentiable quasi-convex optimization problems.The properties of the specific quasi-subdifferentials of quasi-convex functions,including nonemptinessquasi-monotonicity and outer semicontinuity, will be presented.The convergence properties,including the global convergence and iteration complexity,of the interior quasi-subgradient method are investigated under the assumption of the Holder condition of order p. when using the constant/diminishing/dynamic stepsize rules.Convergence rate resuits are obtained by assuming a Holder-type weak sharp minimum condition relative to an induced proximal distance.

报告人简介：胡耀华，1984年，江西吉安人。先后获得浙江大学学士和硕士学位，香港理工大学博士学位（师从杨晓琪教授），现任深圳大学数学与统计学院副教授，硕士生导师，香港理工大学兼职博士生导师，兼任中国运筹学会一数学规划分会青年理事，广东省工业与应用数学学会理事，广东省运筹学会理事。 主要从事连续优化理论与应用研究，主持国家自然科学基金3项，省市级科研项目多项。在SIAM Journal on Optimization, Journal of Machine Leaning Research, Inverse Problems, European Joumal of Operational Research等国际学术期刊发表40余篇论文，授权3项发明专利，开发多个生物信息学工具包/网页服务器。个人主页：https://mayhhu.github.io/ch/index.html

邀请人：矫立国 (助理研究员  jiaolg356@nenu.edu.cn)