主 讲 人：胡耀华

主办单位：数学与大数据学院

讲座时间：2020年11月3日（周二）上午10:00-11:00

讲座地点：知津楼C303

内容简介：

In this talk, we consider the convex composite optimization (CCO) problem that provides a unified framework of a wide variety of important optimization problems, such as convex inclusions, penalty methods for nonlinear programming, and regularized minimization problems. We will introduce a linearized proximal algorithm (LPA) to solve the CCO. The LPA has the attractive computational advantages of simple implementation and fast convergence rate. Under the assumptions of local weak sharp minima of Holderian order and a quasi-regularity condition, we establish a local/semi-local/global superlinear convergence rate for the LPA-type algorithms. We further apply the LPA to solve a (possibly nonconvex) feasibility problem, as well as a sensor network localization problem. Our numerical results illustrate that the LPA meets the demand for an efficient and robust algorithm for the sensor network localization problem.

主讲人简介：

胡耀华博士，深圳大学数学与统计学院副教授，硕士生导师，本科和硕士毕业于浙江大学，博士毕业于香港理工大学，从事最优化理论，算法和应用方面的研究工作。目前在最优化领域的权威期刊SIAM Journal on Optimization，Journal of Machine Learning Research，European Journal of Operational Research，Journal of Global Optimization及Numerical Algorithms和Inverse Problems等期刊上发表了多篇学术论文。主持国家自然科学基金青年项目和面上项目各1项。