报 告 人：Wei Zhu Associate Professor （University of Alabama ）
报告题目：New augmented Lagrangian methods for curvature dependent variational models
报告时间：2016年7月5日(周二）下午4点
报告地点：静远楼1506学术报告厅
报告摘要：Augmented Lagrangian methods (ALMs) have proved to be successful for the minimization of curvature dependent functionals in image processing. However, those ALM based algorithms often suffer from choosing appropriate penalty parameters in the numerical implementation. In this talk, we will discuss our recent works on the development of ALM based algorithms for curve and surface curvature based variational models by introducing fewer Lagrange multipliers. Besides significantly reducing the effort of selecting suitable penalty parameters, the new algorithms help capture curvature more faithfully than those existing ones. Numerical experiments will also be presented to valid the effectiveness of the proposed algorithms.
报告人简介： 08/2008— present Associate Professor Department of Mathematics, University of Alabama ;08/2004— 08/2008 Research Scientist Courant Instructor Courant Institute of Mathematical Sciences, NYU ;06/2004 Ph. D, University of California, Los Angeles ;07/1999 M.S, Peking University, P.R. China ;07/1994 B.S, Tsinghua University, P.R. China.