The Semismooth Newton Method for Sparse Signal Reconstruction Problems
报告人： 张海斌
报告人单位：北京工业大学
时间：22年6月16日（周四）晚上 7：30~8：30
线上腾讯会议号：207-468-447 密码：610064
摘要：An adaptive lp-l1−l2 model is considered where the lp-norm with p ≥ 1 measures the data fidelity and the l1−l2-term measures the sparsity. This proposed model has the ability to deal with different types of noises and extract the sparse property even under high coherent condition. A proximal majorization minimization technique is used to handle the nonconvex regularization term and then a semismooth Newton method to solve the corresponding convex relaxation subproblem. Some convergence results and numerical experiments are given to demonstrate the superiority of the proposed model and the proposed algorithm.
报告人简介：张海斌，教授，博士生导师。2002年6月至今在北京工业大学理学部任教。研究方向：最优化算法及其应用，自动微分算法及其应用。主持完成国家自然科学基金面上项目有“变量部分稀疏正则化算法设计与研究”等三项，发表学术论文50余篇，出版专著两部。
