A primal-dual fixed point algorithm for minimization of the sum of three convex separable functions

发布日期: 2017-04-20  作者:    浏览次数: 261 


报告题目:

A primal-dual fixed point algorithm for minimization of the sum of three convex separable functions

报 告 人:

黄建国 教授

上海交通大学 数学科学学院

报告时间:

421日 星期五 下午 4:00-5:00

报告地点:

数理学院(10号楼)222

相关介绍:

   Many problems arising in image processing and signal recovery with multi -regularization and constraints can be formulated as minimization of a sum of three convex separable functions. Typically, the objective function involves a smooth function with Lipschitz continuous gradient, a linear composite non--smooth function and a non--smooth function. In this talk, we aim to propose a primal-dual fixed point (PDFP) scheme to solve the above class of problems. The proposed algorithm is a symmetric and fully splitting scheme, only involving explicit gradient, linear transform and the proximity operators which may have closed-form solution. The convergence of the algorithm is established and some numerical examples are performed to show its efficiency. This is a joint work with Peijun Chen and Xiaoqun Zhang from Shanghai Jiao Tong University.

 


 
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