{dede:global.cfg_webname/}
  • English
  • 官方微信
  • 首页
  • 栏目名称
    • 测试
  • 第二个
  • 首页
  • 学院概况
    • 学院简介
    • 历史沿革
    • 机构设置
    • 现任领导
    • 历任领导
    • 联系我们
  • 师资队伍
    • 全职教工
    • 讲座 兼职教授
    • 重要人才计划
    • 退休人员名单
  • 人才培养
    • 本科生培养
    • 硕士生培养
    • 博士生培养
  • 科学研究
    • 学术交流
    • 重点学科
    • 科研机构
    • 科研团队
    • 科研成果
    • 讨论班
  • 党团建设
    • 党建动态
    • 工会活动
    • 团学工作
  • 理论学习
    • 主题教育
  • 合作交流
    • 国际合作
    • 校际合作
    • 校企合作
  • 招生就业
    • 招生信息
    • 就业信息
    • 招生宣传
  • 校友之家
    • 校友组织
    • 校友基金
    • 校友活动
    • 百年院庆
    • 校友动态
    • 知名校友
  • 院务信箱

学术交流

  • 学术交流
  • 重点学科
  • 科研机构
  • 科研团队
  • 科研成果
  • 讨论班

学术交流

An Inner-outer Iteration Methods for Solving Convex Optimization Problems Involving the Sum of Three Convex Functions

日期:2020-08-31  作者:  点击:[]

报告题目:An Inner-outer Iteration Methods for Solving Convex Optimization Problems Involving the Sum of Three Convex Functions

主 讲 人:唐 玉 超

单 位:南昌大学理学院

时 间:9月5日14:30

腾 讯 ID:329 234 645

摘 要:

In this paper, we consider solving a class of convex optimization problem which minimizes the sum of three convex functions $f(x)+g(x)+h(Bx)$, where $f(x)$ is differentiable with a Lipschitz continuous gradient, $g(x)$ and $h(x)$ have a closed-form expression of their proximity operators and $B$ is a bounded linear operator. This type of optimization problem has wide application in signal recovery and image processing. To make full use of the differentiability function in the optimization problem, we take advantage of two operator splitting methods: the forward-backward splitting method and the three operator splitting method. In the iteration scheme derived from the two operator splitting methods, we need to compute the proximity operator of $g+h \circ B$ and $h \circ B$, respectively. Although these proximity operators do not have a closed-form solution in general, they can be solved very efficiently. We mainly employ two different approaches to solve these proximity operators: one is dual and the other is primal-dual.

Following this way, we fortunately find that three existing iterative algorithms including Condat and Vu algorithm, primal-dual fixed point (PDFP) algorithm and primal-dual three operator (PD3O) algorithm are a special case of our proposed iterative algorithms. Moreover, we discover a new kind of iterative algorithm to solve the considered optimization problem, which is not covered by the existing ones. Under mild conditions, we prove the convergence of the proposed iterative algorithms. Numerical experiments applied on fused Lasso problem, constrained total variation regularization in computed tomography (CT) image reconstruction and low-rank total variation image super-resolution problem demonstrate the effectiveness and efficiency of the proposed iterative algorithms.

简 介:

唐玉超,南昌大学理学院数学系。2013年西安交通大学数学与统计学院博士毕业。主要研究方向图像反问题中的优化问题。在研国家自然科学基金地区项目一项,主持完成国家自然科学基金青年项目、江西省自然科学基金青年项目和江西省教育厅青年科学基金项目各一项。已在《中国科学-数学》,《Journal of Computational Mathematics》、《Applied Mathematics Letters》、《Mathematical and Computer Modelling》、《Nonlinear Analysis: Theory, Methods & Applications》和《Numerical Algorithms》等国内外期刊发表论文30余篇。

上一条:Minimal Paths for Image Segmentation and Tubular Structure Tracking 下一条:对数学教学“教学生学会思考”的思考

【关闭】

友情链接

  • 学校教务处
  • 学校党委办公室
  • 学校校长办公室
  • 清华大学数学系
  • 浙江大学数学科学院
  • 上海大学数学系
版权信息