数学物理学报 ›› 2022, Vol. 42 ›› Issue (2): 605-620.

• 论文 • 上一篇    下一篇

基于非单调线搜索的HS-DY形共轭梯度方法及在图像恢复中的应用

袁功林1(),吴宇伦1,*(),PhamHongtruong2()   

  1. 1 广西大学数学与信息科学学院&广西应用数学中心 南宁 530004
    2 泰阮大学经济与工商管理学院 越南太原
  • 收稿日期:2021-03-03 出版日期:2022-04-26 发布日期:2022-04-18
  • 通讯作者: 吴宇伦 E-mail:glyuan@gxu.edu.cn;wuyulun@st.gxu.edu.cn;shanghaichina888@yahoo.com
  • 作者简介:袁功林, E-mail: glyuan@gxu.edu.cn|Pham Hongtruong, E-mail: shanghaichina888@yahoo.com
  • 基金资助:
    国家自然科学基金(11661009);广西高校高水平创新团队和优秀学者计划([2019]52);广西自然科学重点基金(2017GXNSFDA198046);中央引导地方科技发展专项基金(ZY20198003);广西"八桂学者"专项

A Modified HS-DY-Type Method with Nonmonotone Line Search for Image Restoration and Unconstrained Optimization Problems

Gonglin Yuan1(),Yulun Wu1,*(),Hongtruong Pham2()   

  1. 1 Center for Applied Mathematics of Guangxi & College of Mathematics and Information Science, Guangxi University, Nanning 530004
    2 Thai Nguyen University of Economics and Business Administration, Thai Nguyen, Vietnam
  • Received:2021-03-03 Online:2022-04-26 Published:2022-04-18
  • Contact: Yulun Wu E-mail:glyuan@gxu.edu.cn;wuyulun@st.gxu.edu.cn;shanghaichina888@yahoo.com
  • Supported by:
    the NSFC(11661009);the High Level Innovation Teams and Excellent Scholars Program in Guangxi Institutions of Higher Education([2019]52);the Guangxi Natural Science Key Fund(2017GXNSFDA198046);the Special Funds for Local Science and Technology Development Guided by the Central Government(ZY20198003);the Special Foundation for Guangxi Ba Gui Scholars

摘要:

该文提出了一种求解图像恢复问题和无约束优化问题的改进的共轭梯度算法, 其中共轭梯度参数是修改过的HS和DY方法的共轭参数的凸组合形式, 新提出的共轭梯度参数比起经典的参数还包含了函数的信息. 该方法在不使用任何线性搜索技术的情况下, 就可以满足充分下降的性质. 此外, 在一定合理条件下, 该文证明了在非单调线性搜索下新方法的全局收敛性. 最后, 在无约束优化和图像恢复问题上的实验表明, 新方法与其他共轭梯度算法相比, 具有良好的竞争力和应用前景.

关键词: 共轭梯度法, 全局收敛性, 无约束优化, 非单调线性搜索, 图像恢复

Abstract:

A modified conjugate gradient algorithm for solving image restoration problems and unconstrained optimization problems is proposed, where the conjugate gradient (CG) parameter is the convex combination of the improved HS and DY methods, and the CG parameter contains function information. In addition, the method does not require any line searches, and it can generate sufficient descent directions. Moreover, under certain conditions, the new method is globally convergent with nonmonotone line search. Finally, experiments on unconstrained optimization and image restoration problems show that the new method has good application prospects and advantages when compared with other conjugate gradient algorithms.

Key words: Conjugate gradient, Global convergence, Unconstrained optimization, Nonmonotone line search, Image restoration

中图分类号: 

  • O221