Scholarlink

<p>This paper is devoted to the numerical algorithms for solving the convex composite optimization. We present some new local convergence results for the linearized proximal algorithms with adaptive stepsizes (for short, ALP) proposed in Hu et al. (Appl Math Optim 87(52), 2023). Moreover, two new global versions of ALP are proposed by adding an evaluation step of the directions, and the corresponding global convergence results are established. The obtained local convergence results of this paper improve and extend the corresponding ones in Hu et al. (2023) by proving a higher convergence rate and relaxing the restrictions on the involved parameters. While our global convergence results fill the lack of global ones in Hu et al. (2023) for some more general problems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Convergence of Linearized Proximal Algorithms with Adaptive Stepsizes for Convex Composite Optimization

  • 期刊论文

  • 发表日期：

    2026年2月18日

查看全文
  • Xin Yang,
  • Weiping Shen,
  • Xinyi Hu,
  • Chong Li

摘要

This paper is devoted to the numerical algorithms for solving the convex composite optimization. We present some new local convergence results for the linearized proximal algorithms with adaptive stepsizes (for short, ALP) proposed in Hu et al. (Appl Math Optim 87(52), 2023). Moreover, two new global versions of ALP are proposed by adding an evaluation step of the directions, and the corresponding global convergence results are established. The obtained local convergence results of this paper improve and extend the corresponding ones in Hu et al. (2023) by proving a higher convergence rate and relaxing the restrictions on the involved parameters. While our global convergence results fill the lack of global ones in Hu et al. (2023) for some more general problems.