BACKTRACKING STRATEGIES FOR ACCELERATED DESCENT METHODS WITH SMOOTH COMPOSITE OBJECTIVES

被引：31

作者：

Calatroni, Luca ^{[1
]}

Chambolle, Antonin ^{[1
]}

机构：

[1] Ecole Polytech, Ctr Math Appl CMAP, CNRS, F-91128 Palaiseau, France

来源：

SIAM JOURNAL ON OPTIMIZATION | 2019年 / 29卷 / 03期

关键词：

composite optimization; forward-backward splitting; acceleration; backtracking; image denoising; elastic net; ALGORITHM; MINIMIZATION; OPTIMIZATION; CONVERGENCE; INEXACT;

D O I：

10.1137/17M1149390

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

We present and analyze a backtracking strategy for a general fast iterative shrinkage/thresholding algorithm proposed by Chambolle and Pock [Acta Numer., 25 (2016), pp. 161-319] for strongly convex composite objective functions. Unlike classical Armijo-type line searching, our backtracking rule allows for local increasing and decreasing of the descent step size (i.e., proximal parameter) along the iterations. We prove accelerated convergence rates and show numerical results for some exemplar problems.

引用

页码：1772 / 1798

页数：27

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