Iterative reweighted minimization methods for regularized unconstrained nonlinear programming

• 成果类型: Article
• 署名作者: Lu, Zhaosong
• 署名单位: Simon Fraser University
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-013-0722-4
• 发表日期: 2014
• 页码: 277-307
• 关键词: sparse; reconstruction; shrinkage; signals

摘要

In this paper we study general regularized unconstrained minimization problems. In particular, we derive lower bounds for nonzero entries of the first- and second-order stationary points and hence also of local minimizers of the minimization problems. We extend some existing iterative reweighted () and () minimization methods to solve these problems and propose new variants for them in which each subproblem has a closed-form solution. Also, we provide a unified convergence analysis for these methods. In addition, we propose a novel Lipschitz continuous -approximation to . Using this result, we develop new methods for the minimization problems and show that any accumulation point of the sequence generated by these methods is a first-order stationary point, provided that the approximation parameter is below a computable threshold value. This is a remarkable result since all existing iterative reweighted minimization methods require that be dynamically updated and approach zero. Our computational results demonstrate that the new method and the new variants generally outperform the existing methods (Chen and Zhou in 2012; Foucart and Lai in Appl Comput Harmon Anal 26:395-407, 2009).