Iterative hard thresholding methods for regularized convex cone 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/s10107-013-0714-4
发表日期：
2014
页码：
125-154
关键词：
摘要：
In this paper we consider regularized convex cone programming problems. In particular, we first propose an iterative hard thresholding (IHT) method and its variant for solving regularized box constrained convex programming. We show that the sequence generated by these methods converges to a local minimizer. Also, we establish the iteration complexity of the IHT method for finding an -local-optimal solution. We then propose a method for solving regularized convex cone programming by applying the IHT method to its quadratic penalty relaxation and establish its iteration complexity for finding an -approximate local minimizer. Finally, we propose a variant of this method in which the associated penalty parameter is dynamically updated, and show that every accumulation point is a local izer of the problem.
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