An implementable proximal point algorithmic framework for nuclear norm minimization

• 成果类型: Article
• 署名作者: Liu, Yong-Jin; Sun, Defeng; Toh, Kim-Chuan
• 署名单位: National University of Singapore; National University of Singapore; Shenyang Aerospace University; Nanyang Technological University; Massachusetts Institute of Technology (MIT); Singapore-MIT Alliance for Research & Technology Centre (SMART)
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-010-0437-8
• 发表日期: 2012
• 页码: 399-436
• 关键词: semidefinite; regularization; PROGRAMS; geometry

摘要

The nuclear norm minimization problem is to find a matrix with the minimum nuclear norm subject to linear and second order cone constraints. Such a problem often arises from the convex relaxation of a rank minimization problem with noisy data, and arises in many fields of engineering and science. In this paper, we study inexact proximal point algorithms in the primal, dual and primal-dual forms for solving the nuclear norm minimization with linear equality and second order cone constraints. We design efficient implementations of these algorithms and present comprehensive convergence results. In particular, we investigate the performance of our proposed algorithms in which the inner sub-problems are approximately solved by the gradient projection method or the accelerated proximal gradient method. Our numerical results for solving randomly generated matrix completion problems and real matrix completion problems show that our algorithms perform favorably in comparison to several recently proposed state-of-the-art algorithms. Interestingly, our proposed algorithms are connected with other algorithms that have been studied in the literature.