A General Iterative Shrinkage and Thresholding algorithm for non-convex regularized optimization problems

Pinghua Gong, Changshui Zhang, Zhaosong Lu, Jianhua Z. Huang, Jieping Ye

Research output: Chapter in Book/Report/Conference proceedingConference contribution

105 Scopus citations

Abstract

Non-convex sparsity-inducing penalties have recently received considerable attentions in sparse learning. Recent theoretical investigations have demonstrated their superiority over the convex counterparts in several sparse learning settings. However, solving the non-convex optimization problems associated with non-convex penalties remains a big challenge. A commonly used approach is the Multi-Stage (MS) convex relaxation (or DC programming), which relaxes the original non-convex problem to a sequence of convex problems. This approach is usually not very practical for large-scale problems because its computational cost is a multiple of solving a single convex problem. In this paper, we propose a General Iterative Shrinkage and Thresholding (GIST) algorithm to solve the nonconvex optimization problem for a large class of non-convex penalties. The GIST algorithm iteratively solves a proximal operator problem, which in turn has a closed-form solution for many commonly used penalties. At each outer iteration of the algorithm, we use a line search initialized by the Barzilai-Borwein (BB) rule that allows finding an appropriate step size quickly. The paper also presents a detailed convergence analysis of the GIST algorithm. The efficiency of the proposed algorithm is demonstrated by extensive experiments on large-scale data sets.

Original languageEnglish (US)
Title of host publication30th International Conference on Machine Learning, ICML 2013
PublisherInternational Machine Learning Society (IMLS)
Pages696-704
Number of pages9
EditionPART 1
StatePublished - 2013
Event30th International Conference on Machine Learning, ICML 2013 - Atlanta, GA, United States
Duration: Jun 16 2013Jun 21 2013

Other

Other30th International Conference on Machine Learning, ICML 2013
CountryUnited States
CityAtlanta, GA
Period6/16/136/21/13

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ASJC Scopus subject areas

  • Human-Computer Interaction
  • Sociology and Political Science

Cite this

Gong, P., Zhang, C., Lu, Z., Huang, J. Z., & Ye, J. (2013). A General Iterative Shrinkage and Thresholding algorithm for non-convex regularized optimization problems. In 30th International Conference on Machine Learning, ICML 2013 (PART 1 ed., pp. 696-704). International Machine Learning Society (IMLS).