Proximal Iteratively Reweighted Algorithm with Multiple Splitting for Nonconvex Sparsity Optimization

Authors

  • Canyi Lu National University of Singapore
  • Yunchao Wei Beijing Jiaotong University
  • Zhouchen Lin Peking University
  • Shuicheng Yan National University of Singapore

DOI:

https://doi.org/10.1609/aaai.v28i1.8897

Keywords:

nonconvex sparse optimzation

Abstract

This paper proposes the Proximal Iteratively REweighted (PIRE) algorithm for solving a general problem, which involves a large body of nonconvex sparse and structured sparse related problems. Comparing with previous iterative solvers for nonconvex sparse problem, PIRE is much more general and efficient. The computational cost of PIRE in each iteration is usually as low as the state-of-the-art convex solvers. We further propose the PIRE algorithm with Parallel Splitting (PIRE-PS) and PIRE algorithm with Alternative Updating (PIRE-AU) to handle the multi-variable problems. In theory, we prove that our proposed methods converge and any limit solution is a stationary point. Extensive experiments on both synthesis and real data sets demonstrate that our methods achieve comparative learning performance, but are much more efficient, by comparing with previous nonconvex solvers.

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Published

2014-06-21

How to Cite

Lu, C., Wei, Y., Lin, Z., & Yan, S. (2014). Proximal Iteratively Reweighted Algorithm with Multiple Splitting for Nonconvex Sparsity Optimization. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1). https://doi.org/10.1609/aaai.v28i1.8897

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Section

Main Track: Machine Learning Applications