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A Proximal Fully Parallel Splitting Method for Stable Principal Component Pursuit

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  • Hongchun Sun
  • Jing Liu
  • Min Sun

Abstract

As a special three-block separable convex programming, the stable principal component pursuit (SPCP) arises in many different disciplines, such as statistical learning, signal processing, and web data ranking. In this paper, we propose a proximal fully parallel splitting method (PFPSM) for solving SPCP, in which the resulting subproblems all admit closed-form solutions and can be solved in distributed manners. Compared with other similar algorithms in the literature, PFPSM attaches a Glowinski relaxation factor to the updating formula for its Lagrange multiplier, which can be used to accelerate the convergence of the generated sequence. Under mild conditions, the global convergence of PFPSM is proved. Preliminary computational results show that the proposed algorithm works very well in practice.

Suggested Citation

  • Hongchun Sun & Jing Liu & Min Sun, 2017. "A Proximal Fully Parallel Splitting Method for Stable Principal Component Pursuit," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-15, October.
    • Handle: RePEc:hin:jnlmpe:9674528
    DOI: 10.1155/2017/9674528
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