A multi-stage convex relaxation approach to noisy structured low-rank matrix recovery

Shujun Bi, Shaohua Pan, Defeng Sun

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

This paper concerns with a noisy structured low-rank matrix recovery problem which can be modeled as a structured rank minimization problem. We reformulate this problem as a mathematical program with a generalized complementarity constraint (MPGCC), and show that its penalty version, yielded by moving the generalized complementarity constraint to the objective, has the same global optimal solution set as the MPGCC does whenever the penalty parameter is over a certain threshold. Then, by solving the exact penalty problem in an alternating way, we obtain a multi-stage convex relaxation approach. We provide theoretical guarantees for our approach under a mild restricted eigenvalue condition, by quantifying the reduction of the error and approximate rank bounds of the first stage convex relaxation in the subsequent stages and establishing the geometric convergence of the error sequence in a statistical sense. Numerical experiments are conducted for some structured low-rank matrix recovery examples to confirm our theoretical findings. Our code can be achieved from https://doi.org/10.5281/zenodo.3600639.

Original languageEnglish
Pages (from-to)569-602
Number of pages34
JournalMathematical Programming Computation
Volume12
Issue number4
DOIs
Publication statusPublished - 1 Dec 2020

Keywords

  • Convex relaxation
  • Exact penalty
  • MPGCC
  • Structured rank minimization

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software

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