A linearly convergent algorithm for solving a class of nonconvex/affine feasibility problems

Amir Beck*, Marc Teboulle

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

15 Scopus citations

Abstract

We introduce a class of nonconvex/affine feasibility (NCF) problems that consists of finding a point in the intersection of affine constraints with a nonconvex closed set. This class captures some interesting fundamental and NP hard problems arising in various application areas such as sparse recovery of signals and affine rank minimization that we briefly review. Exploiting the special structure of NCF, we present a simple gradient projection scheme which is proven to converge to a unique solution of NCF at a linear rate under a natural assumption explicitly given defined in terms of the problem’s data.

Original languageEnglish
Title of host publicationSpringer Optimization and Its Applications
PublisherSpringer International Publishing
Pages33-48
Number of pages16
DOIs
StatePublished - 2011
Externally publishedYes

Publication series

NameSpringer Optimization and Its Applications
Volume49
ISSN (Print)1931-6828
ISSN (Electronic)1931-6836

Funding

FundersFunder number
Israel Science Foundation489-06

    Keywords

    • Affine rank minimization
    • Compressive sensing
    • Gradient projection algorithm
    • Inverse problems
    • Linear rate of convergence
    • Mutual coherence of a matrix
    • Nonconvex affine feasibility
    • Scalable restricted isometry
    • Sparse signal recovery

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