On the behavior of subgradient projections methods for convex feasibility problems in euclidean spaces

Dan Butnariu, Yair Censor, Pini Gurfil, Ethan Hadar

Research output: Contribution to journalArticlepeer-review

Abstract

We study some methods of subgradient projections for solving a convex feasibility problem with general (not necessarily hyperplanes or half-spaces) convex sets in the inconsistent case and propose a strategy that controls the relaxation parameters in a specific self-adapting manner. This strategy leaves enough user flexibility but gives a mathematical guarantee for the algorithm's behavior in the inconsistent case. We present the numerical results of computational experiments that illustrate the computational advantage of the new method.

Original languageEnglish
Pages (from-to)786-807
Number of pages22
JournalSIAM Journal on Optimization
Volume19
Issue number2
DOIs
StatePublished - Jun 2008

Keywords

  • Computational algorithms
  • Convex feasibility problems
  • Projection method

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science

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