Hybrid proximal-type and hybrid shrinking projection algorithms for equilibrium problems, maximal monotone operators, and relatively nonexpansive mappings

L. C. Ceng, Q. H. Ansari, J. C. Yao

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

In this article, we introduce two hybrid proximal-type algorithms and two hybrid shrinking projection algorithms by using the hybrid proximal-type method and the hybrid shrinking projection method, respectively, for finding a common element of the set of solutions of an equilibrium problem, the set of fixed points of a relatively nonexpansive mapping, and the set of solutions to the equation 0Tx for a maximal monotone operator T defined on a uniformly smooth and uniformly convex Banach space. The strong convergence of the sequences generated by the proposed algorithms is established. Our results improve and generalize several known results in the literature.

Original languageEnglish
Pages (from-to)763-797
Number of pages35
JournalNumerical Functional Analysis and Optimization
Volume31
Issue number7
DOIs
StatePublished - Jul 2010
Externally publishedYes

Keywords

  • Equilibrium problems
  • Generalized projection
  • Hybrid proximal-type algorithms
  • Hybrid shrinking projection algorithms
  • Maximal monotone operators
  • Relatively nonexpansive mappings
  • Strong convergence
  • Uniformly smooth and uniformly convex Banach spaces

ASJC Scopus subject areas

  • Analysis
  • Signal Processing
  • Computer Science Applications
  • Control and Optimization

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