Data-compatibility of algorithms for constrained convex optimization

Yair Censor, Maroun Zaknoon, Alexander J. Zaslavski

Research output: Contribution to journalArticlepeer-review


The data-compatibility approach to constrained optimization, proposed here, strives to a point that is “close enough” to the solution set and whose target function value is “close enough” to the constrained minimum value. These notions can replace analysis of asymptotic convergence to a solution point of infinite sequences generated by specific algorithms. We consider a problem of minimizing a convex function over the intersection of the fixed point sets of nonexpansive mappings and demostrate the data-compatibility of the Hybrid Subgradient Method (HSM). A string-averaging HSM is obtained as a by-product and relevance to the minimization over disjoint hard and soft constraints sets is discussed.

Original languageEnglish
Pages (from-to)21-41
Number of pages21
JournalJournal of Applied and Numerical Optimization
Issue number1
StatePublished - Apr 2021

Bibliographical note

Publisher Copyright:
© 2021 Journal of Applied and Numerical Optimization.


  • Constrained minimization
  • Data-compatiblity
  • Feasibility-seeking
  • Hybrid subgradient method
  • Proximity function

ASJC Scopus subject areas

  • Computational Mathematics
  • Control and Optimization
  • Numerical Analysis
  • Modeling and Simulation


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