Weak and strong superiorization: Between feasibility-seeking and minimization

Research output: Contribution to journalArticlepeer-review


We review the superiorization methodology, which can be thought of, in some cases, as lying between feasibility-seeking and constrained minimization. It is not quite trying to solve the full fledged constrained minimization problem; rather, the task is to find a feasible point which is superior (with respect to an objective function value) to one returned by a feasibility-seeking only algorithm. We distinguish between two research directions in the superiorization methodology that nourish from the same general principle: Weak superiorization and strong superior-ization and clarify their nature.

Original languageEnglish
Pages (from-to)41-54
Number of pages14
JournalAnalele Stiintifice ale Universitatii Ovidius Constanta, Seria Matematica
Issue number3
StatePublished - 15 Jul 2015


  • Constrained minimization
  • Convex feasibility problem
  • Dynamic string-averaging
  • Perturbation resilience
  • Strict Fejer monotonicity
  • Superiorization methodology
  • Superiorized version of an algorithm

ASJC Scopus subject areas

  • Analysis
  • Applied Mathematics


Dive into the research topics of 'Weak and strong superiorization: Between feasibility-seeking and minimization'. Together they form a unique fingerprint.

Cite this