Abstract
We consider the superiorization methodology, which can be thought of 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 the objective function value) to one returned by a feasibility-seeking only algorithm. Our main result reveals new information about the mathematical behavior of the superiorization methodology. We deal with a constrained minimization problem with a feasible region, which is the intersection of finitely many closed convex constraint sets, and use the dynamic string-averaging projection method, with variable strings and variable weights, as a feasibility-seeking algorithm. We show that any sequence, generated by the superiorized version of a dynamic string-averaging projection algorithm, not only converges to a feasible point but, additionally, also either its limit point solves the constrained minimization problem or the sequence is strictly Fejér monotone with respect to a subset of the solution set of the original problem.
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Acknowledgments
We thank Gabor Herman for an enlightening discussion about terminology, and we greatly appreciate the constructive comments of two anonymous reviewers which helped us improve the paper.
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Communicated by Jonathan Michael Borwein.
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Censor, Y., Zaslavski, A.J. Strict Fejér Monotonicity by Superiorization of Feasibility-Seeking Projection Methods. J Optim Theory Appl 165, 172–187 (2015). https://doi.org/10.1007/s10957-014-0591-x
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DOI: https://doi.org/10.1007/s10957-014-0591-x
Keywords
- Bounded perturbation resilience
- Constrained minimization
- Convex feasibility problem
- Dynamic string-averaging projections
- Strict Fejér monotonicity
- Subgradients
- Superiorization methodology
- Superiorized version of an algorithm