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• W2135528585 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." @default.
• W2135528585 created "2016-06-24" @default.
• W2135528585 creator A5017375713 @default.
• W2135528585 creator A5075594934 @default.
• W2135528585 date "2014-06-18" @default.
• W2135528585 modified "2023-10-18" @default.
• W2135528585 title "Strict Fejér Monotonicity by Superiorization of Feasibility-Seeking Projection Methods" @default.
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• W2135528585 doi "https://doi.org/10.1007/s10957-014-0591-x" @default.
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