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Weak and Strong Superiorization: Between Feasibility-Seeking and Minimization

Yair Censor (yair***at***math.haifa.ac.il)

Abstract: 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 superiorization and clarify their nature.


Category 1: Nonlinear Optimization (Constrained Nonlinear Optimization )

Category 2: Convex and Nonsmooth Optimization (Convex Optimization )

Citation: Preprint, September 30, 2014. Revised: November 27, 2014. Presented at the Tenth Workshop on Mathematical Modelling of Environmental and Life Sciences Problems, October 16-19, 2014, Constantza, Romania. http://www.ima.ro/workshop/tenth_workshop/.

Download: [PDF]

Entry Submitted: 10/01/2014
Entry Accepted: 10/01/2014
Entry Last Modified: 12/09/2014

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