Superiorization: The asymmetric roles of feasibility-seeking and objective function reduction

The superiorization methodology can be thought of as lying conceptually between feasibility-seeking and constrained minimization. It is not trying to solve the full-fledged constrained minimization problem
composed from the modeling constraints and the chosen objective function. Rather, the task is to find a feasible point which is “superior” (in a well-defined manner) with respect to the objective function, to one returned by a feasibility-seeking only algorithm. We telegraphically review the superiorization methodology and where it stands today and propose a rigorous formulation of its, yet only partially
resolved, guarantee problem. The real-world situation in an application field is commonly represented by constraints defined by the modeling process and the data, obtained from measurements or
otherwise dictated by the model-user. The feasibility-seeking problem requires to find a point in the intersection of all constraints without using any objective function to aim at any specific feasible point.
At the heart of the superiorization methodology lies the modeler desire to use an objective function, that is exogenous to the constraints, in order to seek a feasible solution that will have lower
(not necessarily minimal) objective function value. This aim is less demanding than full-fledged constrained minimization but more demanding than plain feasibility-seeking.
Putting emphasis on the need to satisfy the constraints, because they represent the real-world situation, one recognizes the “asymmetric roles of feasibility-seeking and objective function reduction”, namely, that fulfilling the constraints is the main task while reduction of the exogenous objective function plays only a secondary role. There are two research directions in the superiorization methodology
that nourish from this same general principle: Weak superiorization
and strong superiorization. Since its inception in 2007, the superiorization methodology has
evolved and gained ground, as can be seen from the, compiled and continuously
updated, bibliography at:


Applied Set-Valued Analysis and Optimization, accepted for publication.



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