Derivative-Free Superiorization: Principle and Algorithm
Abstract: The superiorization methodology is intended to work with input data of constrained minimization problems, that is, a target function and a set of constraints. However, it is based on an antipodal way of thinking to what leads to constrained minimization methods. Instead of adapting unconstrained minimization algorithms to handling constraints, it adapts feasibility-seeking algorithms to reduce (not necessarily minimize) target function values. This is done by inserting target-function-reducing perturbations into a feasibility-seeking algorithm while retaining its feasibility-seeking ability and without paying a high computational price. A superiorized algorithm that employs component-wise target function reduction steps is presented. This enables derivative-free superiorization (DFS), meaning that superiorization can be applied to target functions that have no calculable partial derivatives or subgradients. The numerical behavior of our derivative-free superiorization algorithm is illustrated on a data set generated by simulating a problem of image reconstruction from projections. The plots of proximity-target curves of our experiments demonstrate the advantage of the proposed derivative-free superiorization algorithm.
Keywords: Derivative-free; superiorization; constrained minimization; component-wise perturbations; proximity function; bounded perturbations; monotone proximity; proximity-target curve
Category 1: Nonlinear Optimization (Constrained Nonlinear Optimization )
Category 2: Convex and Nonsmooth Optimization
Category 3: Applications -- Science and Engineering (Basic Sciences Applications )
Citation: Preprint, June 6, 2019.
Entry Submitted: 09/02/2019
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