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Multi-objective optimization with convex quadratic cost functions: A multi-parametric programming approach
Affiliation:1. Department of Chemical Engineering, Centre for Process Systems Engineering, Imperial College London, London, United Kingdom;2. Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, United States;1. Technical University of Cluj-Napoca, Department of Mathematics, G. Baritiu 25, Room 198, Romania;1. Laboratoire de Photovoltaïque et Matériaux Semiconducteurs, ENIT-Université Tunis ElManar, BP 37, Le belvédère, 1002 Tunis, Tunisia;2. Laboratory of Semiconductors, Nanostructures and Advanced Technology, Center for Research and Technology Energy, Tourist Route Soliman, BP 95, 2050 Hammam-Lif, Tunisia;3. Physics Department, Faculty of Education of Afif, Shaqra University, Saudi Arabia
Abstract:In this note we present an approximate algorithm for the explicit calculation of the Pareto front for multi-objective optimization problems featuring convex quadratic cost functions and linear constraints based on multi-parametric programming and employing a set of suitable overestimators with tunable suboptimality. A numerical example as well as a small computational study highlight the features of the novel algorithm.
Keywords:Multi-objective optimization  Multi-parametric programming  Explicit Pareto front calculation
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