Constructing Approximations to the Efficient Set of Convex Quadratic Multiobjective Problems

Jörg Fliege (fliegemath.uni-dortmund.de)
Andree Heseler (andree.heselermath.uni-dortmund.de)

Abstract: In multicriteria optimization, several objective functions have to be minimized simultaneously. For this kind of problem, no single solution can adequately represent the whole set of optimal points. We propose a new efficient method for approximating the solution set of a convex quadratic multiobjective programming problem. The method is based on a warm-start interior point algorithm for which we derive complexity results, thereby extending previous results by Yildirim & Wright. Numerical results on bicriteria problems from power plant optimization and portfolio optimization show that the method is an order of magnitude faster than standard methods applied to the problems considered.

Keywords: multicriteria optimization, interior-point methods, warm-start

Category 1: Other Topics (Multi-Criteria Optimization )

Category 2: Nonlinear Optimization (Quadratic Programming )

Citation: Ergebnisberichte Angewandte Mathematik. No. 211. Fachbereich Mathematik, Universität Dortmund, 44221 Dortmund, Germany. January 2002.

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Entry Submitted: 01/10/2002
Entry Accepted: 01/10/2002
Entry Last Modified: 03/31/2004

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