A New Adaptive Algorithm for Convex Quadratic Multicriteria Optimization

Fliege, Jörg; Heermann, Christoph; Weyers, Bernd

doi:10.4230/DagSemProc.04461.3

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A New Adaptive Algorithm for Convex Quadratic Multicriteria Optimization

Authors Jörg Fliege, Christoph Heermann, Bernd Weyers

Part of: Volume: Dagstuhl Seminar Proceedings, Volume 4461
Part of: Series: Dagstuhl Seminar Proceedings (DagSemProc)
License: Creative Commons Attribution 4.0 International license
Publication Date: 2005-08-10

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Document Identifiers

DOI: 10.4230/DagSemProc.04461.3
URN: urn:nbn:de:0030-drops-2386

Subject Classification

Keywords

Multicriteria optimization
warm-start methods
interior-point methods
primal-dual algorithms

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Abstract

We present a new adaptive algorithm for convex quadratic multicriteria
optimization. The algorithm is able to adaptively refine the approximation
to the set of efficient points by way of a warm-start interior-point
scalarization approach. Numerical results show that this technique is
an order of magnitude faster than a standard method used for this problem.

Cite As Get BibTex

Jörg Fliege, Christoph Heermann, and Bernd Weyers. A New Adaptive Algorithm for Convex Quadratic Multicriteria Optimization. In Practical Approaches to Multi-Objective Optimization. Dagstuhl Seminar Proceedings, Volume 4461, pp. 1-39, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2005) https://doi.org/10.4230/DagSemProc.04461.3

Author Details

Jörg Fliege

Christoph Heermann

Bernd Weyers

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