A note on completely positive relaxations of quadratic problems in a multiobjective framework

Artikel

In a single-objective setting, nonconvex quadratic problems can equivalently be reformulated as convex problems over the cone of completely positive matrices. In small dimensions this cone equals the cone of matrices which are entrywise nonnegative and positive semidefinite, so the convex reformulation can be solved via SDP solvers. Considering multiobjective nonconvex quadratic problems, naturally the question arises, whether the advantage of convex reformulations extends to the multicriteria framework. In this note, we show that this approach only finds the supported nondominated points, which can already be found by using the weighted sum scalarization of the multiobjective quadratic problem, i.e. it is not suitable for multiobjective nonconvex problems.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Journal: Journal of Global Optimization ; ISSN: 1573-2916 ; Volume: 82 ; Year: 2021 ; Issue: 3 ; Pages: 615-626 ; New York, NY: Springer US

Klassifikation und Themen

• Klassifikation: Mathematik ; Single Equation Models; Single Variables: Other ; Single Equation Models; Single Variables: General
• Thema: Multiobjective optimization ; Completely positive optimization ; Quadratic programming ; Convexification

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Eichfelder, Gabriele ; Groetzner, Patrick
• Ereignis: Veröffentlichung
• (wer): Springer US
• (wo): New York, NY
• (wann): 2021

Weitere Informationen

• DOI: doi:10.1007/s10898-021-01091-2
• Letzte Aktualisierung: 10.03.2025, 11:42 MEZ