A note on completely positive relaxations of quadratic problems in a multiobjective framework.
Abstract: 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 entry wise 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 nonconvex problems.
Keywords: multiobjective optimization, completely positive optimization, quadratic programming, convexification
Category 1: Other Topics (Multi-Criteria Optimization )
Category 2: Linear, Cone and Semidefinite Programming
Citation: G. Eichfelder and P. Groetzner, A note on completely positive relaxations of quadratic problems in a multiobjective framework. Preprint, 2021.
Entry Submitted: 01/26/2021
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