Finding efficient solutions in robust multiple objective optimization with SOS-convex polynomial data
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Publication:828872
DOI10.1007/s10479-019-03216-zzbMath1460.90167OpenAlexW2936699287WikidataQ128119462 ScholiaQ128119462MaRDI QIDQ828872
Publication date: 5 May 2021
Published in: Annals of Operations Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10479-019-03216-z
multiobjective optimizationrobust optimizationsemidefinite programming relaxationsSOS-convex polynomials
Semidefinite programming (90C22) Multi-objective and goal programming (90C29) Convex functions and convex programs in convex geometry (52A41) Robustness in mathematical programming (90C17)
Related Items (13)
Well-posedness for the optimistic counterpart of uncertain vector optimization problems ⋮ Approximate solutions in nonsmooth and nonconvex cone constrained vector optimization ⋮ Second-order cone programming relaxations for a class of multiobjective convex polynomial problems ⋮ Multi-objective optimization problems with SOS-convex polynomials over an LMI constraint ⋮ A utopia point method-based robust vector polynomial optimization scheme ⋮ Approximate optimality conditions and approximate duality theorems for nonlinear semi-infinite programming problems with uncertainty data ⋮ Characterizing a class of robust vector polynomial optimization via sum of squares conditions ⋮ On semidefinite programming relaxations for a class of robust SOS-convex polynomial optimization problems ⋮ Robust second order cone conditions and duality for multiobjective problems under uncertainty data ⋮ Optimality conditions for minimax optimization problems with an infinite number of constraints and related applications ⋮ Multi-objective convex polynomial optimization and semidefinite programming relaxations ⋮ Robust Pareto solutions for convex quadratic multiobjective optimization problems under data uncertainty ⋮ Minimax programming as a tool for studying robust multi-objective optimization problems
Uses Software
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