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pro vyhledávání: '"A. Vanderpooten"'
It is well known that, under very weak assumptions, multiobjective optimization problems admit $(1+\varepsilon,\dots,1+\varepsilon)$-approximation sets (also called $\varepsilon$-Pareto sets) of polynomial cardinality (in the size of the instance and
Externí odkaz:
http://arxiv.org/abs/2305.15142
Autor:
Guerreiro, Andreia P., Cortes, João, Vanderpooten, Daniel, Bazgan, Cristina, Lynce, Inês, Manquinho, Vasco, Figueira, José Rui
Recently, it has been shown that the enumeration of Minimal Correction Subsets (MCS) of Boolean formulas allows solving Multi-Objective Boolean Optimization (MOBO) formulations. However, a major drawback of this approach is that most MCSs do not corr
Externí odkaz:
http://arxiv.org/abs/2204.06908
Akademický článek
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Papadimitriou and Yannakakis show that the polynomial-time solvability of a certain singleobjective problem determines the class of multiobjective optimization problems that admit a polynomial-time computable $(1+\varepsilon, \dots , 1+\varepsilon)$-
Externí odkaz:
http://arxiv.org/abs/1908.10561
Publikováno v:
Theory of Computing Systems (2021)
We determine the power of the weighted sum scalarization with respect to the computation of approximations for general multiobjective minimization and maximization problems. Additionally, we introduce a new multi-factor notion of approximation that i
Externí odkaz:
http://arxiv.org/abs/1908.01181
Autor:
P. Guerreiro, A., Cortes, J., Vanderpooten, D., Bazgan, C., Lynce, I., Manquinho, V., Figueira, J.R.
Publikováno v:
In Computers and Operations Research May 2023 153
Autor:
Kerbérénès, Antoine1 (AUTHOR) ankerbe@gmail.com, Vanderpooten, Daniel1 (AUTHOR) daniel.vanderpooten@lamsade.dauphine.fr, Vanpeperstraete, Jean‐Michel2 (AUTHOR) jean-michel.vanpeperstraete@naval-group.com
Publikováno v:
International Transactions in Operational Research. Nov2023, Vol. 30 Issue 6, p3455-3478. 24p. 1 Black and White Photograph, 7 Charts, 3 Graphs.
Publikováno v:
Networks; Jan2025, Vol. 85 Issue 1, p76-90, 15p
Given a finite set $N$ of feasible points of a multi-objective optimization (MOO) problem, the search region corresponds to the part of the objective space containing all the points that are not dominated by any point of $N$, i.e. the part of the obj
Externí odkaz:
http://arxiv.org/abs/1502.06111
Publikováno v:
Mathematical Methods of Operations Research; Aug2024, Vol. 100 Issue 1, p5-25, 21p