Definition and characterization of Geoffrion proper efficiency for real vector optimization with infinitely many criteria
A Engau - Journal of Optimization Theory and Applications, 2015 - Springer
Journal of Optimization Theory and Applications, 2015•Springer
The concept and characterization of proper efficiency is of significant theoretical and
computational interest, in multiobjective optimization and decision-making, to prevent
solutions with unbounded marginal rates of substitution. In this paper, we propose a slight
modification to the original definition in the sense of Geoffrion, which maintains the common
characterizations of properly efficient points as solutions to weighted sums or series and
augmented or modified weighted Tchebycheff norms, also if the number of objective …
computational interest, in multiobjective optimization and decision-making, to prevent
solutions with unbounded marginal rates of substitution. In this paper, we propose a slight
modification to the original definition in the sense of Geoffrion, which maintains the common
characterizations of properly efficient points as solutions to weighted sums or series and
augmented or modified weighted Tchebycheff norms, also if the number of objective …
Abstract
The concept and characterization of proper efficiency is of significant theoretical and computational interest, in multiobjective optimization and decision-making, to prevent solutions with unbounded marginal rates of substitution. In this paper, we propose a slight modification to the original definition in the sense of Geoffrion, which maintains the common characterizations of properly efficient points as solutions to weighted sums or series and augmented or modified weighted Tchebycheff norms, also if the number of objective functions is countably infinite. We give new proofs and counterexamples which demonstrate that such results become invalid for infinitely many criteria with respect to the original definition, in general, and we address the motivation and practical relevance of our findings for possible applications in stochastic optimization and decision-making under uncertainty.
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