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Computers & Mathematics with Applications
Article
License: Elsevier Non-Commercial
Data sources: UnpayWall
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Computers & Mathematics with Applications
Article . 1999
License: Elsevier Non-Commercial
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Computers & Mathematics with Applications
Article . 1999 . Peer-reviewed
License: Elsevier Non-Commercial
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ε-Optimality and duality for multiobjective fractional programming

Authors: Liu, Jen-Chwan; Yokoyama, K.;

ε-Optimality and duality for multiobjective fractional programming

Abstract

AbstractUsing the scalar ε-parametric approach, we establish the Karush-Kuhn-Tucker (which we call KKT) necessary and sufficient conditions for an ε-Pareto optimum of nondifferentiable multiobjective fractional objective functions subject to nondifferentiable convex inequality constraints, linear equality constraints, and abstract constraints. These optimality criteria are utilized as a basis for constructing one duality model with appropriate duality theorems. Subsequently, we employ scalar exact penalty function to transform the multiobjective fractional programming problem to an unconstrained problem. Under this case, we derive the KKT necessary and sufficient conditions without a constraint qualification for ε-Pareto optimality of multiobjective fractional programming.

Keywords

Computational Mathematics, Computational Theory and Mathematics, Modelling and Simulation, Penalty functions, ε-Pareto optimality, ε-Parametric approach

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
24
Top 10%
Top 10%
Average
hybrid