Please use this identifier to cite or link to this item: http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/173
Title: Strong duality for robust minimax fractional programming problems
Authors: Jeyakumar, V
Li, G.Y
Srisatkunarajah, S
Keywords: Minimax fractional programming under uncertainty;Minimax linear fractional programming with uncertainty;Robust optimization; Strong duality
Issue Date: Jul-2013
Publisher: Elsevier B.V
Abstract: We develop a duality theory for minimax fractional programming problems in the face of data uncertainty both in the objective and constraints. Following the framework of robust optimization, we establish strong duality between the robust counterpart of an uncertain minimax convex-concave fractional program, termed as robust minimax fractional program, and the optimistic counterpart of its uncertain conventional dual program, called optimistic dual. In the case of a robust minimax linear fractional program with scenario uncertainty in the numerator of the objective function, we show that the optimistic dual is a simple linear program when the constraint uncertainty is expressed as bounded intervals. We also show that the dual can be reformulated as a second-order cone programming problem when the constraint uncertainty is given by ellipsoids. In these cases, the optimistic dual problems are computationally tractable and their solutions can be validated in polynomial time. We further show that, for robust minimax linear fractional programs with interval uncertainty, the conventional dual of its robust counterpart and the optimistic dual are equivalent.
URI: http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/173
ISSN: 03772217
Appears in Collections:Mathematics and Statistics

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