Abstract
In the present paper, we are concerned with second-order duality for nondifferentiable minimax fractional programming under the second-order generalized convexity type assumptions. The weak, strong and converse duality theorems are proved. Results obtained in this paper extend some previously known results on nondifferentiable minimax fractional programming in the literature.
Highlights
It is well known that the minimax fractional programming has wide applications
These types of problems arise in the design of electronic circuits; minimax fractional programming problems appear in formulation of discrete and continuous rational approximation problems with respect to the Chebyshev norm [ ], continuous rational games [ ], multiobjective programming [ ] and engineering design as well as some portfolio solution problems discussed by Bajaona-Xandari and Martinez-Legaz [ ]
In the last few years, much attention has been paid to optimality conditions and duality theorems for the minimax fractional programming problems
Summary
It is well known that the minimax fractional programming has wide applications. These types of problems arise in the design of electronic circuits; minimax fractional programming problems appear in formulation of discrete and continuous rational approximation problems with respect to the Chebyshev norm [ ], continuous rational games [ ], multiobjective programming [ ] and engineering design as well as some portfolio solution problems discussed by Bajaona-Xandari and Martinez-Legaz [ ].In the last few years, much attention has been paid to optimality conditions and duality theorems for the minimax fractional programming problems. Bector and Chandra [ ] formulated a second-order dual for a fractional programming problem and obtained usual duality results under the assumptions [ ] by naming these as convex/concave functions.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
