- Open Access
Second-order duality for nondifferentiable minimax fractional programming problems with generalized convexity
© Khan; licensee Springer. 2013
- Received: 29 June 2013
- Accepted: 12 September 2013
- Published: 8 November 2013
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.
MSC:90C32, 49K35, 49N15.
- minimax programming
- fractional programming
- generalized convexity
It is well known that the minimax fractional programming has wide applications. These types of problems arise in the design of electronic circuits; moreover, 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. For the case of convex differentiable minimax fractional programming, Yadav and Mukherjee  formulated two dual models for the primal problem and derived a duality theorem for convex differentiable minimax fractional programming. A step forward was taken by Chandra and Kumar  who improved the dual formulation of Yadav and Mukherjee. They provided two modified dual problems for minimax fractional programming and proved duality results. Liu and Wu [7, 8] and Ahmad  obtained sufficient optimality conditions and duality for minimax fractional programming under generalized convex type assumptions.
Mangasarian  introduced the notion of second-order duality for nonlinear programs and obtained second-order duality results under certain inequalities. Mond  modified the second-order duality results assuming rather simple inequalities. In this continuation, 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. Recently, Ahmad  has formulated two types of second-order dualities for minimax fractional programming problems and derived weak, strong and strict converse duality theorems under generalized convexity type assumptions. He raised a question as to whether the second-order duality results developed in  hold for nondifferentiable minimax fractional programming problems. In the present paper, a positive answer is given to the question of Ahmad  and a second-order duality for nondifferentiable minimax fractional programming is formulated. The weak, strong and strict converse duality theorems are proved for these programs under the second-order generalized convexity type assumptions.
where Y is a compact subset of , , are functions. C and D are positive semidefinite symmetric matrices. Throughout this paper, we assume that and for all .
Generalized Schwarz inequality
, , .
Definition 2.2 A point is said to be an optimal solution of (NFP) if for each .
In the case where the functions f, g and h in problem (NFP) are continuously differentiable with respect to , Lai et al.  proved the following first-order necessary conditions for optimality of (NFP), which will be required to prove the strong duality theorem.
Theorem 1 (Necessary condition)
Throughout the paper, we assume that ℱ is a sublinear functional. For , let , , ρ, , be real numbers, and let .
If, for a triplet , the set , then we define the supremum over it to be −∞.
Theorem 2 (Weak duality)
for all .
which contradicts (7), since . □
Theorem 3 (Strong duality)
Let be an optimal solution of (NFP), and let , be linearly independent. Then there exist and such that is a feasible solution of (FD). In addition, if the hypotheses of the weak duality theorem are satisfied for all feasible solutions of (FD), then is an optimal solution of (FD), and the two objectives have the same optimal values.
Proof Since is an optimal solution of (NFP) and , are linearly independent then, by Theorem 1, there exist and such that is a feasible solution of (FD) and the two objectives have the same values. Optimality of for (FD) thus follows from the weak duality theorem (Theorem 2). □
Theorem 4 (Strict converse duality)
Then , that is, is an optimal solution of (NFP).
for all , .
which contradicts (7) since . □
In this paper, weak, strong and strict converse duality theorems have been discussed for nondifferentiable minimax fractional programming problems in the framework of generalized convexity type assumptions. This paper has generalized the results of Ahmad .
where , for , , are analytic with respect to W, W is a specified compact subset in , S is a polyhedral cone in , and is analytic. Also, are positive semidefinite Hermitian matrices.
The work is supported by the Deanship of Scientific Research, University of Tabuk, K.S.A.
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