Second-order duality for a nondifferentiable minimax fractional programming under generalized α-univexity
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In this paper, we concentrate our study to derive appropriate duality theorems for two types of second-order dual models of a nondifferentiable minimax fractional programming problem involving second-order α-univex functions. Examples to show the existence of α-univex functions have also been illustrated. Several known results including many recent works are obtained as special cases.
MSC:49J35, 90C32, 49N15.
Keywordsminimax programming fractional programming nondifferentiable programming second-order duality α-univexity
After Schmitendorf , who derived necessary and sufficient optimality conditions for static minimax problems, much attention has been paid to optimality conditions and duality theorems for minimax fractional programming problems [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]. For the theory, algorithms, and applications of some minimax problems, the reader is referred to .
where Y is a compact subset of , , are twice continuously differentiable on and is twice continuously differentiable on , B, and D are a positive semidefinite matrix, , and for each , where .
Motivated by [7, 14, 15], Yang and Hou  formulated a dual model for fractional minimax programming problem and proved duality theorems under generalized convex functions. Ahmad and Husain  extended this model to nondifferentiable and obtained duality relations involving -pseudoconvex functions. Jayswal  studied duality theorems for another two duals of (P) under α-univex functions. Recently, Ahmad et al. derived the sufficient optimality condition for (P) and established duality relations for its dual problem under -invexity assumptions. The papers [2, 4, 5, 6, 7, 11, 12, 13, 14, 15, 17] involved the study of first-order duality for minimax fractional programming problems.
The concept of second-order duality in nonlinear programming problems was first introduced by Mangasarian . One significant practical application of second-order dual over first-order is that it may provide tighter bounds for the value of objective function because there are more parameters involved. Hanson  has shown the other advantage of second-order duality by citing an example, that is, if a feasible point of the primal is given and first-order duality conditions do not apply (infeasible), then we may use second-order duality to provide a lower bound for the value of primal problem.
Recently, several researchers [3, 8, 9, 10, 16] considered second-order dual for minimax fractional programming problems. Husain et al. first formulated second-order dual models for a minimax fractional programming problem and established duality relations involving η-bonvex functions. This work was later on generalized in  by introducing an additional vector r to the dual models, and in Sharma and Gulati  by proving the results under second-order generalized α-type I univex functions. The work cited in [3, 8, 10, 16] involves differentiable minimax fractional programming problems. Recently, Hu et al. proved appropriate duality theorems for a second-order dual model of (P) under η-pseudobonvexity/η-quasibonvexity assumptions. In this paper, we formulate two types of second-order dual models for (P) and then derive weak, strong, and strict converse duality theorems under generalized α-univexity assumptions. Further, examples have been illustrated to show the existence of second-order α-univex functions. Our study extends some of the known results of the literature [5, 6, 11, 12, 14].
2 Notations and preliminaries
But every α-univex function need not be invex. To show this, consider the following example.
Lemma 2.1 (Generalized Schwartz inequality)
The equality holds iffor some.
Following Theorem 2.1 (, Theorem 3.1) will be required to prove the strong duality theorem.
Theorem 2.1 (Necessary condition)
If in addition, we insert the condition , then the result of Theorem 2.1 still holds.
3 Model I
If the set , we define the supremum of over equal to −∞.
Remark 3.1 If , then using (3.3), the above dual model reduces to the problems studied in [6, 11, 12]. Further, if B and D are zero matrices of order n, then (DM1) becomes the dual model considered in .
Next, we establish duality relations between primal (P) and dual (DM1).
Theorem 3.1 (Weak duality)
is second-orderα-univex atz,
This contradicts (3.7), hence the result. □
Theorem 3.2 (Strong duality)
Letbe an optimal solution for (P) and let, be linearly independent. Then there existand, such thatis feasible solution of (DM1) and the two objectives have same values. If, in addition, the assumptions of Theorem 3.1 hold for all feasible solutionsof (DM1), thenis an optimal solution of (DM1).
Proof Since is an optimal solution of (P) and , are linearly independent, then by Theorem 2.1, there exist and such that is feasible solution of (DM1) and the two objectives have same values. Optimality of for (DM1), thus follows from Theorem 3.1. □
Theorem 3.3 (Strict converse duality)
is strictly second-orderα-univex at,
, are linearly independent,
which contradicts (3.8), hence the result. □
4 Model II
If the set is empty, we define the supremum in (DM2) over equal to −∞.
Remark 4.1 If , then using (4.3), the above dual model becomes the dual model considered in [5, 11, 12]. In addition, if B and D are zero matrices of order n, then (DM2) reduces to the problem studied in .
Now, we obtain the following appropriate duality theorems between (P) and (DM2).
Theorem 4.1 (Weak duality)
is second-orderα-univex atz,
which contradicts (4.5). This proves the theorem. □
By a similar way, we can prove the following theorems between (P) and (DM2).
Theorem 4.2 (Strong duality)
Letbe an optimal solution for (P) and let, be linearly independent. Then there existand, such thatis feasible solution of (DM2) and the two objectives have same values. If, in addition, the assumptions of weak duality hold for all feasible solutionsof (DM2), thenis an optimal solution of (DM2).
Theorem 4.3 (Strict converse duality)
is strictly second-orderα-univex atz,
are linearly independent,
5 Concluding remarks
In the present work, we have formulated two types of second-order dual models for a nondifferentiable minimax fractional programming problems and proved appropriate duality relations involving second-order α-univex functions. Further, examples have been illustrated to show the existence of such type of functions. Now, the question arises whether or not the results can be further extended to a higher-order nondifferentiable minimax fractional programming problem.
The authors wish to thank anonymous reviewers for their constructive and valuable suggestions which have considerably improved the presentation of the paper. The second author is also thankful to the Ministry of Human Resource Development, New Delhi (India) for financial support.
- 2.Ahmad I: Optimality conditions and duality in fractional minimax programming involving generalized ρ -invexity. Int. J. Manag. Syst. 2003, 19: 165–180.Google Scholar
- 3.Ahmad I: On second-order duality for minimax fractional programming problems with generalized convexity. Abstr. Appl. Anal. 2011. doi:10.1155/2011/563924Google Scholar
- 4.Ahmad I, Gupta SK, Kailey N, Agarwal RP:Duality in nondifferentiable minimax fractional programming with -invexity. J. Inequal. Appl. 2011. doi:10.1186/1029–242X-2011–75Google Scholar
- 20.Hanson MA: Second order invexity and duality in mathematical programming. Opsearch 1993, 30: 311–320.Google Scholar
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