Duality for semi-infinite programming problems involving -invex functions
© Jayswal et al.; licensee Springer 2013
Received: 12 March 2013
Accepted: 10 April 2013
Published: 23 April 2013
The present paper is framed to study weak, strong and strict converse duality relations for a semi-infinite programming problem and its Wolfe and Mond-Weir-type dual programs under generalized -invexity.
MSC:90C32, 49K35, 49N15.
The root of optimization theory is penetrating into other branch of applied sciences at a rapid pace. Semi-infinite programming is a special case of bilevel programs (multilevel programming) in which lower-level variables do not participate in the objective function. In 1962, the theory of semi-infinite programming (SIP) was developed by Charnes et al. . There are many practical as well as theoretical problems in which constraint depend on time and space and thus can be formulated as semi-infinite programming problems. In recent past, semi-infinite programming has became one of the most interesting research topic in the field of operation research as it has wide variety of applications in control of robots , transportation theory , eigenvalue computations , wavelet filter design [5, 6], statistical design , etc. Duality of semi-infinite programming arises in the theory of systems of linear inequalities, in the theory of uniform approximations of functions and in the classical theory of moments.
In the course of generalization of convex functions, Avriel  first introduced the definition of r-convex functions and established some characterizations and the relations between r-convexity and other generalization of convexity. Antczak  introduced the concept of a class of r-preinvex functions, which is a generalization of r-convex functions and preinvex functions, and obtained some optimality results under r-preinvexity assumption for constrained optimization problems. Lee and Ho  established necessary and sufficient conditions for efficiency of multiobjective fractional programming problems involving r-invex functions and investigated the parametric, Wolfe and Mond-Weir-type dual for multiobjective fractional programming problems concerning r-invexity. In order to generalize the notion of invex and pre-invex functions, Antczak  introduced p-invex sets and -invex functions and derived sufficient optimality conditions for a nonlinear programming problem involving -invex functions. Gupta and Kailey  introduced a new pair of second-order multiobjective symmetric dual programs over arbitrary cones and derived appropriate duality theorems under -bonvexity assumptions.
Many practical and real situations give rise to logarithmic and exponential functions. Keeping this point of view, Yuan et al.  introduced locally -preinvex functions and locally -invex sets and derived necessary and sufficient optimality conditions for nonlinear programming problems. One of the major step is taken by Liu et al.  in the direction of obtaining sufficient optimality conditions for multiple objective programming problem and multiobjective fractional programming problem involving -invex functions.
Taking into account the importance of duality results in optimization theory (see [9, 11, 15–17]), we generalize the notion of -invex functions introduced by Yuan et al.  to (strict) -pseudoinvex and -quasiinvex functions and derive duality results for semi-infinite programming problems.
The rest of the paper is organized as follows: In Section 2, we focus on some notation and definitions. In Sections 3 and 4, weak, strong and strict converse duality theorems are established for Wolfe and Mond-Weir-type dual programs under generalized -invexity. Conclusion and future works are given in Section 5.
2 Notation and preliminaries
Throughout the paper, let be the n-dimensional Euclidean space, and .
Definition 2.1 
where and .
Remark 2.1 It is understood that the logarithm and the exponentials appearing in the above definitions are taken to be componentwise.
Throughout the paper, we take X to be a -invex set unless otherwise specified, right differentiable at 0 with respect to the variable λ for each given pair , and is differentiable function on X. The symbol denotes the right derivative of at 0 with respect to the variable λ for each given pair ; denotes the differential of f at x, and so denotes .
Remark 2.2 All the theorems in the subsequent parts of this paper will be proved only in the case when . The proofs in other cases are easier than this since only changes arise from form of inequality. Moreover, without loss of generality, we shall assume that (in the case when , the direction some of the inequalities in the proof of the theorems should be changed to the opposite one).
Definition 2.3 
If the above inequalities are satisfied at any point then f is said to be -invex (strictly -invex) on X.
Now, we introduce the generalized -invex function as follows.
If the above inequalities are satisfied at any point then f is said to be -pseudoinvex on X.
If the above inequalities are satisfied at any point then f is said to be strict -pseudoinvex on X.
If the above inequalities are satisfied at any point then f is said to be -quasiinvex on X.
where I is an index set which is possibly infinite, f and , are differentiable functions from to .
3 First duality model
where and for finitely many .
Theorem 3.1 (Weak duality)
which contradicts (1). This completes the proof. □
The proof of the following theorem is similar to Theorem 3.1, and hence being omitted.
Theorem 3.2 (Weak duality)
Theorem 3.3 (Strong duality)
Let be an optimal solution for (SIP) and satisfies a suitable constraints qualification for (SIP). Then there exists , such that is feasible for (WSID). If any of the weak duality in Theorems 3.1 or 3.2 also holds, then is an optimal solution for (WSID).
which gives that the is feasible for (WSID). The optimality of for (WSID) follows from weak duality theorems. This completes the proof. □
Theorem 3.4 (Strict converse duality)
Now, we assume that and exhibit a contradiction.
which contradicts the assumption that . Hence . This completes the proof. □
We now prove the duality relations for the following Mond-Weir-type dual problem.
4 Second duality model
where and for finitely many .
Theorem 4.1 (Weak duality)
which contradicts (3). This completes the proof. □
The proof of the following theorem is similar to Theorem 4.1, and hence being omitted.
Theorem 4.2 (Weak duality)
Theorem 4.3 (Strong duality)
Let be an optimal solution for (SIP) and satisfies a suitable constraints qualification for (SIP). Then there exists , such that is feasible for (MWSID). If any of the weak duality in Theorems 4.1 or 4.2 also holds, then is an optimal solution for (MWSID).
which gives that the is feasible for (MWSID). The optimality of for (MWSID) follows from weak duality theorems. This completes the proof. □
Theorem 4.4 (Strict converse duality)
Now, we assume that and exhibit a contradiction.
which contradicts the fact that . Hence, . This completes the proof. □
In the present paper, we introduced generalized -invex functions and consider two types of dual program for a class of semi-infinite programming problem to establish the weak, strong and strict converse duality theorems assuming the functions involved to be generalized -invex functions. In fact, a lot of efforts have been taken to extend some known results for generalized invex functions, for example, see [9, 11, 14, 15]. That is why we conclude that this paper enriched optimization theory as far as mathematics is concerned. Although there are some difficulties (like constructing the suitable examples or counter examples to show the existence), the semi-infinite programming problems involving the generalized invex functions are very interesting. As a future scope, the authors would like to extend the results to fractional semi-infinite programming problem.
The research of the first author is supported by the Indian School of Mines, Dhanbad under FRS(17)/2010-11/AM.
- Charnes A, Cooper WW, Kortanek K: A duality theory for convex programs with convex constraints. Bull. Am. Math. Soc. 1962, 68: 605–608. 10.1090/S0002-9904-1962-10870-2MathSciNetView ArticleGoogle Scholar
- Demeulenaere B, Schutter JD, Swevers J: Robust high-order repetitive control: optimal performance trade-offs. Automatica 2008, 44: 2628–2634. 10.1016/j.automatica.2008.02.028View ArticleGoogle Scholar
- Kortanek K, Yamasaki M: Semi-infinite transportation problems. J. Math. Anal. Appl. 1982, 88: 555–565. 10.1016/0022-247X(82)90214-1MathSciNetView ArticleGoogle Scholar
- Hettich R, Zencke P: Two Case-Studies in Parametric Semi-Infinite Programming. Lecture Notes in Control and Information Sciences 66. Systems and Optimization 1985, 132–155.View ArticleGoogle Scholar
- Kirac A: Theory and design of optimum FIR compaction filters. IEEE Trans. Signal Process. 1998, 46: 903–917. 10.1109/78.668545View ArticleGoogle Scholar
- Kortanek K, Moulin P: Semi-infinite programming in orthogonal wavelet filter design. Nonconvex Optim. Appl. In Semi-Infinite Programming. Edited by: Reemtsen R, Rückmann J-J. Kluwer Academic, Dordrecht; 1998:323–357.View ArticleGoogle Scholar
- Gürtuna F: Duality of ellipsoidal approximations via semi-infinite programming. SIAM J. Optim. 2010, 20: 1421–1438. 10.1137/080717973View ArticleGoogle Scholar
- Avriel M: r -Convex functions. Math. Program. 1972, 2: 309–323. 10.1007/BF01584551MathSciNetView ArticleGoogle Scholar
- Antczak T: r -Preinvexity and r -invexity in mathematical programming. Comput. Math. Appl. 2005, 50: 551–566. 10.1016/j.camwa.2005.01.024MathSciNetView ArticleGoogle Scholar
- Lee JC, Ho SC: Optimality and duality for multiobjective fractional problems with r -invexity. Taiwan. J. Math. 2008, 12: 719–740.MathSciNetGoogle Scholar
- Antczak T:-Invex sets and functions. J. Math. Anal. Appl. 2001, 263: 355–379. 10.1006/jmaa.2001.7574MathSciNetView ArticleGoogle Scholar
- Gupta SK, Kailey N: Second-order multiobjective symmetric duality involving cone-bonvex functions. J. Glob. Optim. 2013, 55: 125–140. 10.1007/s10898-012-9878-3MathSciNetView ArticleGoogle Scholar
- Yuan DH, Liu XL, Yang SY, Nyamsuren D, Altannar C:Optimality conditions and duality for nonlinear programming problems involving locally -pre-invex functions and -invex sets. Int. J. Pure Appl. Math. 2007, 41: 561–576.MathSciNetGoogle Scholar
- Liu X, Yuan D, Yang S, Lai G:Multiple objective programming involving differentiable -invex functions. CUBO 2011, 13: 125–136. 10.4067/S0719-06462011000100008MathSciNetView ArticleGoogle Scholar
- Ahmad I, Gupta SK, Kailey N, Agarwal RP: Duality in nondifferentiable minimax fractional programming with B - -invexity. J. Inequal. Appl. 2011., 2011: Article ID 75Google Scholar
- Ahmad I, Husain Z:Second order -convexity and duality in multiobjective programming. Inf. Sci. 2006, 176: 3094–3103. 10.1016/j.ins.2005.08.003MathSciNetView ArticleGoogle Scholar
- Soleimani-damaneh M, Sarabi ME: Sufficient conditions for nonsmooth r -invexity. Numer. Funct. Anal. Optim. 2008, 29: 674–686. 10.1080/01630560802099886MathSciNetView ArticleGoogle Scholar
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