Second order duality for minmax fractional programming

In the present paper, two types of second order dual models are formulated for a minmax fractional programming problem. The concept of η-bonvexity/generalized η-bonvexity is adopted in order to discuss weak, strong and strict converse duality theorems. The research of Z. Husain is supported by the Department of Atomic Energy, Government of India, under the NBHM Post-Doctoral Fellowship Program No. 40/9/2005-R&D II/1739.     

Saddle points and Lagrangian-type duality for discrete minmax fractional subset programming problems with generalized convex functions

In this paper we present four sets of saddle-point-type optimality conditions, construct two Lagrangian-type dual problems, and prove weak and strong duality theorems for a discrete minmax fractional subset programming problem. We establish these optimality and duality results under appropriate (b,?,ρ,θ)-convexity hypotheses.     

Second order duality for nondifferentiable minimax programming problems with generalized convexity

In this paper, we are concerned with a class of nondifferentiable minimax programming problem and its two types of second order dual models. Weak, strong and strict converse duality theorems from a view point of generalized convexity are established. Our study naturally unifies and extends some previously known results on minimax programming.     

Conjugate duality in generalized fractional programming

The concepts of conjugate duality are used to establish dual programs for a class of generalized nonlinear fractional programs. It is now known that, under certain restrictions, a symmetric duality exists for generalized linear fractional programs. In this paper, we establish this symmetric duality for the nonlinear case.     

Second order symmetric duality in nondifferentiable multiobjective fractional programming with cone convex functions

In the present paper, we consider Mond-Weir type nondifferentiable second order fractional symmetric dual programs over arbitrary cones and derive duality results under second order K?F-convexity/K?F-pseudoconvexity assumptions. Our results generalize several known results in the literature.     

Second order symmetric duality in multiobjective programming involving generalized cone-invex functions

In this paper, cone-second order pseudo-invex and strongly cone-second order pseudo-invex functions are defined. A pair of Mond–Weir type second order symmetric dual multiobjective programs is formulated over arbitrary cones. Weak, strong and converse duality theorems are established under aforesaid generalized invexity assumptions. A second self-duality theorem is also given by assuming the functions involved to be skew-symmetric.     

Second order nondifferentiable multiobjective fractional programming under generalized univex functions

In this paper, a new class of second order $(d,\rho,\eta,\theta)$-type 1 univex function is introduced. The Wolfe type second order dual problem (SFD) of the nondifferentiable multiobjective fractional programming problem (MFP) is considered, where the objective and constraint functions involved are directionally differentiable. Also the duality results under second order$(d,\rho,\eta,\theta)$-type 1 univex functions are established.     

Second order symmetric duality in multiobjective programming

A pair of Mond–Weir type multiobjective second order symmetric dual programs are formulated without non-negativity constraints. Weak duality, strong duality and converse duality theorems are established under η-bonvexity and η-pseudobonvexity assumptions. A second order self-duality theorem is given by assuming the functions involved to be skew-symmetric.     

Higher order duality in multiobjective fractional programming with support functions

In this paper a new class of higher order (F,ρ,σ)-type I functions for a multiobjective programming problem is introduced, which subsumes several known studied classes. Higher order Mond-Weir and Schaible type dual programs are formulated for a nondifferentiable multiobjective fractional programming problem where the objective functions and the constraints contain support functions of compact convex sets in Rn. Weak and strong duality results are studied in both the cases assuming the involved functions to be higher order (F,ρ,σ)-type I. A number of previously studied problems appear as special cases.     

Using duality to solve generalized fractional programming problems

In this paper we explore the relations between the standard dual problem of a convex generalized fractional programming problem and the partial dual problem proposed by Barros et al. (1994). Taking into account the similarities between these dual problems and using basic duality results we propose a new algorithm to directly solve the standard dual of a convex generalized fractional programming problem, and hence the original primal problem, if strong duality holds. This new algorithm works in a similar way as the algorithm proposed in Barros et al. (1994) to solve the partial dual problem. Although the convergence rates of both algorithms are similar, the new algorithm requires slightly more restrictive assumptions to ensure a superlinear convergence rate. An important characteristic of the new algorithm is that it extends to the nonlinear case the Dinkelbach-type algorithm of Crouzeix et al. (1985) applied to the standard dual problem of a generalized linear fractional program. Moreover, the general duality results derived for the nonlinear case, yield an alternative way to derive the standard dual of a generalized linear fractional program. The numerical results, in case of quadratic-linear ratios and linear constraints, show that solving the standard dual via the new algorithm is in most cases more efficient than applying directly the Dinkelbach-type algorithm to the original generalized fractional programming problem. However, the numerical results also indicate that solving the alternative dual (Barros et al., 1994) is in general more efficient than solving the standard dual.This research was carried out at the Econometric Institute, Erasmus University Rotterdam, the Netherlands and was supported by the Tinbergen Institute Rotterdam     

Second order duality for multiobjective programming with cone constraints

We focus on second order duality for a class of multiobjective programming problem subject to cone constraints. Four types of second order duality models are formulated. Weak and strong duality theorems are established in terms of the generalized convexity, respectively. Converse duality theorems, essential parts of duality theory, are presented under appropriate assumptions. Moreover, some deficiencies in the work of Ahmad and Agarwal (2010) are discussed.     

Second order duality in fuzzy mathematical programming with fuzzy coefficients

In this paper, the concept of second order convexity of fuzzy mappings with fuzzy order relation under ranking function is used to formulate a second order duality for fuzzy mathematical programming problems with fuzzy coefficients, where all the fuzzy mappings are twice differentiable. Also, Wolfe’s dual of these problems under ranking function is investigated, and finally, an advantage of second order duality with respect to first order duality is stated.     

Second order symmetric duality in non-differentiable multiobjective programming with F-convexity

This paper is concerned with a pair of Mond–Weir type second order symmetric dual non-differentiable multiobjective programming problems. We establish the weak and strong duality theorems for the new pair of dual models under second order F-convexity assumptions. Several results including many recent works are obtained as special cases.     

On semi-infinite minmax programming with generalized invexity

Here, we consider the minmax programming problem with a set of restrictions indexed in a compact. As a novelty, we obtain optimality criteria of the Kuhn--Tucker type involving a limited number of restrictions and prove both necessity and sufficiency under new weaker invexity assumptions. Also some dual problems are introduced and it is proved that the weak and strong duality properties hold within the same environment.     

Optimality conditions for fractional minmax programming

Let V ?H be real separable Hilbert spaces. The abstract wave equation u′' + A(t)u = g(u), where u(t) ?V, A(t) maps V to its dual $\text{V}{}^1$, and g is a nonlinear map from the ball S(R₀) = {u?V: ∥u∥ < R₀} into H, is considered. It is assumed that g is locally Lipschitz in S(R₀) and possibly singular at the boundary. Local existence and continuation theorems are established for the Cauchy problem u(0) = u₀?S(R₀), u′(0) = u₁?H. Global existence is shown for g(u) = εφ(u), where φ has a potential and ε is small. Global nonexistence is shown for g(u) = εφ(u), where φ satisfies an abstract convexity property and ε is large.     

Second order symmetric duality in multiobjective programming involving cones

An attempt is made to remove certain omissions and inconsistencies in the recent work of Mishra and Lai (European J. Oper. Res., 178:20–26, 2007).     

Second order mixed type duality in multiobjective programming problems

Second order mixed type dual is introduced for multiobjective programming problems. Results about weak duality, strong duality, and strict converse duality are established under generalized second order (F,ρ)-convexity assumptions. These results generalize the duality results recently given by Aghezzaf and Hachimi involving generalized first order (F,ρ)-convexity conditions.     

Optimality conditions and duality for nondifferentiable multiobjective fractional programming with generalized convexity

In this paper, we consider nondifferentiable multiobjective fractional programming problems. A concept of generalized convexity, which is called (C,α,ρ,d)-convexity, is first discussed. Based on this generalized convexity, we obtain efficiency conditions for multiobjective fractional programming (MFP). Furthermore, we establish duality results for three types of dual problems of (MFP) and present the corresponding duality theorems.     

Optimality conditions and duality for a minimax fractional programming with generalized convexity

We establish the sufficient conditions for generalized fractional programming from a viewpoint of the generalized convexity. When the sufficient conditions are utilized, the corresponding duality theorems are derived for two types of duals of the generalized fractional programming. We extend the corresponding results of several authors.