Optimality and Duality for Invex Nonsmooth Multiobjective programming problems

We consider nonsmooth multiobjective programming problems with inequality and equality constraints involving locally Lipschitz functions. Several sufficient optimality conditions under various (generalized) invexity assumptions and certain regularity conditions are presented. In addition, we introduce a Wolfe-type dual and Mond–Weir-type dual and establish duality relations under various (generalized) invexity and regularity conditions.     

Optimality Conditions and Duality for Nonsmooth Fractional Continuous-Time Problems

In this paper, we consider a class of nonsmooth fractional continuous-time problems. Optimality conditions under certain structure of generalized invexity are derived for this class. Subsequently, two parameter-free dual models are formulated. Finally weak, strong, and strict converse duality theorems are proved in the framework of generalized invexity.     

Optimality and Duality for Complex Nondifferentiable Fractional Programming

Both parametric and nonparametric necessary and sufficient optimality conditions are established for a class of complex nondifferentiable fractional programming problems containing generalized convex functions. Subsequently, these optimality criteria are utilized as a basis for constructing one parametric and two other parameter-free dual models with appropriate duality theorems.     

Minimax Fractional Programming for <Emphasis Type="Italic">n</Emphasis>-Set Functions and Mixed-Type Duality under Generalized Invexity

We establish the sufficient optimality conditions for a minimax programming problem involving p fractional n-set functions under generalized invexity. Using incomplete Lagrange duality, we formulate a mixed-type dual problem which unifies the Wolfe type dual and Mond-Weir type dual in fractional n-set functions under generalized invexity. Furthermore, we establish three duality theorems: weak, strong, and strict converse duality theorem, and prove that the optimal values of the primal problem and the mixed-type dual problem have no duality gap under extra assumptions in the framework. This research was partly supported by the National Science Council, NSC 94-2115-M-033-003, Taiwan.     

Duality Without a Constraint Qualification for Minimax Fractional Programming

Using a parametric approach, we establish the necessary and sufficient conditions for generalized fractional programming without the need of a constraint qualification. Subsequently, these optimality criteria are utilized as a basis for constructing a parametric dual model and two other parameter-free dual models. Several duality theorems are established.     

Optimality conditions and duality models for generalized fractional programming problems containing locally subdifferentiable and ρ:-convex functions

Both parametric and nonparametric necessary and sufficient optimality conditions are established for a class of nonsmooth generalized fractional programming problems containing ρ-convex functions. Subsequently, these optimality criteria are utilized as a basis for constructing two parametric and four parameter-free duality models and proving appropriate duality theorems. Several classes of generalized fractional programming problems, including those with arbitrary norms, square roots of positive semidefinite quadratic forms, support functions, continuous max functions, and discrete max functions, which can be viewed as special cases of the main problem are briefly discussed. The optimality and duality results developed here also contain, as special cases, similar results for nonsmooth problems with fractional, discrete max, and conventional objective functions which are particular cases of the main problem considered in this paper     

OPTIMALITY CONDITIONS AND DUALITY MODELS FOR A CLASS OF NONSMOOTH CONTINUOUS-TIME GENERALIZED FRACTIONAL PROGRAMMING PROBLEMS

Abstract

Both parametric and parameter-free stationary-point-type and saddle-point-type necessary and sufficient optimality conditions are established for a class of nonsmooth continuous-time generalized fractional programming problems with Volterra-type integral inequality and nonnegativity constraints. These optimality criteria are then utilized for constructing ten parametric and parameter-free Wolfe-type and Lagrangian-type dual problems and for proving weak, strong, and strict converse duality theorems. Furthermore, it is briefly pointed out how similar optimality and duality results can be obtained for two important special cases of the main problem containing arbitrary norms and square roots of positive semidefinite quadratic forms. All the results developed here are also applicable to continuous-time programming problems with fractional, discrete max, and conventional objective functions, which are special cases of the main problem studied in this paper.     

KKT Optimality Conditions and Nonsmooth Continuous Time Optimization Problems

We introduce a concept of generalized invexity for the nonsmooth continuous time optimization problems, namely, the concept of Karush-Kuhn-Tucker (KKT) invexity. Then, we prove that this notion is necessary and sufficient for global optimality of a KKT point. We also extend the notion of weak-invexity for nonsmooth continuous time optimization problems. Further, we show that weak-invexity is a necessary and sufficient condition for weak duality.     

On variational problems involving higher order derivatives

Fritz John, and Karush-Kuhn-Tucker type optimality conditions for a constrained variational problem invoving higher order derivatives are obtained. As an application of these Karush-Kuhn-Tucker type optimality conditions, Wolfe and Mond-Weir type duals are formulated, and various duality relationships between the primal problem and each of the duals are established under invexity and generalized invexity. It is also shown that our results can be viewed as dynamic generalizations of those of the mathematical programming already reported in the literature.     

Optimality and duality for nonsmooth multiobjective fractional programming with mixed constraints

We consider nonsmooth multiobjective fractional programming problems with inequality and equality constraints. We establish the necessary and sufficient optimality conditions under various generalized invexity assumptions. In addition, we formulate a mixed dual problem corresponding to primal problem, and discuss weak, strong and strict converse duality theorems. This research was partially supported by Project no. 850203 and Center of Excellence for Mathematics, University of Isfahan, Iran.     

Necessary and sufficient optimality conditions for fractional multi-objective problems

Using a special scalarization, we give necessary optimality conditions for fractional multiobjective optimization problems. Under a generalized invexity, sufficient optimality conditions are also given. All over the article, the data are assumed to be continuous but not necessarily Lipschitz.     

Generalized invexity for nonsmooth vector-valued mappings

We define four types of invexity for Lipschitz vector-valued mappings from R^p to R^q that generalize previous definitions of invexity in the differentiable setting. After establishing relationships between the various definitions, we show the importance of the concept of nonsmooth invexity in the field of optimization. In particular, we obtain conditions sufficient for optimality in unconstrained and cone-constrained nondifferentiable programming that are weaker than previous conditions presented in the literature; we also obtain weak and strong duality results.     

Optimality and duality for nonsmooth multiobjective programming problems with V-r-invexity

In the paper, we consider a class of nonsmooth multiobjective programming problems in which involved functions are locally Lipschitz. A new concept of invexity for locally Lipschitz vector-valued functions is introduced, called V-r-invexity. The generalized Karush–Kuhn–Tuker necessary and sufficient optimality conditions are established and duality theorems are derived for nonsmooth multiobjective programming problems involving V-r-invex functions (with respect to the same function η).     

ρ-Invex Functions and ( F , ρ)Convex Functions: Properties and Equivalences

-invexity, -pseudo invexity and -quasi invexity (and their extentions to nondifferentiable Lipschitz functions) have been used to weaken the assumption of convexity in solving duality problems or to state sufficient optimality conditions in nonlinear programming. An attempt to generalize further these concepts has been done with the introduction of ( F , )convexity for differentiable and nondifferentiable Lipschitz functions. Theorems and results regarding both the duality problems and the sufficience of Kuhn-Tucker conditions have been reproduced for these new classes of functions. The aim of this article is to show that for both differentiable and nondifferentiable Lipschitz functions ( F , )convexity is not a generalization of -invexity, but these families of functions coincide.     

Nonsmooth multiobjective continuous-time problems with generalized invexity

A nonsmooth multiobjective continuous-time problem is considered. The definition of invexity and its generalizations for continuous-time functions are extended. Then, optimality conditions under generalized invexity assumptions are established. Subsequently, these optimality conditions are utilized as a basis for formulating dual problems. Duality results are also obtained for Wolfe as well as Mond-Weir type dual, using generalized invexity on the functions involved.      

Optimality Criteria and Duality in Multiobjective Programming Involving Nonsmooth Invex Functions

In this paper a generalization of invexity is considered in a general form, by means of the concept of K-directional derivative. Then in the case of nonlinear multiobjective programming problems where the functions involved are nondifferentiable, we established sufficient optimality conditions without any convexity assumption of the K-directional derivative. Then we obtained some duality results.     

Optimality conditions and strong duality in abstract and continuous-time linear programming

In this work, optimality conditions for infinite-dimensional linear programs are considered. Strong duality as an optimality condition is investigated. A new approach to duality in the form of positive extendability of linear functionals is proposed. A necessary and sufficient condition for duality in the form of a boundedness test of a related linear program is developed. Elaborating on the continuous time framework, counter cases where duality is not valid are given. In lieu of duality, other generalized duality conditions are proposed for the purpose of testing the optimality of a solution.     

On the optimality of some classes of invex problems

In this paper we define two notions: Kuhn–Tucker saddle point invex problem with inequality constraints and Mond–Weir weak duality invex one. We prove that a problem is Kuhn–Tucker saddle point invex if and only if every point, which satisfies Kuhn–Tucker optimality conditions forms together with the respective Lagrange multiplier a saddle point of the Lagrange function. We prove that a problem is Mond–Weir weak duality invex if and only if weak duality holds between the problem and its Mond–Weir dual one. Additionally, we obtain necessary and sufficient conditions, which ensure that strong duality holds between the problem with inequality constraints and its Wolfe dual. Connections with previously defined invexity notions are discussed.     

Necessary and Sufficient Conditions for Minimax Fractional Programming

We establish the necessary and sufficient optimality conditions for a class of nondifferentiable minimax fractional programming problems solving generalized convex functions. Subsequently, we apply the optimality conditions to formulate one parametric dual problem and we prove weak duality, strong duality, and strict converse duality theorems.     

非凸分式优化问题的最优性充分条件和对偶

通过引入广义弧连通概念,在Rn空间中,研究极大极小非凸分式规划问题的最优性充分条件及其对偶问题.首先获得了极大极小非凸分式规划问题的最优性充分条件;然后建立分式规划问题的一个对偶模型并得到了弱对偶定理,强对偶定理和逆对偶定理.