On proper and improper efficient solutions of optimal problems with multicriteria

The properness of the efficient solution of the optimal problem with multicriteria has been independently defined by Kuhn and Tucker, Geoffrion, and Klinger. A theorem of Geoffrion describes the relation between Geoffrion's and Kuhn and Tucker's properness. In this paper, the dual part of the theorem is given, and some geometric approach is applied to derive the optimal conditions of proper efficient solutions and improper efficient solutions.     

一般约束最优化的拟乘子—强次可行方向法

本文讨论一般等式和不等式约束的优化问题，首先提出了问题的拟Ｋｕｈｎ－Ｔｕｃｋｅｒ点和拟乘子法两个新概念，然后借助于不等式约束优化问题强次可行方向法的思想和技巧建立问题的两个新算法。     

Isolated efficiency in nonsmooth semi-infinite multi-objective programming

ABSTRACT

In this paper, isolated efficient solutions of a given nonsmooth Multi-Objective Semi-Infinite Programming problem (MOSIP) are studied. Two new Data Qualifications (DQs) are introduced and it is shown that these DQs are, to a large extent, weaker than already known Constraint Qualifications (CQs). The relationships between isolated efficiency and some relevant notions existing in the literature, including robustness, are established. Various necessary and sufficient conditions for characterizing isolated efficient solutions of a general problem are derived. It is done invoking the tangent cones, the normal cones, the generalized directional derivatives, and some gap functions. Using these characterizations, the (strongly) perturbed Karush-Kuhn-Tucker (KKT) optimality conditions for MOSIP are analyzed. Furthermore, it is shown that each isolated efficient solution is a Geoffrion properly efficient solution under appropriate assumptions. Moreover, Kuhn-Tucker (KT) and Klinger properly efficient solutions for a nonsmooth MOSIP are defined and it is proved that each isolated efficient solution is a KT properly efficient solution in general, and a Klinger properly efficient solution under a DQ. Finally, in the last section, the largest isolated efficiency constant for a given isolated efficient solution is determined.     

Some mathematical properties of a DEA model for the joint determination of efficiencies

This paper explores the Kuhn–Tucker conditions and convexity issues in a non-linear DEA model for the joint determination of efficiencies developed by Mar Molinero. It is shown that the usual convexity conditions that apply to Linear Programming problems are satisfied in this case. First order Kuhn–Tucker conditions are derived and interpreted. Estimation strategies are suggested. Some empirical work is reported.     

Some results on linear delay integral equations

Recently Borwein has proposed a definition for extending Geoffrion's concept of proper efficiency to the vector maximization problem in which the domination cone S is any nontrivial, closed convex cone. However, when S is the non-negative orthant, solutions may exist which are proper according to Borwein's definition but improper by Geoffrion's definition. As a result, when S is the non-negative orthant, certain properties of proper efficiency as defined by Geoffrion do not hold under Borwein's definition. To rectify this situation, we propose a definition of proper efficiency for the case when S is a nontrivial, closed convex cone which coincides with Geoffrion's definition when S is the non-negative orthant. The proposed definition seems preferable to Borwein's for developing a theory of proper efficiency for the case when S is a nontrivial, closed convex cone.     

Second-order necessary efficiency conditions for nonsmooth vector equilibrium problems

This paper presents primal and dual second-order Fritz John necessary conditions for weak efficiency of nonsmooth vector equilibrium problems involving inequality, equality and set constraints in terms of the Páles–Zeidan second-order directional derivatives. Dual second-order Karush–Kuhn–Tucker necessary conditions for weak efficiency are established under suitable second-order constraint qualifications.     

Pareto Optimizing and Kuhn–Tucker Stationary Sequences

We study the relation between weakly Pareto minimizing and Kuhn–Tucker stationary nonfeasible sequences for vector optimization under constraints, where the weakly Pareto (efficient) set may be empty. The work is placed in a context of Banach spaces and the constraints are described by a functional taking values in a cone. We characterize the asymptotic feasibility in terms of the constraint map and the asymptotic efficiency via a Kuhn–Tucker system completely approximate, distinguishing the classical bounded case from the nontrivial unbounded one. The latter requires Auslender–Crouzeix type conditions and Ekeland's variational principle for constrained vector problems.     

When the Karush–Kuhn–Tucker Theorem Fails: Constraint Qualifications and Higher-Order Optimality Conditions for Degenerate Optimization Problems

In this paper, we present higher-order analysis of necessary and sufficient optimality conditions for problems with inequality constraints. The paper addresses the case when the constraints are not assumed to be regular at a solution of the optimization problems. In the first two theorems derived in the paper, we show how Karush–Kuhn–Tucker necessary conditions reduce to a specific form containing the objective function only. Then we present optimality conditions of the Karush–Kuhn–Tucker type in Banach spaces under new regularity assumptions. After that, we analyze problems for which the Karush–Kuhn–Tucker form of optimality conditions does not hold and propose necessary and sufficient conditions for those problems. To formulate the optimality conditions, we introduce constraint qualifications for new classes of nonregular nonlinear optimization. The approach of p-regularity used in the paper can be applied to various degenerate nonlinear optimization problems due to its flexibility and generality.     

The Karush–Kuhn–Tucker optimality conditions for fuzzy optimization problems

This paper considers optimization problems with fuzzy-valued objective functions. For this class of fuzzy optimization problems we obtain Karush–Kuhn–Tucker type optimality conditions considering the concept of generalized Hukuhara differentiable and pseudo-invex fuzzy-valued functions.     

Optimality and duality in nonlinear programming involving semilocally B-preinvex and related functions

A nonlinear programming problem is considered where the functions involved are η-semidifferentiable. Fritz John and Karush–Kuhn–Tucker types necessary optimality conditions are obtained. Moreover, a result concerning sufficiency of optimality conditions is given. Wolfe and Mond–Weir types duality results are formulated in terms of η-semidifferentials. The duality results are given using concepts of generalized semilocally B-preinvex functions.     

A special newton-type optimization method

The Kuhn–Tucker conditions of an optimization problem with inequality constraints are transformed equivalently into a special nonlinear system of equations T ₀(z) = 0. It is shown that Newton's method for solving this system combines two valuable properties: The local Q-quadratic convergence without assuming the strict complementary slackness condition and the regularity of the Jacobian of T ₀ at a point z under reasonable conditions, so that Newton’s method can be used also far from a Kuhn–Tucker point     

Necessary conditions for efficiency in terms of the Michel–Penot subdifferentials

In this article, we study a multiobjective optimization problem involving inequality and equality cone constraints and a set constraint in which the functions are either locally Lipschitz or Fréchet differentiable (not necessarily C ¹-functions). Under various constraint qualifications, Kuhn–Tucker necessary conditions for efficiency in terms of the Michel–Penot subdifferentials are established.     

Multiplier Rules and Saddle-Point Theorems for Helbig’s Approximate Solutions in Convex Pareto Problems

This paper deals with approximate Pareto solutions in convex multiobjective optimization problems. We relate two approximate Pareto efficiency concepts: one is already classic and the other is due to Helbig. We obtain Fritz John and Kuhn–Tucker type necessary and sufficient conditions for Helbig’s approximate solutions. An application we deduce saddle-point theorems corresponding to these solutions for two vector-valued Lagrangian functions.     

Multiobjective programming under d-invexity

In this paper, a generalization of convexity is considered in the case of nonlinear multiobjective programming problem where the functions involved are nondifferentiable. By considering the concept of Pareto optimal solution and substituting d-invexity for convexity, the Fritz John type and Karush–Kuhn–Tucker type necessary optimality conditions and duality in the sense of Mond–Weir and Wolfe for nondifferentiable multiobjective programming are given.     

Quasimin and quasisaddlepoint for vector optimization

For a constrained multicriteria optimization problem with differentiable functions, but not assuming any convexity, vector analogs of quasimin, Kuhn-Tucker point, and (suitably defined) vector quasisaddlepoint are shown to be equivalent. A constraint qualification is assumed. Similarly, a proper (by Geoffrion's definition) weak minimum is equivalent to a Kuhn–Tucker point with a strictly positive multiplier for the objective, and also to a vector quasisaddlepoint with an attached stability property. Under generalized invex hypotheses, these properties reduce to proper minimum and stable saddlepoint. Various known results are thus unified.     

On alternative theorems and necessary conditions for efficiency

In this article, we establish theorems of the alternative for a system described by inequalities, equalities and a set inclusion, which are generalizations of Tucker's classical theorem of the alternative, and develop Kuhn–Tucker necessary conditions for efficiency to mathematical programs in normed linear spaces involving inequality, equality and set constraints with positive Lagrange multipliers of all the components of objective functions.     

Optimality criteria in E-convex programming

Necessary and sufficient optimality criteria in nonlinear programming are discussed for a class of E-convex programming problems which is considered more general than a class of convex programming problems. We also modify the Fritz John and the Kuhn–Tucker problems to E-Fritz John and E-Kuhn–Tucker problems.     

Nonsmooth multiobjective programming: strong Kuhn–Tucker conditions

We consider a multiobjective optimization problem with a feasible set defined by inequality and equality constraints and a set constraint, where the objective and constraint functions are locally Lipschitz. Several constraint qualifications are given in such a way that they generalize the classical ones, when the functions are differentiable. The relationships between them are analyzed. Then, we establish strong Kuhn–Tucker necessary optimality conditions in terms of the Clarke subdifferentials such that the multipliers of the objective function are all positive. Furthermore, sufficient optimality conditions under generalized convexity assumptions are derived. Moreover, the concept of efficiency is used to formulate duality for nonsmooth multiobjective problems. Wolf and Mond–Weir type dual problems are formulated. We also establish the weak and strong duality theorems.     

A class of smoothing SAA methods for a stochastic mathematical program with complementarity constraints

A class of smoothing sample average approximation (SAA) methods is proposed for solving the stochastic mathematical program with complementarity constraints (SMPCC) considered by Birbil et al. [S.I. Birbil, G. Gürkan, O. Listes, Solving stochastic mathematical programs with complementarity constraints using simulation, Math. Oper. Res. 31 (2006) 739–760]. The almost sure convergence of optimal solutions of the smoothed SAA problem to that of the true problem is established by the notion of epi-convergence in variational analysis. It is demonstrated that, under suitable conditions, any accumulation point of Karash–Kuhn–Tucker points of the smoothed SAA problem is almost surely a kind of stationary point of SMPCC as the sample size tends to infinity. Moreover, under a strong second-order sufficient condition for SMPCC, the exponential convergence rate of the sequence of Karash–Kuhn–Tucker points of the smoothed SAA problem is investigated through an application of Robinson?s stability theory. Some preliminary numerical results are reported to show the efficiency of proposed method.     

Convexificators and boundedness of the Kuhn–Tucker multipliers set

In this paper we consider a nonsmooth optimization problem with equality, inequality and set constraints. We propose new constraint qualifications and Kuhn–Tucker type necessary optimality conditions for this problem involving locally Lipschitz functions. The main tool of our approach is the notion of convexificators. We introduce a nonsmooth version of the Mangasarian–Fromovitz constraint qualification and show that this constraint qualification is necessary and sufficient for the Kuhn–Tucker multipliers set to be nonempty and bounded.