锥约束非光滑多目标优化问题的对偶及最优性条件

研究了一类涉广义不变凸锥约束非光滑多目标优化问题(记为(MOP)),结合Craven与Yang广义选择定理,建立了该优化问题的Kuhn-Tucker型最优性充分必要条件以及其鞍点与弱有效解之间的关系,给出了(MOP)的Wolfe型与Mond-Weir型弱、强以及逆对偶理论.     

一致Fb,ε-凸多目标半无限规划ε-有效解的充分性

利用C larke广义梯度,定义了一致Fb,ε-凸函数和严格一致Fb,ε-凸函数,得到了涉及这些广义凸性和一致Fb-伪凸函数、一致Fb-拟凸函数等一些非光滑非凸函数的一类非光滑多目标半无限规划的一些K uhn-Tucker型充分性条件.     

非光滑(F,ρ,)θ-d一致不变凸广义分式规划的对偶性

结合F-凸,η-不变凸及d一致不变凸的概念给出了非光滑广义(F,ρ,θ)-d一致不变凸函数;就一类在凸集C上目标函数为Lipschitz连续的带有可微不等式约束的广义分式规划,提出一个对偶,并利用在广义Kuhn-Tucker约束品性或广义Arrow-Hurwicz-Uzawa约束品性的条件下得到的最优性必要条件,证明相应的弱对偶定理、强对偶定理及严格逆对偶定理.     

非光滑非凸多目标规划的Wolfe型对偶性

本文利用作者提出的某些非凸概念,讨论了非光滑非凸多目标规划的Wolfe型对偶性.     

关于非李普希兹规划的Kuhn-Tucker最优性条件

本文利用Pshenichnyi引进的上凸逼近和广义次梯度,讨论了非李普希兹规划(目标函数或约束函数不是局部李普希兹函数)的Kuhn-Tucker最优性条件及精确罚函数存在性条件。     

非光滑半无限多目标优化问题的Lagrange鞍点准则

本文研究了一类非光滑半无限多目标优化问题,并讨论它的鞍点准则.首先,定义了这类半无限多目标优化问题的标量和向量情形的Lagrange函数和鞍点;其次,分别讨论了标量和向量情形的鞍点准则的必要性;最后,在非光滑(Φ,ρ)-不变凸性假设下给出这两种情形的鞍点准则的充分性.     

拟不变凸集值优化的Kuhn-Tucker条件与Wolfe对偶

研究了拟不变凸集值优化最优性的Kuhn-Tucker条件及Wolfe型对偶问题．首先引进了alpha-阶G-拟不变凸集和alpha-阶S-拟不变凸集值函数的概念,由此研究了alpha-阶G-拟不变凸集所对应的伴随切锥及alpha-阶伴随导数的性质;最后,借助alpha-阶伴随切导数刻画了alpha-阶S-拟不变凸集值优化最优性的Kuhn-Tucker条件和Wolfe型对偶．     

近似锥-次类凸集值优化的严有效性

在Hausdorff局部凸拓扑线性空间中考虑约束集值优化问题(VP)的严有效性.在近似锥-次类凸假设下,利用凸集分离定理,分别得到了Kuhn-Tucker型和Lagrange型最优性条件,建立了与(VP)等价的两种形式的无约束优化.     

非光滑多目标规划的二阶充分性

本文给出了古典方向导数的一、二阶定义，借助于平均值理论给出非光滑多目标规划的二阶充分性条件。     

非光滑多目标规划的最优性条件

近年来,关于非光滑最优化问题的研究十分活跃,尤其是对单目标规划,出现了很多成果.关于非光滑多目标规划,也有不少工作.然而,以前的研究,多是利用次微分(凸规划情形)或广义梯度进行讨论的.文[1]利用古典方向导数给出了非光滑无约束单目标规划的最优性条件,并指出,在广泛的非光滑函数类中,方向导数是存在的.本文则以更     

Optimality conditions for directionally differentiable multi-objective programming problems

This paper is concerned with the optimality for multi-objective programming problems with nonsmooth and nonconvex (but directionally differentiable) objective and constraint functions. The main results are Kuhn-Tucker type necessary conditions for properly efficient solutions and weakly efficient solutions. Our proper efficiency is a natural extension of the Kuhn-Tucker one to the nonsmooth case. Some sufficient conditions for an efficient solution to be proper are also given. As an application, we derive optimality conditions for multi-objective programming problems including extremal-value functions.This work was done while the author was visiting George Washington University, Washington, DC.     

Necessary optimality conditions for nonsmooth semi-infinite programming problems

In this paper, for a nonsmooth semi-infinite programming problem where the objective and constraint functions are locally Lipschitz, analogues of the Guignard, Kuhn-Tucker, and Cottle constraint qualifications are given. Pshenichnyi-Levin-Valadire property is introduced, and Karush-Kuhn-Tucker type necessary optimality conditions are derived.     

Second-Order Conditions for Efficiency in Nonsmooth Multiobjective Optimization Problems

In this paper, we are concerned with a nonsmooth multiobjective optimization problem with inequality constraints. We introduce a second-order constraint qualification, which is a generalization of the Abadie constraint qualification and derive second-order Kuhn-Tucker type necessary conditions for efficiency under the constraint qualification. Moreover, we give some conditions which ensure the constraint qualification holds.     

Nonconvex energy functions,related eigenvalue hemivariational inequalities on the sphere and applications

The present paper deals with a new type of eigenvalue problems arising in problems involving nonconvex nonsmooth energy functions. They lead to the search of critical points (e.g. local minima) for nonconvex nonsmooth potential functions which in turn give rise to hemivariational inequalities. For this type of variational expressions the eigenvalue problem is studied here concerning the existence and multiplicity of solutions by applying a critical point theory appropriate for nonsmooth nonconvex functionals.     

Kuhn-Tucker sufficiency for global minimum of multi-extremal mathematical programming problems

The Kuhn-Tucker Sufficiency Theorem states that a feasible point that satisfies the Kuhn-Tucker conditions is a global minimizer for a convex programming problem for which a local minimizer is global. In this paper, we present new Kuhn-Tucker sufficiency conditions for possibly multi-extremal nonconvex mathematical programming problems which may have many local minimizers that are not global. We derive the sufficiency conditions by first constructing weighted sum of square underestimators of the objective function and then by characterizing the global optimality of the underestimators. As a consequence, we derive easily verifiable Kuhn-Tucker sufficient conditions for general quadratic programming problems with equality and inequality constraints. Numerical examples are given to illustrate the significance of our criteria for multi-extremal problems.     

On Efficiency Conditions for Nonsmooth Vector Equilibrium Problems with Equilibrium Constraints

In this article, necessary conditions of Fritz John type for weak efficient solutions of a nonsmooth vector equilibrium problem involving equilibrium constraints (VEPEC) in terms of the Clarke subdifferentials are established. Under constraint qualifications which are suitable for (VEPEC), necessary conditions of Kuhn-Tucker type for efficiency are derived. Under assumptions on generalized convexity of data, sufficient conditions for efficiency are developed. Some applications to vector variational inequalities and vector optimization problems with equilibrium constraints are also given.     

Necessary and sufficient conditions for Kuhn-Tucker type optimality and for weak duality of nonsmooth programming

In this paper, we are concerned with a nonsmooth programming problem with inequality constraints. We obtain an optimality condition for Kuhn-Tucker points to be minimizers. Later on, we present necessary and sufficient conditions for weak duality between the primal problem and its mixed type dual, which help us to extend some earlier work from the literature.     

Necessary Optimality Conditions in Terms of Convexificators in Lipschitz Optimization

This study is devoted to constraint qualifications and Kuhn-Tucker type necessary optimality conditions for nonsmooth optimization problems involving locally Lipschitz functions. The main tool of the study is the concept of convexificators. First, the case of a minimization problem in the presence of an arbitrary set constraint is considered by using the contingent cone and the adjacent cone to the constraint set. Then, in the case of a minimization problem with inequality constraints, Abadie type constraint qualifications and several other qualifications are proposed; Kuhn-Tucker type necessary optimality conditions are derived under the qualifications.Communicated by S. SchaibleThe authors thank the referees for bringing to their attention some papers closely related to this study and for helpful comments and constructive suggestions that have greatly improved the original version of the paper. Further, they are indebted to Professors H. W. Sun and F. Y. Lu, who suggested an example for this paper. The first author thanks S. Schaible for encouragement during this research.     

Extrapolated Smoothing Descent Algorithm for Constrained Nonconvex and Nonsmooth Composite Problems*

In this paper, the authors propose a novel smoothing descent type algorithm with extrapolation for solving a class of constrained nonsmooth and nonconvex problems,where the nonconvex term is possibly nonsmooth. Their algorithm adopts the proximal gradient algorithm with extrapolation and a safe-guarding policy to minimize the smoothed objective function for better practical and theoretical performance. Moreover, the algorithm uses a easily checking rule to update the smoothing parameter to ensure that any accumulation point of the generated sequence is an (affine-scaled) Clarke stationary point of the original nonsmooth and nonconvex problem. Their experimental results indicate the effectiveness of the proposed algorithm.