具有广义B-凸函数的非光滑多目标规划的最优性与向量Lagrange鞍点理论

利用广义B-凸函数等概念，讨论了一类非光滑多目标规划，给出了广义最优性充分条件和Mond-Weir型对偶结果，讨论了向量Lagrange乘子性质并证明了向量值鞍点定理。     

Characterizing strict efficiency for convex multiobjective programming problems

The article pertains to characterize strict local efficient solution (s.l.e.s.) of higher order for the multiobjective programming problem (MOP) with inequality constraints. To create the necessary framework, we partition the index set of objectives of MOP to give rise to subproblems. The s.l.e.s. of order m for MOP is related to the local efficient solution of a subproblem. This relationship inspires us to adopt the D.C. optimization approach, the convex subdifferential sum rule, and the notion of ε-subdifferential to derive the necessary and sufficient optimality conditions for s.l.e.s. of order m \geqq 1{m \geqq 1} for the convex MOP. Further, the saddle point criteria of higher order are also presented.     

Conjugate maps and duality in multiobjective optimization

This paper considers duality in convex vector optimization. A vector optimization problem requires one to find all the efficient points of the attainable value set for given multiple objective functions. Embedding the primal problem into a family of perturbed problems enables one to define a dual problem in terms of the conjugate map of the perturbed objective function. Every solution of the stable primal problem is associated with a certain solution of the dual problem, which is characterized as a subgradient of the perturbed efficient value map. This pair of solutions also provides a saddle point of the Lagrangian map.     

Super efficiency in vector optimization with nearly convexlike set-valued maps

In this paper, we establish a scalarization theorem and a Lagrange multiplier theorem for super efficiency in vector optimization problem involving nearly convexlike set-valued maps. A dual is proposed and duality results are obtained in terms of super efficient solutions. A new type of saddle point, called super saddle point, of an appropriate set-valued Lagrangian map is introduced and is used to characterize super efficiency.     

Optimality and mixed saddle point criteria in multiobjective optimization

In this paper, higher order strong convexity for Lipschitz functions is introduced and is utilized to derive the optimality conditions for the new concept of strict minimizer of higher order for a multiobjective optimization problem. Variational inequality problem is introduced and its solutions are related to the strict minimizers of higher order for a multiobjective optimization problem. The notion of vector valued partial Lagrangian is also introduced and equivalence of the mixed saddle points of higher order and higher order minima are provided.     

向量最优化问题的Lagrange对偶与择一定理

本文讨论无限维向量最优化问题的Lagrange对偶与弱对偶，建立了若干鞍点定理与弱鞍点定理．作为研究对偶问题的工具，建立了一个新的择一定理．     

集值映射向量优化问题的ε-超鞍点和ε-对偶定理

本文研究集值映射向量优化问题的ε-超鞍点和ε-对偶定理。在集值映射是近似广义锥次似凸的假设下，利用ε-超有效解的标量化和Lagrange乘子定理，建立和证明了关于ε-超有效解的鞍点和对偶定理。     

Saddle points of general augmented Lagrangians for constrained nonconvex optimization

Local and global saddle point conditions for a general augmented Lagrangian function proposed by Mangasarian are investigated in the paper for inequality and equality constrained nonconvex optimization problems. Under second order sufficiency conditions, it is proved that the augmented Lagrangian admits a local saddle point, but without requiring the strict complementarity condition. The existence of a global saddle point is then obtained under additional assumptions that do not require the compactness of the feasible set and the uniqueness of global solution of the original problem.     

Saddle point and generalized convex duality for multiobjective programming

In this paper we consider the dual problems for multiobjective programming with generalized convex functions. We obtain the weak duality and the strong duality. At last, we give an equivalent relationship between saddle point and efficient solution in multiobjective programming.     

A duality approach to minimax results for quasi-saddle functions in finite dimensions

In this paper we show how saddle point theorems for a quasiconvex—quasiconcave function can be derived from duality theory. A symmetric duality framework that provides the machinery for deriving saddle point theorems is presented. Generating the theorems,via the framework, provides a deeper understanding of assumptions employed in existing theorems which do not utilize duality theory.     

有效解适合鞍点准则的条件

建立了单目标优化问题的用次微分表达的对偶问题,并用其对偶性给出多目标优化问题的有效解适合鞍点准则的条件.     

Higher-order optimality conditions for strict local minima

In this work, we study a nonsmooth optimization problem with generalized inequality constraints and an arbitrary set constraint. We present necessary conditions for a point to be a strict local minimizer of order k in terms of higher-order (upper and lower) Studniarski derivatives and the contingent cone to the constraint set. In the same line, when the initial space is finite dimensional, we develop sufficient optimality conditions. We also provide sufficient conditions for minimizers of order k using the lower Studniarski derivative of the Lagrangian function. Particular interest is put for minimizers of order two, using now a special second order derivative which leads to the Fréchet derivative in the differentiable case.     

非光滑多目标广义本性凸规划的最优性条件与对偶

In this paper, we introduce generalized essentially pseudoconvex function and generalized essentially quasiconvex function, and give sufficient optimality conditions of the nonsmooth generalized convex multi-objective programming and its saddle point theorem about cone efficient solution. We set up Mond-Weir type duality and Craven type duality for nonsmooth multiobjective programming with generalized essentially convex functions, and prove them.     

Saddle points for vector valued functions: existence, necessary and sufficient theorems

Necessary and sufficient conditions for a point to be a weak saddle point of a vector valued function (i.e. to be a solution of the vector saddle point problem) are given. Also, an existence result for a vector saddle point to have a solution is given.     

A Note on First-Order Sufficient Optimality Conditions for Pareto Problems

We take into consideration the first-order sufficient conditions, established by Jiménez and Novo (Numer. Funct. Anal. Optim. 2002; 23:303–322) for strict local Pareto minima. We give here a more operative condition for a strict local Pareto minimum of order 1.     

First and second order sufficient conditions for strict minimality in nonsmooth vector optimization

In this paper we present first and second order sufficient conditions for strict local minima of orders 1 and 2 to vector optimization problems with an arbitrary feasible set and a twice directionally differentiable objective function. With this aim, the notion of support function to a vector problem is introduced, in such a way that the scalar case and the multiobjective case, in particular, are contained. The obtained results extend the multiobjective ones to this case. Moreover, specializing to a feasible set defined by equality, inequality, and set constraints, first and second order sufficient conditions by means of Lagrange multiplier rules are established.     

集值优化问题ε-严有效性的最优性条件

本文是文[1]工作的继续,对ε-严有效性开展进一步的研究.对于集值优化问题(SVP),在有关映射为锥-类凸的假设条件下,得到了ε-(真)严有效点(解)的ε-Lagrange乘子、ε-真严鞍点和ε-Lagrange型对偶等结果.     

序线性空间中向量优化问题的K-T型定理

本文在广义凸性条件下,研究实线性空间中一类向量优化问题的最优性条件,我们引入F ritz-John鞍点,、“K-T鞍点”讨论它们与有效解、弱有效解之间的关系.     

线性空间中向量极值问题的鞍点

首先在序线性空间中引入广义次似凸映射 ,建立其择一定理 .然后 ,在这种空间中定义向量 Fritz-John鞍点和向量 Kuhn- Tucker鞍点 ,我们讨论了其二者之间以及向量极值问题的弱有效解与他们的关系 .     

Instability analysis of saddle points by a local minimax method

The objective of this work is to develop some tools for local instability analysis of multiple critical points, which can be computationally carried out. The Morse index can be used to measure local instability of a nondegenerate saddle point. However, it is very expensive to compute numerically and is ineffective for degenerate critical points. A local (weak) linking index can also be defined to measure local instability of a (degenerate) saddle point. But it is still too difficult to compute. In this paper, a local instability index, called a local minimax index, is defined by using a local minimax method. This new instability index is known beforehand and can help in finding a saddle point numerically. Relations between the local minimax index and other local instability indices are established. Those relations also provide ways to numerically compute the Morse, local linking indices. In particular, the local minimax index can be used to define a local instability index of a saddle point relative to a reference (trivial) critical point even in a Banach space while others failed to do so.