半无限规划与方向导数

本文对半无限凸规划提出一个用方向导数表述的对偶问题，其对偶间隙为零．     

半无限规划的对偶性

本文对半无限凸规划提出一个新的对偶问题,它由扰动函数及其次微分刻划.同时讨论了弱对偶性、强对偶性及逆对偶性,证明强对偶性等价于鞍点准则.     

半无限规划的鞍点与对偶性

该文给出半无限凸规划鞍点准则成立的一个简洁的充要条件，证明它等价于无对偶间隙，并给出用有限子规划表达的－充分条件．     

半无限规划的一个对偶问题

本文对半无限凸规划提出一个新的对偶问题，使用扰动函数、次微分和法锥，文中证明了相应的弱对偶性及强对偶性的充要条件．     

非光滑多目标半无限规划问题的混合型对偶

该文研究了非光滑多目标半无限规划问题的混合型对偶.首先,利用Lagrange函数介绍了非光滑多目标半无限规划混合型对偶的弱有效解和有效解的定义.其次,利用Dini?伪凸性建立了非光滑多目标半无限规划混合型对偶的弱对偶定理、强对偶定理和逆对偶定理.该文所得结果推广了已有文献中的主要结果.     

非紧致集上的最优值函数与广义半无限极大极小规划

研究非紧致集上的最优值函数, 给出了它的方向导数与次微分的结构表示式, 利用它们建立了广义半无限极大极小规划与其一阶最优性条件.     

半无限规划的一个非线性Lagrangian对偶模型

本文给出半无限规划的一个对偶罚函数模型,该模型能处理目标函数不是凸函数的情形,从而凸(SIP)对偶为该模型的一个特例.并且,作为罚函数,本模型的罚因子比l1-罚函数要小,这使得算法更可行,最后,给出零对偶间隙证明.     

非光滑半无限多目标规划近似解的对偶和鞍点定理

本文利用切向次微分研究了一类非光滑半无限多目标规划问题,并讨论了它的对偶定理和鞍点定理.首先,建立了半无限多目标规划问题的Mond-Weir型对偶,在广义凸性假设下,获得了半无限多目标规划问题近似解的弱对偶、强对偶和逆对偶定理.其次,定义了向量值拉格朗日函数的ε-拟鞍点,获得了ε-拟鞍点的必要和充分条件.这些结论推广和改进了文献中的相应结果.最后以具体的例子来说明了本文的结论.     

半无限规划的正则性

本文讨论了半无限凸规划的Lagrange正则性,即最优解适合鞍点准则的充要条件.文中进而研究厂正则点及约束函数的正则性.     

半无限极大极小问题的全局收敛方法

利用广义伪方向导数，在较弱的条件下，给出了半无限极大极小问题（P）的全局收敛性理论算法模型；利用离散策略给出了问题（P）全局收敛的可实现算法．数值结果表明本文给出的可实现算法是有效的．     

The Dual Gap Function for Variational Inequalities

In this paper we further study the dual gap function G, which was introduced by Marcotte and Zhu, for the variational inequality problem (VIP). We characterize the directional derivative and subdifferential of G. Based on these, we get a better understanding of the concepts of a global error bound, weak sharpness, and minimum principle sufficiency property for the pseudo-monotone VIP.     

Semismooth Newton Methods for Solving Semi-Infinite Programming Problems

In this paper we present some semismooth Newton methods for solving the semi-infinite programming problem. We first reformulate the equations and nonlinear complementarity conditions derived from the problem into a system of semismooth equations by using NCP functions. Under some conditions a solution of the system of semismooth equations is a solution of the problem. Then some semismooth Newton methods are proposed for solving this system of semismooth equations. These methods are globally and superlinearly convergent. Numerical results are also given.     

An Adaptive Dual Parametrization Algorithm for Quadratic Semi-infinite Programming Problems

The so called dual parametrization method for quadratic semi-infinite programming (SIP) problems is developed recently for quadratic SIP problems with a single infinite constraint. A dual parametrization algorithm is also proposed for numerical solution of such problems. In this paper, we consider quadratic SIP problems with positive definite objective and multiple linear infinite constraints. All the infinite constraints are supposed to be continuously dependent on their index variable on a compact set which is defined by a number equality and inequalities. We prove that in the multiple infinite constraint case, the minimu parametrization number, just as in the single infinite constraint case, is less or equal to the dimension of the SIP problem. Furthermore, we propose an adaptive dual parametrization algorithm with convergence result. Compared with the previous dual parametrization algorithm, the adaptive algorithm solves subproblems with much smaller number of constraints. The efficiency of the new algorithm is shown by solving a number of numerical examples.     

A Smoothing Newton Method for Semi-Infinite Programming

This paper is concerned with numerical methods for solving a semi-infinite programming problem. We reformulate the equations and nonlinear complementarity conditions of the first order optimality condition of the problem into a system of semismooth equations. By using a perturbed Fischer–Burmeister function, we develop a smoothing Newton method for solving this system of semismooth equations. An advantage of the proposed method is that at each iteration, only a system of linear equations is solved. We prove that under standard assumptions, the iterate sequence generated by the smoothing Newton method converges superlinearly/quadratically.     

A Dual Parametrization Method for Convex Semi-Infinite Programming

We formulate convex semi-infinite programming problems in a functional analytic setting and derive optimality conditions and several duality results, based on which we develop a computational framework for solving convex semi-infinite programs.     

Second-Order Parameter-Free Duality Models in Semi-Infinite Minmax Fractional Programming

In this article, we construct and discuss several second-order parameter-free duality models for a semi-infinite minmax fractional programming problem and establish appropriate duality results under various generalized second-order (?, β, φ, ρ, θ)-univexity assumptions.     

On Optimality Conditions for Generalized Semi-Infinite Programming Problems

Generalized semi-infinite optimization problems (GSIP) are considered. We generalize the well-known optimality conditions for minimizers of order one in standard semi-infinite programming to the GSIP case. We give necessary and sufficient conditions for local minimizers of order one without the assumption of local reduction. The necessary conditions are derived along the same lines as the first-order necessary conditions for GSIP in a recent paper of Jongen, Rückmann, and Stein (Ref. 1) by assuming the so-called extended Mangasarian–Fromovitz constraint qualification. Using the ideas of a recent paper of Rückmann and Shapiro, we give short proofs of necessary and sufficient optimality conditions for minimizers of order one under the additional assumption of the Mangasarian–Fromovitz constraint qualification at all local minimizers of the so-called lower-level problem.     

由方向导数表述的对偶问题

本文利用扰动函数的方向导数引进凸规划的一个新的对偶问题，并证明了相应的对偶性．     

Multidimensional Dual Dynamic Programming

In this paper, we develop a dual approach to the dynamic programming for the optimal control problem in a multidimensional case. The idea of our method consists in defining, instead of the value function, a new function which satisfies a dual first-order partial differential equation of dynamic programming. We then prove a suitable verification theorem and introduce the concept of a dual feedback control. The sufficient optimality conditions thus obtained are analogous to their one-dimensional counterparts.     

An Algorithm Constructing Solutions for a Family of Linear Semi-Infinite Problems

A family of linear semi-infinite problems depending on a parameter is considered. For fixed , a sensitivity analysis of the problem solution is carried out and rules constructing the solutions of this family for in a right-side neighborhood of ₀ are described. Results on the one-sided derivatives of the solution with respect to the parameter are presented. On the basis of the results, an active-set-strategy and a path-following algorithm are suggested.