F─广义凸多目标规划的对偶理论

本文讨论Ｆ—广义凸多目标规划的对偶理论，证明了弱对偶、直接对偶和逆对偶定理．主要结果参考文献［１］的推广和发展。     

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.     

广义多品种最小费用流问题的对偶理论

基于广义多品种最小费用流问题的性质，将问题转化成一对含有内、外层问题的双水平规划，内层规划实际是单品种费用流问题，而外层问题是分离的凸规划，使用相关的凸分析理论，导出了广义多品种最小费用流问题的对偶规划，对偶定理和Kuhn-Tucker条件。     

多阶段有补偿问题的非线性模型

通过对已有补偿问题的模型进行总结，抽象与升华，本文建立了Ｂａｎａｃｈ空间中一般形式多阶段有补偿随机规划问题的一个非线性模型，使已有所有的补偿问题均成为其特例；然后利用可测集值映射理论，正规凸的被积函数的性质及文［８」中的结论等，讨论了所给模型的适定性与其基本性质．     

求解一类二阶段有补偿问题的对偶梯度法

利用对偶理论，本文给出了求解一类具有简单补偿的非线性二阶段问题的新对偶梯度法．在假设目标函数为可分连续可微凸函数的条件下，在每一选代步可将原二阶段有补偿问题转化为几个一维凸规划问题，大大简化了问题的求解．所给算法简单易行，文中还证明了该算法的全局收敛性．     

多目标分式规划的两种新对偶形式

§1.引言和引理 在[1]和[2]中,C.Singh和林锉云曾分别研究了多目标分式规划的对偶问题,本文则给出多目标分式规划的另外两种新的对偶形式。这两种对偶规划和R.Jagannathan以及C.Bector关于非线性规划的对偶理论有关。最后,我们还讨论了所研究的两种多目标分式对偶规划之间的相互关系。 考虑多目标分式规划     

均衡约束数学规划问题的一类广义Mond-Weir型对偶理论

针对均衡约束数学规划模型难以满足约束规范及难于求解的问题,基于Mond和Weir提出的标准非线性规划的对偶形式,利用其S稳定性,建立了均衡约束数学规划问题的一类广义Mond-Weir型对偶,从而为求解均衡约束优化问题提供了一种新的方法.在Hanson-Mond广义凸性条件下,利用次线性函数,分别提出了弱对偶性、强对偶性和严格逆对偶性定理,并给出了相应证明.该对偶化方法的推广为研究均衡约束数学规划问题的解提供了理论依据.     

一类多目标广义分式规划问题的最优性条件和对偶

研究了一类不可微多目标广义分式规划问题．首先,在广义Abadie约束品性条件下,给出了其真有效解的Kuhn—Tucker型必要条件．随后,在（C,a,P,d）一凸性假设下给出其真有效解的充分条件．最后,在此基础上建立了一种对偶模型,证明了对偶定理．得到的结果改进了相关文献中的相应结论．     

B-(p,r)-不变凸规划问题的Mond-Weir型对偶

利用一类新的广义凸函数:B- (p,r) -不变凸函数,建立了多目标规划问题的Mond- Weir型对偶,证明了弱对偶、强对偶和逆对偶定理.其结论具有一般性,推广了许多涉及不变凸函数、不变B-凸函数和(p,r) -不变凸函数的文献的结论.     

两个泛函优化定理的证明

在泛函优化理论中,Lagrange乘子定理、对偶定理占有重要地位.建立了带有等式和不等式约束的泛函优化问题,并给出了广义Lagrange乘子定理、广义Lagrange对偶定理的证明.     

L-shaped decomposition of two-stage stochastic programs with integer recourse

We consider two-stage stochastic programming problems with integer recourse. The L-shaped method of stochastic linear programming is generalized to these problems by using generalized Benders decomposition. Nonlinear feasibility and optimality cuts are determined via general duality theory and can be generated when the second stage problem is solved by standard techniques. Finite convergence of the method is established when Gomory’s fractional cutting plane algorithm or a branch-and-bound algorithm is applied.     

Measures as Lagrange multipliers in multistage stochastic programming

A duality theory is developed for multistage convex stochastic programming problems whose decision (or recourse) functions can be approximated by continuous functions satisfying the same constraints. Necessary and sufficient conditions for optimality are obtained in terms of the existence of multipliers in the class of regular Borel measures on the underlying probability space, these being decomposable, of course, into absolutely continuous and singular components with respect to the given probability measure. This provides an alternative to the approach where the multipliers are elements of the dual of $\text{L}$^∞ with an analogous decomposition. However, besides the existence of strictly feasible solutions, special regularity conditions are required, such as the “laminarity” of the probability measure, a property introduced in an earlier paper. These are crucial in ensuring that the minimum in the optimization problem can indeed be approached by continuous functions.     

Applications of stochastic programming: Achievements and questions

When solving a decision problem under uncertainty via stochastic programming it is essential to choose or to build a suitable stochastic programming model taking into account the nature of the real-life problem, character of input data, availability of software and computer technology. Besides a brief review of history and achievements of stochastic programming, selected modeling issues concerning applications of multistage stochastic programs with recourse (the choice of the horizon, stages, methods for generating scenario trees, etc.) will be discussed.     

Robust duality for generalized convex programming problems under data uncertainty

In this paper we present a robust duality theory for generalized convex programming problems in the face of data uncertainty within the framework of robust optimization. We establish robust strong duality for an uncertain nonlinear programming primal problem and its uncertain Lagrangian dual by showing strong duality between the deterministic counterparts: robust counterpart of the primal model and the optimistic counterpart of its dual problem. A robust strong duality theorem is given whenever the Lagrangian function is convex. We provide classes of uncertain non-convex programming problems for which robust strong duality holds under a constraint qualification. In particular, we show that robust strong duality is guaranteed for non-convex quadratic programming problems with a single quadratic constraint with the spectral norm uncertainty under a generalized Slater condition. Numerical examples are given to illustrate the nature of robust duality for uncertain nonlinear programming problems. We further show that robust duality continues to hold under a weakened convexity condition.     

On robust duality for fractional programming with uncertainty data

In this paper, we present a duality theory for fractional programming problems in the face of data uncertainty via robust optimization. By employing conjugate analysis, we establish robust strong duality for an uncertain fractional programming problem and its uncertain Wolfe dual programming problem by showing strong duality between the deterministic counterparts: robust counterpart of the primal model and the optimistic counterpart of its dual problem. We show that our results encompass as special cases some programming problems considered in the recent literature. Moreover, we also show that robust strong duality always holds for linear fractional programming problems under scenario data uncertainty or constraint-wise interval uncertainty, and that the optimistic counterpart of the dual is tractable computationally.     

Stochastic Lipschitz dynamic programming

Mathematical Programming - We propose a new algorithm for solving multistage stochastic mixed integer linear programming (MILP) problems with complete continuous recourse. In a similar way to...     

Duality for Set-Valued Multiobjective Optimization Problems,Part 1: Mathematical Programming

The duality of multiobjective problems is studied with the help of the apparatus of conjugate set-valued mappings introduced by the author. In this paper (Part 1), a duality theory is developed for set-valued mappings, which is then used to derive dual relations for some general multiobjective optimization problems which include convex programming and optimal control problems. Using this result, in the companion paper (Part 2), duality theorems are proved for multiobjective quasilinear and linear optimal control problems. The theory is applied to get dual relations for some multiobjective optimal control problem.     

双层线性规划的一个全局优化方法

用线性规划对偶理论分析了双层线性规划的最优解与下层问题的对偶问题可行域上极点之间的关系，通过求得下层问题的对偶问题可行域上的极点，将双层线性规划转化为有限个线性规划问题，从而用线性规划方法求得问题的全局最优解．由于下层对偶问题可行域上只有有限个极点，所以方法具有全局收敛性．