ρ─弧式凸函数下（VP）和（VD）对偶定理

作者在文献［１］中定义了一类广义凸函数：ρ－弧式凸性函数，并讨论了其基本性质。在此基础上，本文在ρ－弧式凸函数条件下，论证了多目标规划（ＶＰ）和对偶规划（ＣＤ）的三个对偶定理．     

分次Morita对偶，Morita对偶与Smash积

设Ｃ和ｒ都是群，是Ｇ－型分次环，是Γ－型分次环．是双分次模，Ｒ＃Ｇ是Ｒ的Ｓｍａｓｈ积，Ａ＃Γ是Ａ的Ｓｍａｓｈ积。令Ｗ＝（＿ｇＵ＿（σ－１））＿（ｇ，σ）即（ｇ，σ）位置取＿ｇＵ＿（σ－１）的元素的｜Ｇ｜×｜Γ｜矩阵的全体组成的集合，且每个矩阵的每行和每列的非零元只有有限个，按矩阵运算，Ｗ构成（Ｒ＃６，Ａ＃Γ）双模。则＿ＲＵ＿Ａ定义了一个分次Ｍｏｒｉｔａ对偶当且仅当＿（Ｒ＃Ｇ）Ｗ＿（Ａ＃Γ）定义了一个Ｍｏｒｉｔａ对偶。     

算子代数的C_σ性质、C_σ性质与自反性

本文首次引入算子集合的Ｃ＿σ性质和性质的概念，它们与性质Ｃ和性质Ｄ＿σ（１）有一定的关系．两个主要结果是：（１）具有性质Ｃ＿σ的对偶代数一定是遗传自反的．（２）如果对偶代数所生成的ｎ－自反代数具有性质Ｃ＿σ，则该对偶代数一定是遗传ｎ－自反的．     

Banach空间中向量优化问题的对称对偶与自身对偶

Ｂａｎａｃｈ空间中向量优化问题的对称对偶与自身对偶董加礼，陈东彦，王连成（吉林工业大学应用数学系，长春，１３００２５）１．引言对称对偶性与自身对偶性是６０年代初关于二次规划研究中提出来的，并且很快被推广到一般的线性规划中，尤其对非线性凸规划问题，这两...     

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．     

基于两点累积信息原／倒变量展开的对偶优化方法的收敛性分析

基于两点累积信息原／倒变量展开的对偶优化方法的收敛性分析邢誉峰（北京航空航天大学固体力学研究所）钱令希（大连理工大学工程力学所）ＡＭＡＴＨＥＭＡＴＩＣＡＬＣＯＮＶＥＲＧＥＮＣＥＡＮＡＬＹＳＩＳＯＦＴＨＥＤＵＡＬＭＥＴＨＯＤＢＹＥＸＰＡＮＳＩＯＮＯＦＯ...     

偏序线性空间中向量集值映射最优化问题解的鞍点条件和Lagrange对偶

本文在没有任何拓扑结构的条件下，给出了向量集值映射最优化问题解的鞍点充分和必要条件以及Ｌａｇｒａｎｇｅ对偶，从而将文献（１）中的有关结果推广到更一般的偏序线性空间，并进一步给出了逆对偶定理。     

非凸规划的对偶问题极值

本文对非凸规划的对偶问题的目标函数极值给出一个表达式 ,从而得出对偶间隙 ,使用的方法是扰动函数的凸色 ,而不使用任何有关凸性的假定     

概率约束问题的对偶切平面算法

概率约束问题的对偶切平面算法唐恒永（沈阳师范学院数学计算机系，沈阳１１００３１）一、引言随机规划中的概率约束问题能应用于很多工程技术和经济问题。由于该问题具有难以处理的非线性的概率约束，所以求解起来比较困难。［１］给出了求解这类问题的一个综述，比较容...     

Comment on “A nonlinear Lagrangian dual for integer programming”

We present a counterexample and correction to the contention by Xu and Li that the nonlinear Lagrangian dual problem they propose [Oper. Res. Lett. 30 (2002) 401] asymptotically has no duality gap.     

An exact algorithm for the vehicle routing problem based on the set partitioning formulation with additional cuts

This paper presents a new exact algorithm for the Capacitated Vehicle Routing Problem (CVRP) based on the set partitioning formulation with additional cuts that correspond to capacity and clique inequalities. The exact algorithm uses a bounding procedure that finds a near optimal dual solution of the LP-relaxation of the resulting mathematical formulation by combining three dual ascent heuristics. The first dual heuristic is based on the q-route relaxation of the set partitioning formulation of the CVRP. The second one combines Lagrangean relaxation, pricing and cut generation. The third attempts to close the duality gap left by the first two procedures using a classical pricing and cut generation technique. The final dual solution is used to generate a reduced problem containing only the routes whose reduced costs are smaller than the gap between an upper bound and the lower bound achieved. The resulting problem is solved by an integer programming solver. Computational results over the main instances from the literature show the effectiveness of the proposed algorithm.      

On duality gap in linear conic problems

In their paper “Duality of linear conic problems” Shapiro and Nemirovski considered two possible properties (A) and (B) for dual linear conic problems (P) and (D). The property (A) is “If either (P) or (D) is feasible, then there is no duality gap between (P) and (D)”, while property (B) is “If both (P) and (D) are feasible, then there is no duality gap between (P) and (D) and the optimal values val(P) and val(D) are finite”. They showed that (A) holds if and only if the cone K is polyhedral, and gave some partial results related to (B). Later Shapiro conjectured that (B) holds if and only if all the nontrivial faces of the cone K are polyhedral. In this note we mainly prove that both the “if” and “only if” parts of this conjecture are not true by providing examples of closed convex cone in \mathbbR⁴{\mathbb{R}^{4}} for which the corresponding implications are not valid. Moreover, we give alternative proofs for the results related to (B) established by Shapiro and Nemirovski.     

Duality for nondifferentiable minimax programming in complex spaces

Employing the optimality (necessary and sufficient) conditions of a nondifferentiable minimax programming problem in complex spaces, we formulate a one-parametric dual and a parameter free dual problems. On both dual problems, we establish three duality theorems: weak, strong, and strict converse duality theorem, and prove that there is no duality gap between the two dual problems with respect to the primal problem under some generalized convexities of complex functions in the complex programming problem.     

Optimal dividend payments under a time of ruin constraint: Exponential claims

We consider the optimal dividends problem under the Cramér–Lundberg model with exponential claim sizes subject to a constraint on the expected time of ruin. We introduce the dual problem and show that the complementary slackness conditions are satisfied, thus there is no duality gap. Therefore the optimal value function can be obtained as the point-wise infimum of auxiliary value functions indexed by Lagrange multipliers. We also present a series of numerical examples.     

Duality for nondifferentiable minimax programming in complex spaces

Employing the optimality (necessary and sufficient) conditions of a nondifferentiable minimax programming problem in complex spaces, we formulate a one-parametric dual and a parameter free dual problems. On both dual problems, we establish three duality theorems: weak, strong, and strict converse duality theorem, and prove that there is no duality gap between the two dual problems with respect to the primal problem under some generalized convexities of complex functions in the complex programming problem.     

Duality and solutions for quadratic programming over single non-homogeneous quadratic constraint

This paper extends and completes the discussion by Xing et?al. (Canonical dual solutions to the quadratic programming over a quadratic constraint, submitted) about the quadratic programming over one quadratic constraint (QP1QC). In particular, we relax the assumption to cover more general cases when the two matrices from the objective and the constraint functions can be simultaneously diagonalizable via congruence. Under such an assumption, the nonconvex (QP1QC) problem can be solved through a dual approach with no duality gap. This is unusual for general nonconvex programming but we can explain by showing that (QP1QC) is indeed equivalent to a linearly constrained convex problem, which happens to be dual of the dual of itself. Another type of hidden convexity can be also found in the boundarification technique developed in Xing et?al. (Canonical dual solutions to the quadratic programming over a quadratic constraint, submitted).     

THE DUAL MIXED METHOD FOR AN UNILATERAL PROBLEM

In this paper, the dual mixed method for an unilateral problem, which is the simplified modelling of scalar function for the friction-free contact problem, is considered. The dual mixed problem is introduced, the existence and uniqeness of the solution of the problem are presented, and error bounds O(h^3/4 ) and O(h^3/2 ) are obtained for the dual mixed finite element approximations of Raviart-Thomas elements for k= 0 and k= 1 respectively.     

A note on duality gaps in linear programming over convex sets

For certain types of mathematical programming problems, a related dual problem can be constructed in which the objective value of the dual problem is equal to the objective function of the given problem. If these two problems do not have equal values, a duality gap is said to exist. No such gap exists for pairs of ordinary dual linear programming problems, but this is not the case for linear programming problems in which the nonnegativity conditionx ? 0 is replaced by the condition thatx lies in a certain convex setK. Duffin (Ref. 1) has shown that, whenK is a cone and a certain interiority condition is fulfilled, there will be no duality gap. In this note, we show that no duality gap exists when the interiority condition is satisfied andK is an arbitrary closed convex set inR ⁿ .     

Subdifferentiability and the Duality Gap

We point out a connection between sensitivity analysis and the fundamental theorem of linear programming by characterizing when a linear programming problem has no duality gap. The main result is that the value function is subdifferentiable at the primal constraint if and only if there exists an optimal dual solution and there is no duality gap. To illustrate the subtlety of the condition, we extend Kretschmer's gap example to construct (as the value function of a linear programming problem) a convex function which is subdifferentiable at a point but is not continuous there. We also apply the theorem to the continuum version of the assignment model.