带消失约束的区间值优化问题的最优性条件与对偶定理

本文考虑一类带消失约束的非光滑区间值优化问题(IOPVC)。在一定的约束条件下得到了问题(IOPVC)的LU最优解的必要和充分性最优性条件,研究了其与Mond-Weir型对偶模型和Wolfe型对偶模型之间的弱对偶,强对偶和严格逆对偶定理,并给出了一些例子来阐述我们的结果。     

关于具有不确定性凸半无限规划的近似解（英文）

在本文中,我们考虑约束函数带有不确定信息的凸半无限优化问题的近似解(也称为ε-解),并建立了凸半无限规划的鲁棒对等问题,同时给出了其近似解.进一步地,提出了鲁棒对偶问题的必要条件和充分条件.在锥约束条件下,基于鲁棒优化方法,证明了近似解意义下的拉格朗日对偶性质.     

自身对偶数学规划问题的推广

一个数学规划问题称为是自身对偶的,如果它可以从它的对偶问题中增加或减去某些约束条件而得到,而且它和它的对偶问题有相同的最优解和相同的最优值.凡是自身对偶的数学规划问题都有这样一些重要性质:它的最优值等于零,它的最优解在约束集合的边界上,等等。因此,自身对偶是一类非常重要的对偶模型,它在数学规划的对偶理论中,占有极其重要的地位。文章[1,2]分别讨论了自身对偶的线性规划问题和二次规划问题。文章[3]推广了文章[1]和[2]的结果,建立了如下一类自身对偶的凸规划问题     

一类非单调互补问题的Canonical对偶问题研究

对于一类具有广泛应用背景的非单调互补问题,我们构建了这类问题的Canonical对偶问题。其对偶问题可以写成和原问题类似的互补问题。我们给出了对偶问题和原问题解之间的对偶关系,并且将对偶问题转化成一个一维优化问题,这不但可以方便的求解这类问题,也为研究这类问题性质提供了一个非常直观的研究工具。最后,本文给出了几个算例来演示对偶问题的性质。     

半凸多目标优化ε-拟弱有效解的最优性条件及对偶定理

本文在相关文献考虑MP问题的基础上,增加了等式约束条件,即本文考虑了VP问题,并将已有文献中的凸性假设改为半凸性假设,得到VP问题的ε-拟弱有效解的相应最优性条件.接着,本文定义了VP问题的拉格朗日函数及其ε-拟弱鞍点,得到VP问题的ε-拟弱鞍点相应定理.最后,本文考虑了VP问题的对偶问题,获得了VP问题的弱对偶和强对偶定理.     

广义次似凸集值优化的对偶定理

我们讨论了广义次似凸集值优化的对偶定理.首先,我们给出了广义次似凸集值优化的对偶问题.其次,我们给出了广义次似凸集值优化的对偶定理.最后,我们考虑了广义次似凸集值优化问题的标量化对偶,并给出了一系列对偶定理.     

线性规划可行解的一个性质

通过对线性规划问题可行解的性质的推广,导出推广后的可行解与对应的对偶线性规划的约束条件之间互为充分必要的关系。     

一类对偶平坦的球对称的芬斯勒度量

本文研究了对偶平坦的芬斯勒度量的构造问题.通过分析球对称的对偶平坦的芬斯勒度量的方程的解,我们构造了一类新的对偶平坦的芬斯勒度量,并得到了球对称的芬斯勒度量成为对偶平坦的充分必要条件.     

一类对偶平坦的球对称的芬斯勒度量（英文）

本文研究了对偶平坦的芬斯勒度量的构造问题.通过分析球对称的对偶平坦的芬斯勒度量的方程的解,我们构造了一类新的对偶平坦的芬斯勒度量,并得到了球对称的芬斯勒度量成为对偶平坦的充分必要条件.     

基于对偶犹豫模糊偏好信息的双边稳定匹配决策方法

针对基于对偶犹豫模糊偏好信息的双边稳定匹配问题,提出了一种新的匹配方法.首先,给出了基于对偶犹豫模糊偏好信息的双边稳定匹配问题的描述;然后,依据双边主体给出的偏好信息构造对偶犹豫模糊偏好矩阵,使用投影技术将对偶犹豫模糊偏好矩阵转化为满意度矩阵;接着,以双方主体满意度最大化为目标,考虑稳定匹配的约束条件,构建了匹配模型;进而,运用组合满意度分析方法,将多目标优化模型转化为单目标优化模型,通过模型求解得到最优的匹配方案;最后,实例分析说明了所提方法的实用性和有效性.     

On zero duality gap in nonconvex quadratic programming problems

We present in this paper new sufficient conditions for verifying zero duality gap in nonconvex quadratically/linearly constrained quadratic programs (QP). Based on saddle point condition and conic duality theorem, we first derive a sufficient condition for the zero duality gap between a quadratically constrained QP and its Lagrangian dual or SDP relaxation. We then use a distance measure to characterize the duality gap for nonconvex QP with linear constraints. We show that this distance can be computed via cell enumeration technique in discrete geometry. Finally, we revisit two sufficient optimality conditions in the literature for two classes of nonconvex QPs and show that these conditions actually imply zero duality gap.     

A generalized duality and applications

The aim of this paper is to present a nonconvex duality with a zero gap and its connection with convex duality. Since a convex program can be regarded as a particular case of convex maximization over a convex set, a nonconvex duality can be regarded as a generalization of convex duality. The generalized duality can be obtained on the basis of convex duality and minimax theorems. The duality with a zero gap can be extended to a more general nonconvex problems such as a quasiconvex maximization over a general nonconvex set or a general minimization over the complement of a convex set. Several applications are given.On leave from the Institute of Mathematics, Hanoi, Vietnam.     

Solutions and optimality criteria for nonconvex constrained global optimization problems with connections between canonical and Lagrangian duality

This paper presents a canonical duality theory for solving a general nonconvex quadratic minimization problem with nonconvex constraints. By using the canonical dual transformation developed by the first author, the nonconvex primal problem can be converted into a canonical dual problem with zero duality gap. A general analytical solution form is obtained. Both global and local extrema of the nonconvex problem can be identified by the triality theory associated with the canonical duality theory. Illustrative applications to quadratic minimization with multiple quadratic constraints, box/integer constraints, and general nonconvex polynomial constraints are discussed, along with insightful connections to classical Lagrangian duality. Criteria for the existence and uniqueness of optimal solutions are presented. Several numerical examples are provided.     

A geometric study of duality gaps, with applications

Lagrangian relaxation is often an efficient tool to solve (large-scale) optimization problems, even nonconvex. However it introduces a duality gap, which should be small for the method to be really efficient. Here we make a geometric study of the duality gap. Given a nonconvex problem, we formulate in a first part a convex problem having the same dual. This formulation involves a convexification in the product of the three spaces containing respectively the variables, the objective and the constraints. We apply our results to several relaxation schemes, especially one called “Lagrangean decomposition” in the combinatorial-optimization community, or “operator splitting” elsewhere. We also study a specific application, highly nonlinear: the unit-commitment problem. Received: June 1997 / Accepted: December 2000?Published online April 12, 2001     

DC approach to weakly convex optimization and nonconvex quadratic optimization problems

Motivated by weakly convex optimization and quadratic optimization problems, we first show that there is no duality gap between a difference of convex (DC) program over DC constraints and its associated dual problem. We then provide certificates of global optimality for a class of nonconvex optimization problems. As an application, we derive characterizations of robust solutions for uncertain general nonconvex quadratic optimization problems over nonconvex quadratic constraints.     

Optimal Potentials for Problems with Changing Sign Data

The main purpose of this paper is to study the duality and penalty method for a constrained nonconvex vector optimization problem. Following along with the image space analysis, a Lagrange-type duality for a constrained nonconvex vector optimization problem is proposed by virtue of the class of vector-valued regular weak separation functions in the image space. Simultaneously, some equivalent characterizations to the zero duality gap property are established including the Lagrange multiplier, the Lagrange saddle point and the regular separation. Moreover, an exact penalization is also obtained by means of a local image regularity condition and a class of particular regular weak separation functions in the image space.     

Convexifiability of continuous and discrete nonnegative quadratic programs for gap-free duality

In this paper we show that a convexifiability property of nonconvex quadratic programs with nonnegative variables and quadratic constraints guarantees zero duality gap between the quadratic programs and their semi-Lagrangian duals. More importantly, we establish that this convexifiability is hidden in classes of nonnegative homogeneous quadratic programs and discrete quadratic programs, such as mixed integer quadratic programs, revealing zero duality gaps. As an application, we prove that robust counterparts of uncertain mixed integer quadratic programs with objective data uncertainty enjoy zero duality gaps under suitable conditions. Various sufficient conditions for convexifiability are also given.     

Solutions and optimality criteria for nonconvex quadratic-exponential minimization problem

This paper presents a set of complete solutions and optimality conditions for a nonconvex quadratic-exponential optimization problem. By using the canonical duality theory developed by the first author, the nonconvex primal problem in n-dimensional space can be converted into an one-dimensional canonical dual problem with zero duality gap, which can be solved easily to obtain all dual solutions. Each dual solution leads to a primal solution. Both global and local extremality conditions of these primal solutions can be identified by the triality theory associated with the canonical duality theory. Several examples are illustrated.     

A note on the duality gap in nonconvex optimization and a very simple procedure for bid evaluation type problems

First a priori estimates of the duality gap in nonconvex programming are given by very easy arguments on well-known results in duality theory. Then a very simple procedure for solving bid evaluation type problems is deduced.     

A class of problems where dual bounds beat underestimation bounds

We investigate the problem of minimizing a nonconvex function with respect to convex constraints, and we study different techniques to compute a lower bound on the optimal value: The method of using convex envelope functions on one hand, and the method of exploiting nonconvex duality on the other hand. We investigate which technique gives the better bound and develop conditions under which the dual bound is strictly better than the convex envelope bound. As a byproduct, we derive some interesting results on nonconvex duality.