有限理性下变分不等式的逼近定理

该文基于Simon的有限理性理论,首先构造了有限理性下变分不等式问题的逼近定理,为有关变分不等式问题的不同算法提供了一个理论支持,充分体现了有限理性是对完全理性的逼近,是以完全理性为终极目标的.然后,利用集值分析的方法,将有限理性的逼近定理应用于变分不等式问题解的收敛性分析,在Baire分类的意义下,分别得到了函数扰动及函数和约束集同时扰动两种情况下单调变分不等式问题的解具有通有收敛性的结果.     

多目标最优化恰当有效解的稳定性

主要研究当两种类型的参数扰动时,多目标最优化问题中恰当有效解的稳定性.在点集映射的连续性意义下,分析讨论这种稳定性问题并分别给出引起扰动的两参数u,v所对应的点集映射Q1(u)和Q2(v),同时严格证明了在两个闭凸锥U,V上Q1(u)和Q2(v)的连续性定理.最后,通过附注对其进行补充和改进.     

关于非凸的有限理性的稳定性

关于有限理性方面的文献, 大多数都是在满足凸性条件下研究有限理性的相关性质, 在一定程度上限制了其应用范围. 应用Ekeland变分原理, 减弱了有限理性模型的假设条件, 考虑在不满足凸性条件下的有限理性模型的稳定性问题. 具体给出了非凸的Ky Fan点问题解的稳定性, 非凸非紧的Ky Fan点问题解的稳定性, 非凸向量值函数Ky Fan点解的稳定性和非凸非紧向量值函数Ky Fan点解的稳定性. 作为应用, 还给出了非凸的n人非合作博弈有限理性模型解的稳定性和非凸的多目标博弈有限理性模型解的稳定性.     

模糊映射不动点的存在性与稳定性

本文给出了模糊映射不动点的一个存在性定理,并应用有限理性研究的统一模式,研究了一类特殊的模糊不动点问题的稳定性,即在有限理性框架下证明了当模糊映射和可行集发生扰动时,在Baire分类意义下大多数模糊不动点问题都是稳定的.更进一步,在一定条件下给出了有限理性下模糊不动点问题的逼近定理,为关于模糊不动点问题的求解算法提供了理论支持.     

向量均衡问题强有效解的稳定性

在可行集扰动而向量值映射不扰动、可行集与向量值映射均扰动、可行集扰动而集值映射不扰动以及可行集与集值映射均扰动四种情况下,分别讨论了向量均衡问题强有效解的稳定性.     

一个有经济意义的多目标规划模型的调整

本文在线性约束条件下 ,同时考虑三个目标函数的最优化 ,即线性函数、二次函数、分式函数 .对于已知的线性规划的最优基可行解 ,通过调整二次函数和分式函数中的系数向量和系数矩阵 ,使其成为这两个规划的最优解 .模型的改进有经济意义的解释     

扰动多目标规划的次微分稳定性

本文利用共轭对偶算子定义了次微分,在一般拓扑向量空间中系统地讨论了多目标规划次微分稳定性.在目标函数为锥严格凸,约束函数为拟凸以及锥半连续的条件下,得到扰动多目标规划问题的整体稳定性.另外,通过引进点集,映射在一点凸的定义,得到问题的局部稳定性.我们将所得到的结论应用于有限维欧氏空间中控制结构为正锥的情形,还得到一些特殊结果.     

单机供应链排序及流水作业的反问题模型

最优化问题是在给定参数情况下,对某个目标函数,如费用、容量等,寻找问题的最优解.然而在许多现实生活中,有时只能知道问题的参数近似值和一个可行解,需要最小程度地调整参数,使得给定的可行解成为最优,这就是最优化问题的反问题.本文研究单台机器供应链排序和流水作业排序的反问题.根据调整参数的不同,本文利用排序理论把这些反问题表示为相应的数学规划形式.     

极大极小随机规划逼近问题最优解集和最优值的稳定性

研究了特殊的二层极大极小随机规划逼近收敛问题. 首先将下层初始随机规划最优解集拓展到非单点集情形, 且可行集正则的条件下, 讨论了下层随机规划逼近问题最优解集关于上层决策变量参数的上半收敛性和最优值函数的连续性. 然后把下层随机规划的epsilon-最优解向量函数反馈到上层随机规划的目标函数中, 得到了上层随机规划逼近问题的最优解集关于最小信息概率度量收敛的上半收敛性和最优值的连续性.     

大多数单调变分不等式具有唯一解

本文研究单调变分不等式解的唯一性.应用集值分析的方法,本文证明了,在Baire分类意义下,大多数单调半分不等式具有唯一解,并且每个具有多解的单调变分不等式可以由一列具有唯一解的单调变分不等式任意逼近.本文在两种不同的情形下进行了讨论,一种是只考虑率目标函数的扰动,另一种是不仅考虑目标函数的扰动也考虑约束集合的扰动.     

Analytic approximation and differentiability of joint chance constraints

ABSTRACT

An inner–outer approximation approach was recently developed to solve single chance constrained optimization (SCCOPT) problems. In this paper, we extend this approach to address joint chance constrained optimization (JCCOPT) problems. Using an inner–outer approximation, two smooth parametric optimization problems are defined whose feasible sets converge to the feasible set of JCCOPT from inside and outside, respectively. Any optimal solution of the inner approximation problem is a priori feasible to the JCCOPT. As the approximation parameter tends to zero, a subsequence of the solutions of the inner and outer problems, respectively, converge asymptotically to an optimal solution of the JCCOPT. As a main result, the continuous differentiability of the probability function of a joint chance constraint is obtained by examining the uniform convergence of the gradients of the parametric approximations.     

Saddle-Point Optimality: A Look Beyond Convexity

The fact that two disjoint convex sets can be separated by a plane has a tremendous impact on optimization theory and its applications. We begin the paper by illustrating this fact in convex and partly convex programming. Then we look beyond convexity and study general nonlinear programs with twice continuously differentiable functions. Using a parametric extension of the Liu-Floudas transformation, we show that every such program can be identified as a relatively simple structurally stable convex model. This means that one can study general nonlinear programs with twice continuously differentiable functions using only linear programming, convex programming, and the inter-relationship between the two. In particular, it follows that globally optimal solutions of such general programs are the limit points of optimal solutions of convex programs.     

On Intrinsic Complexity of Nash Equilibrium Problems and Bilevel Optimization

In this article we study generalized Nash equilibrium problems (GNEP) and bilevel optimization side by side. This perspective comes from the crucial fact that both problems heavily depend on parametric issues. Observing the intrinsic complexity of GNEP and bilevel optimization, we emphasize that it originates from unavoidable degeneracies occurring in parametric optimization. Under intrinsic complexity, we understand the involved geometrical complexity of Nash equilibria and bilevel feasible sets, such as the appearance of kinks and boundary points, non-closedness, discontinuity and bifurcation effects. The main goal is to illustrate the complexity of those problems originating from parametric optimization and singularity theory. By taking the study of singularities in parametric optimization into account, the structural analysis of Nash equilibria and bilevel feasible sets is performed. For GNEPs, the number of players’ common constraints becomes crucial. In fact, for GNEPs without common constraints and for classical NEPs we show that—generically—all Nash equilibria are jointly nondegenerate Karush–Kuhn–Tucker points. Consequently, they are isolated. However, in presence of common constraints Nash equilibria will constitute a higher dimensional set. In bilevel optimization, we describe the global structure of the bilevel feasible set in case of a one-dimensional leader’s variable. We point out that the typical discontinuities of the leader’s objective function will be caused by follower’s singularities. The latter phenomenon occurs independently of the viewpoint of the optimistic or pessimistic approach. In case of higher dimensions, optimistic and pessimistic approaches are discussed with respect to possible bifurcation of the follower’s solutions.     

Deterministic global optimization with partition sets whose feasibility is not known: Application to concave minimization,reverse convex constraints,DC-programming,and Lipschitzian optimization

A crucial problem for many global optimization methods is how to handle partition sets whose feasibility is not known. This problem is solved for broad classes of feasible sets including convex sets, sets defined by finitely many convex and reverse convex constraints, and sets defined by Lipschitzian inequalities. Moreover, a fairly general theory of bounding is presented and applied to concave objective functions, to functions representable as differences of two convex functions, and to Lipschitzian functions. The resulting algorithms allow one to solve any global optimization problem whose objective function is of one of these forms and whose feasible set belongs to one of the above classes. In this way, several new fields of optimization are opened to the application of global methods.     

A unifying theory of exactness of linear penalty functions II: parametric penalty functions

In this article, we develop a general theory of exact parametric penalty functions for constrained optimization problems. The main advantage of the method of parametric penalty functions is the fact that a parametric penalty function can be both smooth and exact unlike the standard (i.e. non-parametric) exact penalty functions that are always nonsmooth. We obtain several necessary and/or sufficient conditions for the exactness of parametric penalty functions, and for the zero duality gap property to hold true for these functions. We also prove some convergence results for the method of parametric penalty functions, and derive necessary and sufficient conditions for a parametric penalty function to not have any stationary points outside the set of feasible points of the constrained optimization problem under consideration. In the second part of the paper, we apply the general theory of exact parametric penalty functions to a class of parametric penalty functions introduced by Huyer and Neumaier, and to smoothing approximations of nonsmooth exact penalty functions. The general approach adopted in this article allowed us to unify and significantly sharpen many existing results on parametric penalty functions.     

Levitin–Polyak well-posedness for constrained quasiconvex vector optimization problems

In this paper, a notion of Levitin–Polyak (LP in short) well-posedness is introduced for a vector optimization problem in terms of minimizing sequences and efficient solutions. Sufficient conditions for the LP well-posedness are studied under the assumptions of compactness of the feasible set, closedness of the set of minimal solutions and continuity of the objective function. The continuity assumption is then weakened to cone lower semicontinuity for vector-valued functions. A notion of LP minimizing sequence of sets is studied to establish another set of sufficient conditions for the LP well-posedness of the vector problem. For a quasiconvex vector optimization problem, sufficient conditions are obtained by weakening the compactness of the feasible set to a certain level-boundedness condition. This in turn leads to the equivalence of LP well-posedness and compactness of the set of efficient solutions. Some characterizations of LP well-posedness are given in terms of the upper Hausdorff convergence of the sequence of sets of approximate efficient solutions and the upper semicontinuity of an approximate efficient map by assuming the compactness of the set of efficient solutions, even when the objective function is not necessarily quasiconvex. Finally, a characterization of LP well-posedness in terms of the closedness of the approximate efficient map is provided by assuming the compactness of the feasible set.     

On factoring parametric multivariate polynomials

This paper presents a new algorithm for the absolute factorization of parametric multivariate polynomials over the field of rational numbers. This algorithm decomposes the parameters space into a finite number of constructible sets. The absolutely irreducible factors of the input parametric polynomial are given uniformly in each constructible set. The algorithm is based on a parametric version of Hensel's lemma and an algorithm for quantifier elimination in the theory of algebraically closed field in order to reduce the problem of finding absolute irreducible factors to that of representing solutions of zero-dimensional parametric polynomial systems. The complexity of this algorithm is single exponential in the number n of the variables of the input polynomial, its degree d w.r.t. these variables and the number r of the parameters.     

A Generalized Time–Cost Trade-Off Transportation Problem

In the classical transportation problem if the unit costs and transportation durations are considered, the time-cost trade-off solutions can be determined by the well-known threshold approach assuming that all the transportations are permitted to be simultaneous in time. If all the unit costs are linear functions of time over a specified interval of time, a parametric technique can be applied for identifying all the time-cost trade-off solutions pertaining to this interval. In this paper, the unit costs considered are piecewise linear non-increasing functions of time and transportations are allowed to be simultaneous. It is shown that a parametric method involving a finite sequence of parametric transportation problems reveals all the time-cost trade-off solutions of this generalized trade-off problem. Computational experience is included. If the transportation problem has considerable degeneracy, the parametric approach may pose some computational difficulty. This difficulty can be reduced by using an alternative method involving the bicriteria optimization approach of Aneja and Nair. Also, a direct method is outlined for the case where a finite set of discrete alternatives of unit cost-time pairs is available.     

On second-order directional derivatives of value functions

Directional derivatives of value functions play an essential role in the sensitivity and stability analysis of parametric optimization problems, in studying bi-level and min–max problems, in quasi-differentiable calculus. Their calculation is studied in numerous works by A.V. Fiacco, V.F. Demyanov and A.M. Rubinov, R.T. Rockafellar, A. Shapiro, J.F. Bonnans, A.D. Ioffe, A. Auslender and R. Cominetti, and many other authors. This article is devoted to the existence of the second order directional derivatives of value functions in parametric problems with non-single-valued solutions. The main idea of the investigation approach is based on the development of the method of the first-order approximations by V.F. Demyanov and A.M. Rubinov.     

Stability in disjunctive linear optimization I: continuity of the feasible set

The aim of this paper is to study the continuous dependence of the feasible set of a disjunctive semi-infinite linear optimization problem on all involved parameters (matrix and right-hand side). The feasible set of such an optimization problem is the union of (a. possible infinite number of) convex sets, which each is described by a finite or an infinite number of strict and non-strict linear inequalities. We derive necessary and sufficient conditions for the upper- and lower-semi-continuity, and the closedness of the feasible-set-mapping Z Especially, the compactness of the boundary of the feasible set and the closedness of Z are equivalent to the upper-semi-continuity of Zwhile the lower semi-continuity of Z is equivalent to a certain constraint qualification. This constraint qualification is a strengthened kind of Slater condition, rrom tuese investigations, we derive known results in parametric semi-infinite optimization and parametric integer programming.