单峰映射允许揉搓序列的维数和测度

本文证明了单峰映射的允许揉搓序列组成的集合在符号空间∑2中的Hausdorff维数为1,1维Hausdorff测度为零.     

单峰映射允许揉搓序列的维数和测度

本文证明了单峰映射的允许揉搓序列组成的集合在符号空间∑_2中的 Hausdorff 维数为1,1维 Hausdorff 测度为零。     

拟线性退缩抛物方程

如果(2)中的二次型恒为正定,则(1)是抛物型方程.但这里只要求它非负,即允许方程(1)在(t,x,u)的一个集合上退缩,甚至允许它完全退缩而成为一阶方程.因此,这类方程超出了古典理论的范围. 在这类方程中,最典型的和研究得最多的是非线性扩散方程     

欧氏空间中加细采样的小波标架不变性

设A、B分别为Z~(δ×δ)允许矩阵和加细允许矩阵,Ψ(?)L~2(R~8)为有限个函数的集合。如果Ψ可以生成L~2(R~8)的小波标架则也为L~2(R~8)的小波标架,且标架界分别为原来的|detB|倍,在实际计算中具有更小的误差。     

关于(f,g)-因子定理的注记

关于（ｆ，ｇ）－因子定理的注记蔡茂诚（中国科学院系统科学研究所，北京１０００８０）国家自然科学基金资助项目．１９９３年２月５日收到．我们只考虑有限的一般图，允许多重边和自环．一般图记作Ｇ＝（Ｖ，Ｅ），其中Ｖ（Ｇ）是点集合，而Ｅ（Ｇ）是边集合．对于任意...     

基于模糊软矩阵的企业信息化水平的综合评价

利用加权模糊软集评价方法对企业信息化水平进行综合评价.评价过程中,考虑到专家知识的局限性,允许不同专家采用不同个人评价指标集,应用模糊软集合方法对不同专家的评价信息进行融合,得到最终的评价结果,并通过实例说明该评价方法的应用.     

集合概念的扩张与完善

从集合概念的内涵出发,剖析了单值集——Cantor集合、Fuzzy集合、Rough集合、可拓集合概念的内涵;剖析了复值集——Grey集合、未确知集合、Vague集合、泛灰(UG)集合和广义泛灰(GUG)集合的内涵;从而得出广义泛灰集合是内涵最深的集合概念;它具有极强的描述能力,可以描述客观存在的一切现象,故又是外延最广的集合概念.     

广义集合概念及其内涵剖析

在剖析C an tor集合、Fuzzy集合、R ough集合、Ex tension集合、G rey集合;U nascerta ined集合、U n iversal G rey集合、V ague集合概念内涵的基础上,建立了广义集合(G eneralized sets)概念,并在对其内涵进行剖析的基础上讨论了它的基本性质。     

新集合概念的应用与学生创新思维的培养

首先阐述了模糊集合、粗集合与P-集合(P=Packet)的概念、结构、特征及其图形的直观表示,此三种集合在本文中将简称"新集合".然后介绍了新集合与有限普通元素集合的关系,介绍了新集合的简单应用.教学实践表明,将这些新集合概念渗透到基础数学教学中,对培养学生的创新思维能力与研究开发能力具有十分有益的促进作用.     

随机容错设施布局问题的近似算法

在确定性的容错设施布局问题中, 给定顾客的集合和地址的集合. 在每个地址上可以开设任意数目的不同设施. 每个顾客j有连接需求r_j. 允许将顾客j连到同一地址的不同设施上. 目标是开设一些设施并将每个顾客j连到r_j个不同的设施上, 使得总开设费用和连接费用最小. 研究两阶段随机容错设施布局问题(SFTFP), 顾客的集合事先不知道, 但是具有有限多个场景并知道其概率分布. 每个场景指定需要服务的顾客的子集. 并且每个设施有两种类型的开设费用. 在第一阶段根据顾客的随机信息确定性地开设一些设施, 在第二阶段根据顾客的真实信息再增加开设一些设施．给出随机容错布局问题的线性整数规划和基于线性规划舍入的5-近似算法.     

求解多层线性规划的模糊规划法

本文用模糊集理论中的隶属函数描述多层线性规划的各层目标,在第一层给定最小满意水平下,通过求解相应层次的模糊规划来确定各层的最小满意度,从而最终得到问题的一个满意解。提出的方法只需求解一系列线性规划问题,具有较好的计算复杂性和可行性,最后的算例进一步验证了方法的有效性。     

非线性参数规划问题$\varepsilon$-最优解集集值映射的连续性

对非线性参数规划问题$\varepsilon$-最优解集集值映射的连续性条件进行了研究.首先在可行集集值映射局部有界且正则的条件下,讨论了非线性参数规划问题最优值函数的连续性,然后针对$\varepsilon$-最优解集集值映射的结构特征并利用此结果和集值分析理论,给出了非线性参数规划问题$\varepsilon$-最优解集集值映射连续的一个充分条件.     

Canonical Duality Theory and Solutions to Constrained Nonconvex Quadratic Programming

This paper presents a perfect duality theory and a complete set of solutions to nonconvex quadratic programming problems subjected to inequality constraints. By use of the canonical dual transformation developed recently, a canonical dual problem is formulated, which is perfectly dual to the primal problem in the sense that they have the same set of KKT points. It is proved that the KKT points depend on the index of the Hessian matrix of the total cost function. The global and local extrema of the nonconvex quadratic function can be identified by the triality theory [11]. Results show that if the global extrema of the nonconvex quadratic function are located on the boundary of the primal feasible space, the dual solutions should be interior points of the dual feasible set, which can be solved by deterministic methods. Certain nonconvex quadratic programming problems in {\open {R}}^{n} can be converted into a dual problem with only one variable. It turns out that a complete set of solutions for quadratic programming over a sphere is obtained as a by-product. Several examples are illustrated.     

一类二层多目标规划的若干性质

本文对于下层为线性多目标规划的二层规划问题,在约束域非空有界的条件下证明了可行集的弱拟凸性、连通性,为算法设计提供了理论依据．     

Unboundedness in Integer and Discrete Programming L.P. Relaxations

Commercial branch and bound codes for solving the general mixed integer linear programming problem commence by solving the linear programming relaxation of the submitted problem, terminating if the relaxation is unbounded. It is assumed that the submitted problem is either unbounded or has no feasible solutions. It is shown that the assumption is correct for all integer programming problems which can be submitted to the currently available codes (though counter examples which cannot be so submitted are given), but that the assumption is generally incorrect for discrete linear programming problems (using for example the special ordered set construct). Sufficient conditions on formulations to ensure its correctness are given. One possible formulation approach, applicable to special ordered set situations, is discussed.     

一类稀疏约束非线性规划的约束规格

研究一类带有闭凸集约束的稀疏约束非线性规划问题,这类问题在变量选择、模式识别、投资组合等领域具有广泛的应用.首先引进了限制性Slater约束规格的概念,证明了该约束规格强于限制性M-F约束规格,然后在此约束规格成立的条件下,分析了其局部最优解成立的充分和必要条件.最后,对约束集合的两种具体形式,指出限制性Slater约束规格必满足,并给出了一阶必要性条件的具体表达形式.     

Prox-Regularization Methods for Generalized Fractional Programming

If a fractional program does not have a unique solution or the feasible set is unbounded, numerical difficulties can occur. By using a prox-regularization method that generates a sequence of auxiliary problems with unique solutions, these difficulties are avoided. Two regularization methods are introduced here. They are based on Dinkelbach-type algorithms for generalized fractional programming, but use a regularized parametric auxiliary problem. Convergence results and numerical examples are presented.     

On Optimization over the Efficient Set in Linear Multicriteria Programming

The efficient set of a linear multicriteria programming problem can be represented by a reverse convex constraint of the form g(z)≤0, where g is a concave function. Consequently, the problem of optimizing some real function over the efficient set belongs to an important problem class of global optimization called reverse convex programming. Since the concave function used in the literature is only defined on some set containing the feasible set of the underlying multicriteria programming problem, most global optimization techniques for handling this kind of reverse convex constraint cannot be applied. The main purpose of our article is to present a method for overcoming this disadvantage. We construct a concave function which is finitely defined on the whole space and can be considered as an extension of the existing function. Different forms of the linear multicriteria programming problem are discussed, including the minimum maximal flow problem as an example. The research was partly done while the third author was visiting the Department of Mathematics, University of Trier with the support by the Alexander von Humboldt Foundation. He thanks the university as well as the foundation.     

二层规划可行解的存在性

二层规划通常是用两个最优化问题来描述，其中第一个问题(上层问题)的约束集部分受限于第二个问题(下层问题)的最优响应。可行解的存在性是二层规划问题中一个基本而重要的研究内容, 该文借助于下层目标函数的Clarke'次微分映射的w伪单调性，着重讨论了这一问题。