模糊粗糙近似算子的拓扑性质

考虑论域上一二元关系所决定的模糊粗糙近似算子的拓扑性质,证明了任一自反二元关系可以决定一模糊拓扑.并且,当二元关系自反对称时,该模糊拓扑中的元是开集当且仅当它是闭集;当二元关系自反传递时,该模糊拓扑的闭包与内部算子恰为模糊粗糙上、下近似算子.     

由子基生成的内部算子和闭包算子

本文研究粗糙集与拓扑空间的关系,统一地使用拓扑空间中的集合关于子基的内部和闭包来研究粗糙集理论和覆盖广义粗糙集理论中的下近似集和上近似集,以及由它们导出的关于子基的开集,导集,闭集,边界．研究这两个概念及由它们导出的相关概念的性质不仅对于粗糙集理论,而且对于拓扑学本身都有重要的理论和实际应用意义．     

Rodabaugh α-闭包的推广

对LF拓扑空间的分明集及模糊格L中的元素α,Rodabaugh提出了α-闭包的概念.本文对此做了推广，对LF拓扑空间中的任一LF集，定义了HFα-闭包．并顺便引入了HFα-闭集的概念.文中讨论了这两个概念的基本性质。     

决策信息系统中的拓扑学方法

在决策信息系统中引入拓扑结构,借助拓扑学的基本概念(拓扑、内部和闭包等)研究决策问题,用它们刻画决策信息系统中的一些重要概念(决策协调集、决策约简集、下近似协调集、下近似约简集、上近似协调集、上近似约简集),并利用它们把这些重要概念推广到最一般的情况,建立起相应的约简理论.     

覆盖空间及粗糙集与拓扑的统一

引入覆盖空间,定义了其邻域、内部、闭包、测度等概念,研究了它们的性质.得出了粗糙集近似空间和拓扑空间都是具体覆盖空间的重要结论,从而用覆盖空间统一了粗糙集和拓扑.利用覆盖空间,得到了粗糙集和拓扑中更深刻的性质,从算子论和集合论的角度丰富和深化了粗糙集与拓扑的内容.     

近似空间的拓扑性质及其应用

近似空间(U,R)的全体可定义集构成X上的一个拓扑.本文在不要求论域U是有限的前提下探讨近似空间上这个拓扑的局部性质和可数性质,以及拓扑空间可近似化的充要条件及公理化体系,并寻找它们在粗糙集理论中的应用.     

由上近似数诱导的拟阵及其特征

拟阵理论与粗糙集理论之间有很多相似之处,近年来,探讨这二者之间的联系成为一个研究热点.首先利用基于等价关系的上近似数诱导了一系列拟阵结构,然后刻画了这类拟阵的独立集、基集、秩函数以及闭包等,讨论了与其它拟阵之间的一些联系.     

从边界运算出发建立拓扑空间

拓扑空间是现代数学中的一个重要的基本概念 .在集合上建立拓扑空间的方法很多 ,通常用开集公理来刻划 ,也可以选取点的邻域系 ,闭集 ,集合的闭包和内部等作为拓扑的原始概念 .本文选取集合的边界作为原始概念 ,在集合上建立拓扑空间     

近似凸集的某些性质

主要研究了两类近似凸集的关系和性质.首先,举例说明两类近似凸集没有相互包含关系.其次,在近似凸集(nearly convex)条件下,证明了在一定条件下函数上图是近似凸集与凸集的等价关系.同时,考虑了近似凸函数与函数上图是近似凸集的等价刻画、近似凸函数与函数水平集是近似凸集的必要性,并用例子说明近似凸函数与函数水平集是近似凸集的充分性不成立.最后,基于近似凸函数和拟凸函数的概念,给出了近似拟凸函数的概念并研究了近似拟凸函数与水平集是近似凸集的等价刻画.     

覆盖下近似算子的拓扑性质

在不限制U为有限论域的情况下,研究了覆盖下近似算子XL和CL的拓扑性质。证明了覆盖下近似算子XL是内部算子,而且由XL生成的拓扑TXL为包含由覆盖C本身作为子基生成的拓扑TC的最小Alexandrov拓扑。特别地,当U为有限论域时,TXL=TC.然而,覆盖下近似算子CL不是内部算子。当覆盖C为某拓扑的基时,CL是内部算子,且此时由CL生成的拓扑TCL与TC是同一个拓扑。若进一步要求U为有限论域,则TCL=TXL=TC,进而CL=XL.     

Approximate Optimality and Approximate Duality for Quasi Approximate Solutions in Robust Convex Semidefinite Programs

In this paper, we study quasi approximate solutions for a convex semidefinite programming problem in the face of data uncertainty. Using the robust optimization approach (worst-case approach), approximate optimality conditions and approximate duality theorems for quasi approximate solutions in robust convex semidefinite programming problems are explored under the robust characteristic cone constraint qualification. Moreover, some examples are given to illustrate the obtained results.     

Approximate Duality

We extend the Lagrangian duality theory for convex optimization problems to incorporate approximate solutions. In particular, we generalize well-known relationships between minimizers of a convex optimization problem, maximizers of its Lagrangian dual, saddle points of the Lagrangian, Kuhn–Tucker vectors, and Kuhn–Tucker conditions to incorporate approximate versions. As an application, we show how the theory can be used for convex quadratic programming and then apply the results to support vector machines from learning theory.     

Approximate Inclusion-Exclusion

The Inclusion-Exclusion formula expresses the size of a union of a family of sets in terms of the sizes of intersections of all subfamilies. This paper considers approximating the size of the union when intersection sizes are known for only some of the subfamilies, or when these quantities are given to within some error, or both.In particular, we consider the case when allk-wise intersections are given for everykK. It turns out that the answer changes in a significant way aroundK=n: ifKO(n) then any approximation may err by a factor of (n/K ²), while ifK (n) it is shown how to approximate the size of the union to within a multiplicative factor of .When the sizes of all intersections are only given approximately, good bounds are derived on how well the size of the union may be approximated. Several applications for Boolean function are mentioned in conclusion.Partially supported by NSF 865727-CCR and ARO DALL03-86-K-017. Part of this work was done in U.C. Berkeley, supported by NSF CCR-8411954.     

Approximate Homomorphisms

which ``almost everywhere' looks like an ultrafilter has to be close to some fixed ultrafilter. Received: August 20, 1997     

Approximate AF flows

When is a flow on a unital AF algebra A such that there is an increasing sequence (A_n) of finite-dimensional -invariant C*-subalgebras of A with dense union, we call an AF flow. We show that an approximate AF flow is a cocycle perturbation of an AF flow.     

Approximate portfolio analysis

This paper presents a portfolio selection model based on the idea of approximation. The model describes a portfolio by its decumulative distribution curve and a preference structure by a family of convex indifference curves. It prescribes the optimal portfolio as the one whose decumulative curve has the highest tangent indifference curve. The model extends the mean–variance model in the sense that it does not restrict the return distributions of assets to be normal. While under the assumption of normality, the model simplifies to the mean–variance model. The model has a measure of risk attitudes that resembles the Arrow–Pratt measure while combining both wealth and probability attitudes. Using this measure, we show that the smaller the curvature of a value function and the larger the curvature of a weighting function, the more risk averse an agent.