有向图的边割(X,Y)中｜X｜和｜Y｜的下界与有向图的极大性和超级性

在已有的极大边连通、超级边连通、极大局部边连通有向图概念的基础上,提出超级局部边连通有向图的概念,对一般的、二部的、基础图的团数至多为p的有向图、定向图分别给出｜(X,Y)｜＜δ(D)的边割(X,Y)、非平凡的最小边割(X,Y)中｜X｜和｜Y｜的下界,据此分别得到极大边连通、超级边连通有向图的最小度条件.类似地分别得到...     

极大3限制边连通二部图的充分条件

设G=(V,E)是一个连通图.称一个边集合S■E是一个k限制边割,如果G-S的每个连通分支至少有k个顶点.称G的所有k限制边割中所含边数最少的边割的基数为G的k限制边连通度,记为λ_k(G).定义ξ_k(G)=min{[X,■]:|X|=k,G[X]连通,■=V(G)\X}.称图G是极大k限制边连通的,如果λ_k(G)=ξ_k(G).本文给出了围长为g>6的极大3限制边连通二部图的充分条件.     

正则图的限制性边连通度

将连通图分离成阶至少为二的分支之并的边割称为限制性边割,最小限制性边割的阶称为限制性边连通度. 用λ′(G)表示限制性连通度,则λ′(G)≤ξ(G),其中ξ(G)表示最小边度. 如果上式等号成立,则称G是极大限制性边连通的. 本文证明了当k＞|G|/2时,k正则图G是极大限制性边连通的,其中k≥2, |G|≥4; k的下界在某种程度上是不可改进的.     

关于拟k-连通图的一个注释

G是一个k-连通图,T是G的一个k-点割,若G-T可被划分成两个子图G1,G2,且|G1 |≥2,|G2 |≥2,则称T是G的一个非平凡点割.假定G是一个不含非平凡(k-1)点割的(k-1)-连通图,则称G是一个拟k-连通图.证明了对任意一个k≥5且t＞k/2的整数,若G是一个不含(K2+tK1)的k-连通图,且G中任...     

平面图 4-割问题的 O(|V|~2)算法

给定一个简单图 G=(V,E).V 是顶点集,E■V×V 是边集.所谓 k-割乃是E 的一个子集 E_1,它使图 G_1=(V,E—E_1)恰包含 k 个分支.寻找一个图的最小 k-割问题,无论在理论上和实践中都有重要的意义.Hochbaum 和 shmoys 在文献[1]中给出了平面图最小3-割的 O(|V|~2)算法.本文将给出一个平面图最小4-割的O(|V|~2)算法.本文用到的概念及符号记法均与文献[1]一致.     

极大非正则图的边数（英文）

设G是一个连通图,最大度和最小度分别为△(G)和δ(G).图G的非正则指标t(G)是指G的度序列中不同值的个数.如果t(G)=△(G)一δ(G)+1,则称图G为极大非正则图.本文给出了极大非正则图和不含三角的极大非正则图边数的上界,同时给出极大非正则图边数的一个紧的下界.     

偶一致超图划分问题的若干结果

划分问题因其在多个领域的重要应用一直是图论的研究热点.利用张量的特征值研究超图的划分与奇划分,并结合边割的界给出最大奇割、平均最小割、等周数等超图拓扑指标的界.当k取2时,这些结果与对应的图谱理论中的经典结论一致,因此可视为这些结论在超图的推广.     

图的极大Tutte集的几个有效算法

图G=(V,E)的Tutte集定义为X■V(G)满足ω_o(G-X)一|X|=def(G).若不存在Tutte集Y■X,则称X为图G的极大Tutte集.通过找极大extreme集和D-图的极大独立集给出一般图G的找极大Tutte集的两个有效算法,并给出结论:X■V(G)是二部图G的极大Tutte集当且仅当X为二部图G的最小覆盖,从而得到找二部图G的极大Tutte集的一个有效算法.     

最小最大树划分的近似算法与最小和树划分的精确算法

本文研究把连通赋权图的点集划分成p个子集,要求每个点子集的导出子图都连通,并且使得所得到的p个子图的最小支撑树中权重最大者的权重达到最小(最小最大树划分问题),或者使得所得到的p个子图的最小支撑树权重之和达到最小(最小和树划分问题)．文中给出了最小最大树划分问题的强NP困难性证明,并给出了一个多项式时间算法,该算法是最小最大树划分问题的竞争比为p的近似算法,同时是最小和树划分问题的精确算法．     

直径为5的图的2-导出匹配划分和2-导出匹配覆盖问题的NP-完全性

给定一个简单图G和正整数κ,具有完美匹配的图G的κ-导出匹配划分是对顶点集V(C)的一个κ-划分(V1,V2,...,Vκ),其中对每一个i(1≤i≤κ),由Vi导出的G的子图G[Vi]是1-正则的.κ-导出匹配划分问题是指对给定的图G,判定G是否存在一个κ-导出匹配划分.令M1,M2…,Mκ为图G的κ个导出匹配,如果V(M1)UV(M2)∪...∪V(Mκ)=V(G),则我们称{M1,M2,...,Mκ}是G的κ-导出匹配覆盖.κ-导出匹配覆盖问题是指对给定的图G,判定G是否存在κ-导出匹配覆盖.本文给出了Yang,Yuan和Dong所提出问题的解,证明了直径为5的图的导出匹配2一划分问题和导出匹配2-覆盖问题都是NP-完全的.     

Approximation of min–max and min–max regret versions of some combinatorial optimization problems

This paper investigates, for the first time in the literature, the approximation of min–max (regret) versions of classical problems like shortest path, minimum spanning tree, and knapsack. For a constant number of scenarios, we establish fully polynomial-time approximation schemes for the min–max versions of these problems, using relationships between multi-objective and min–max optimization. Using dynamic programming and classical trimming techniques, we construct a fully polynomial-time approximation scheme for min–max regret shortest path. We also establish a fully polynomial-time approximation scheme for min–max regret spanning tree and prove that min–max regret knapsack is not at all approximable. For a non-constant number of scenarios, in which case min–max and min–max regret versions of polynomial-time solvable problems usually become strongly NP-hard, non-approximability results are provided for min–max (regret) versions of shortest path and spanning tree.     

General approximation schemes for min–max (regret) versions of some (pseudo-)polynomial problems

While the complexity of min–max and min–max regret versions of most classical combinatorial optimization problems has been thoroughly investigated, there are very few studies about their approximation. For a bounded number of scenarios, we establish general approximation schemes which can be used for min–max and min–max regret versions of some polynomial or pseudo-polynomial problems. Applying these schemes to shortest path, minimum spanning tree, minimum weighted perfect matching on planar graphs, and knapsack problems, we obtain fully polynomial-time approximation schemes with better running times than the ones previously presented in the literature.     

Approximation by Shepard type pseudo-linear operators and applications to Image Processing

Recently, it has been shown that sum and product are not the only operations that can be used in order to define concrete approximation operators. Several other operations provided by fuzzy sets theory can be used. In the present paper, pseudo-linear approximation operators are investigated from the practical point of view in Image Processing. We study max–min, max–product Shepard type approximation operators together with Shepard operators based on pseudo-operations generated by an increasing continuous generator. It is shown that in several cases these outperform classical approximation operators based on sum and product operations.     

A smoothing algorithm for finite min–max–min problems

We generalize a smoothing algorithm for finite min–max to finite min–max–min problems. We apply a smoothing technique twice, once to eliminate the inner min operator and once to eliminate the max operator. In mini–max problems, where only the max operator is eliminated, the approximation function is decreasing with respect to the smoothing parameter. Such a property is convenient to establish algorithm convergence, but it does not hold when both operators are eliminated. To maintain the desired property, an additional term is added to the approximation. We establish convergence of a steepest descent algorithm and provide a numerical example.     

On the stability of normalized Powell–Sabin B-splines

In this paper we show that the normalized Powell–Sabin B-splines form a stable basis for the max norm. The approximation constants depend only on the smallest angle in the underlying triangulation. Since the B-splines refer to the size of the Powell–Sabin triangles, we find that small Powell–Sabin triangles yield better approximation constants than big Powell–Sabin triangles. Next, in addition to the max norm, we treat the L_p norm. Here the approximation constants depend also on a fraction proper to the triangulation, thus the B-splines are not stable for the L_p norm. Finally, as a special case, we consider the B-spline bases obtained from Powell–Sabin triangles with minimal area and pay extra attention to the approximation constants for the max norm.     

An approximate Newton method for non-smooth equations with finite max functions

A new version of finite difference approximation of the generalized Jacobian for a finite max function is constructed. Numerical results are reported for the generalized Newton methods using this approximation.     

最大割问题和最大平分割问题基于半定规划松弛的近似算法

考虑每条边具有非负权重的无向图, 最大割问题要求将顶点集划分为两个集合使得它们之间的边的权重之和最大. 当最大割问题半定规划松弛的最优解落到二维空间时, Goemans将近似比从0.87856...改进为0.88456. 依赖于半定规划松弛的目标值与总权和的比值的曲线, 此曲线的最低点为0.88456, 当半定规划松弛的目标值与总权和的比值在0.5到0.9044之间时, 利用Gegenbauer多项式舍入技巧, 改进了Zwick的近似比曲线. 进一步, 考虑最大割问题的重要变形------最大平分割问题, 在此问题中增加了划分的两部分的点数相等的要求. 同样考虑了最大平分割问题半定规划松弛的最优解落到二维空间的情形, 并利用前述的Gegenbauer多项式舍入技巧得到0.7091-近似算法.     

On scenario aggregation to approximate robust combinatorial optimization problems

As most robust combinatorial min–max and min–max regret problems with discrete uncertainty sets are NP-hard, research in approximation algorithm and approximability bounds has been a fruitful area of recent work. A simple and well-known approximation algorithm is the midpoint method, where one takes the average over all scenarios, and solves a problem of nominal type. Despite its simplicity, this method still gives the best-known bound on a wide range of problems, such as robust shortest path or robust assignment problems. In this paper, we present a simple extension of the midpoint method based on scenario aggregation, which improves the current best K-approximation result to an \((\varepsilon K)\)-approximation for any desired \(\varepsilon > 0\). Our method can be applied to min–max as well as min–max regret problems.     

Approximation of max independent set, min vertex cover and related problems by moderately exponential algorithms

Using ideas and results from polynomial time approximation and exact computation we design approximation algorithms for several NP-hard combinatorial problems achieving ratios that cannot be achieved in polynomial time (unless a very unlikely complexity conjecture is confirmed) with worst-case complexity much lower (though super-polynomial) than that of an exact computation. We study in particular two paradigmatic problems, max independent set and min vertex cover.     

基于区间直觉梯形模糊数的改进TOPSIS多属性决策方法

研究了属性权重完全未知的区间直觉梯形模糊数的多属性决策问题,结合TOPSIS方法定义了相对贴近度及总贴近度公式.首先由区间直觉梯形模糊数的Hamming距离给出了每个方案的属性与正负理想解的距离,基于此,给出了相对贴近度矩阵,根据所有决策方案的综合贴近度最小化建立多目标规划模型,从而确定属性的权重值,然后根据区间直觉梯形模糊数的加权算数平均算子求出各决策方案的总贴近度,根据总贴近度的大小对方案进行排序;最后,通过实例分析说明该方法的可行性和有效性.