具有顶点与边限制的最大基数对集

简单图G=[V,E],任意给定SV,FE。求G的最大基数对集,Edmonds给出了著名的花算法[1]。我们首先利用修改算法[2],求复盖S中尽可能多的顶点的最大基数对集。当然,这样的对集可能还不是唯一的;在所有这样的对集中,我们要找一个对集,使得它进一步满足新增加的条件——使得|M∩|F|的基数最小。本文给出了这一问题的一个算法。算法的主要步骤是: ①利用改进的花算法[2],在G中找复盖S中尽可能多的顶点的最大基数对集,从而得     

无严格互补松驰条件的序列线性方程组新算法

该文通过构造特殊形式的有效集来逼近KKT点处的有效集，给出了一个任意初始点下的序列线性方程组新算法，并证明了该算法在没有严格互补松驰条件的情况下具有全局收敛性和一步超线性收敛性。      

基于最钝角规则的亏基对偶单纯形Ⅰ阶段算法

对偶单纯形算法或原始对偶单纯形算法都需要一个初始对偶可行基．就此目的而言，基于最钝角行主元规则的对偶Ⅰ阶段算法非常有效[15].本文将其思想应用于亏基情形，建立一个不含比值检验的新的亏基对偶Ⅰ价段算法．初步的数值实验表明，该算法可在总体上减少运行时间和迭代次数，极具竞争性．     

解非线性互补问题的约束积分水平集算法

本文考虑有约束的非线性互补问题的全局最优化问题,在文[1][5]的基础上,利用数论中一致分布佳点集列,给出了以数论方法代替Monte-Caclo投点的实现算法,并证明了所给实现算法的全局收敛性.最后所给出的两个数值例子表明本算法对求非线性互补问题是有效的.     

指定根的最小树形图的一个算法

文[1]给出了求有向图的最小树形图的算法,[2]中给出了求有指定根的最小树形图的算法.本文采用了[1]和[2]中的收缩回路的方法,给出了求有指定根的最小树形图的一个算法.设有向图G=(X,U),其中X={x_1,x_2,…,x_n}是顶点的集合,     

非线性规划的QP-free方法

该文提出一种QP-free可行域方法用来解满足光滑不等式约束的最优化问题.此方法把QP-free方法和3-1线性互补函数相结合一个等价于原约束问题的一阶KKT条件的方程组,并在此基础上给出解这个方程组的迭代算法. 这个方法的每一步迭代都可以看作是对求KKT条件解的牛顿或拟牛顿迭代的扰动,且在该方法中每一步的迭代均具有可行性. 该方法是可实行的且具有全局性, 且不需要严格互补条件、聚点的孤立性和积极约束函数梯度的线性独立等假设. 在与文献[2]中相同的适当条件下,此方法还具有超线性收敛性. 数值检验结果表示,该文提出的QP-free可行域方法是切实有效的方法.     

概率约束非线性规划及对偶规划的最优解

§ 1.引言E.komaromi在[1]、[2]中讨论了概率约束线性规划 min{CX|P(A_1X≥ξ)≥p,A_2X≥6}及对偶规划最优解的存在性,并给出了一个求其最优解的算法.本文讨论了概率约束非线性规划     

"求线性规划问题可行基的一种方法"的再注记

文[1]给出一个求线性规划问题可行基的方法，文[2]指出其判定条件（3）有误，然而所用的反例并不正确。本文给出三个正确的反例；此外，还给出反例表明文[1]的判定条件（2）也不正确的。     

一个绘制统筹图的算法

统筹图又叫计划网络图。任给一个其元素叫做工序（或作业或活动）的有限偏序集，要绘制它的一个最优统筹图，限含虚工序数目为最少者，是一个尚未从理论上解决的问题。本文讨论了虚工序产生的原因和如何减少虚工序数量的一些途径；指出了高度为二的编序集其最优统筹图含虚工序数目达到最大且等于该偏序集框图的边数的充分必要条件；本文给出了一个绘制最优统筹图的近似算法，此算法弥补了文[2]和[3]所给算法的一些不足之处。     

求解广义互补问题

1 引言 设为一闭凸锥,f是R~n到自身的一映射.广义互补问题,记作GCP(K,f),即找一向量x满足 GCP(K,f) x∈K,f(x)∈且x~Tf(x)=0,(1) 其中,是K的对偶锥(即对任一K中向量x,满足x~Ty≤0的所有y的集合).该问题首先 由Habetler和Price提出.当K=R_+~n(R~n空间的正卦限),此问题就是一般的互补问题.许多作者已经提出了很多求解线性或非线性互补问题的方法.例如:Dafermos,Fukushima,Harker和Price以及其它如参考文献所列.近年来,何针对单调线性变分不等式提出了一些投影收缩算法. Fang在函数是Lipschitz连续及强单调的条件下,在[3]给出一简单的迭代投影法,在[4]中给出一线性化方法去求解广义互补问题(1).在[3]中,他的迭代模式是     

Efficient Algorithms for Variants of Weighted Matching and Assignment Problems

Obtaining a matching in a graph satisfying a certain objective is an important class of graph problems. Matching algorithms have received attention for several decades. However, while there are efficient algorithms to obtain a maximum weight matching, not much is known about the maximum weight maximum cardinality, and maximum cardinality maximum weight matching problems for general graphs. Our contribution in this work is to show that for bounded weight input graphs one can obtain an algorithm for both maximum weight maximum cardinality (for real weights), and maximum cardinality maximum weight matching (for integer weights) by modifying the input and running the existing maximum weight matching algorithm. Also, given the current state of the art in maximum weight matching algorithms, we show that, for bounded weight input graphs, both maximum weight maximum cardinality, and maximum cardinality maximum weight matching have algorithms of similar complexities to that of maximum weight matching. Subsequently, we also obtain approximation algorithms for maximum weight maximum cardinality, and maximum cardinality maximum weight matching.      

Persistency in combinatorial optimization problems on matroids

Optimization problems on matroids are generalizations of such important combinatorial optimization problems like the problem of minimum spanning tree of a graph, the bipartite matching problem, flow problems, etc. We analyze algorithms for finding the maximum weight independent set of a matroid and for finding a maximum cardinality intersection of two matroids and extend them to obtain the so-called “persistency” partition of the basic set of the matroid, where contain elements belonging to all optimum solutions; contain elements not belonging to any optimum solution; contain elements that belong to some but not to all optimum solutions.     

Optimum matching forests I: Special weights

We introduce the concept of matching forests as a generalization of branchings in a directed graph and matchings in an undirected graph. Given special weights on the edges of a mixed graph, we present an efficient algorithm for finding an optimum weight-sum matching forest. The algorithm is a careful application of known branching and matching algorithms. The maximum cardinality matching forest problem is solved as a special case.Research partially supported by a N.R.C. of Canada Postdoctorate Fellowship.     

Improved processor bounds for combinatorial problems in RNC

Our main result improves the known processor bound by a factor ofn ⁴ (maintaining the expected parallel running time,O(log³ n)) for the following important problem:find a perfect matching in a general or in a bipartite graph with n vertices. A solution to that problem is used in parallel algorithms for several combinatorial problems, in particular for the problems of finding i) a (perfect) matching of maximum weight, ii) a maximum cardinality matching, iii) a matching of maximum vertex weight, iv) a maximums-t flow in a digraph with unit edge capacities. Consequently the known algorithms for those problems are substantially improved.The results of this paper have been presented at the 26-th Annual IEEE Symp. FOCS, Portland, Oregon (October 1985).Partially supported by NSF Grants MCS 8303139 and DCR 8511713.Supporeted by NSF Grants MCS 8203232 and DCR 8507573.     

赋权二部图最大匹配的灵敏分析

本主要从理论上讨论赋权二部图的权的变化对最优解的影响，并在原最大权匹配的基础上给出求解权值变化后的最大权匹配的算法。     

最大匹配图的围长

将一个图的所有最大匹配作为顶点集,称两个最大匹配相邻,若它们之一通过交换一条边得到另一个,由引所得图为该图的最大匹配图。本文研究了最大匹配图的围长,从而给出了最大匹配图是树或完全图的条件。     

Constructing a perfect matching is in random NC

We show that the problem of constructing a perfect matching in a graph is in the complexity class Random NC; i.e., the problem is solvable in polylog time by a randomized parallel algorithm using a polynomial-bounded number of processors. We also show that several related problems lie in Random NC. These include:

1. Constructing a perfect matching of maximum weight in a graph whose edge weights are given in unary notation;
2. Constructing a maximum-cardinality matching;
3. Constructing a matching covering a set of vertices of maximum weight in a graph whose vertex weights are given in binary;
4. Constructing a maximums-t flow in a directed graph whose edge weights are given in unary.

     

On finding augmenting graphs

Method of augmenting graphs is a general approach to solve the maximum independent set problem. As the problem is generally NP-hard, no polynomial time algorithms are available to implement the method. However, when restricted to particular classes of graphs, the approach may lead to efficient solutions. A famous example of this type is the maximum matching algorithm: it finds a maximum matching in a graph G, which is equivalent to finding a maximum independent set in the line graph of G. In the particular case of line graphs, the method reduces to finding augmenting (alternating) chains. Recent investigations of more general classes of graphs revealed many more types of augmenting graphs. In the present paper we study the problem of finding augmenting graphs different from chains. To simplify this problem, we introduce the notion of a redundant set. This allows us to reduce the problem to finding some basic augmenting graphs. As a result, we obtain a polynomial time solution to the maximum independent set problem in a class of graphs which extends several previously studied classes including the line graphs.     

On the two-connected planar spanning subgraph polytope

The problem of finding a two-connected planar spanning subgraph of maximum weight in a complete edge-weighted graph is important in automatic graph drawing. We investigate the problem from a polyhedral point of view.     

Tutte sets in graphs II: The complexity of finding maximum Tutte sets

A well-known formula of Tutte and Berge expresses the size of a maximum matching in a graph G in terms of what is usually called the deficiency. A subset X of V(G) for which this deficiency is attained is called a Tutte set of G. While much is known about maximum matchings, less is known about the structure of Tutte sets. We explored the structural aspects of Tutte sets in another paper. Here, we consider the algorithmic complexity of finding Tutte sets in a graph. We first give two polynomial algorithms for finding a maximal Tutte set. We then consider the complexity of finding a maximum Tutte set, and show it is NP-hard for general graphs, as well as for several interesting restricted classes such as planar graphs. By contrast, we show we can find maximum Tutte sets in polynomial time for graphs of level 0 or 1, elementary graphs, and 1-tough graphs.