An efficient parallel strategy for the perfect domination problem on distance-hereditary graphs

A graph is distance-hereditary if the distance stays the same between any of two vertices in every connected induced subgraph containing both. Two well-known classes of graphs, trees and cographs, both belong to distance-hereditary graphs. In this paper, we first show that the perfect domination problem can be solved in sequential linear-time on distance-hereditary graphs. By sketching some regular property of the problem, we also show that it can be easily parallelized on distance-hereditary graphs.     

An efficient parallel algorithm for the efficient domination problem on distance-hereditary graphs

In the literature, there are quite a few sequential and parallel algorithms for solving problems on distance-hereditary graphs. With an n-vertex and m-edge distance-hereditary graph G, we show that the efficient domination problem on G can be solved in O(log/sup 2/ n) time using O(n + m) processors on a CREW PRAM. Moreover, if a binary tree representation of G is given, the problem can be optimally solved in O(log n) time using O(n/log n) processors on an EREW PRAM.     

An efficient parallel strategy for the two-fixed-endpoint Hamiltonian path problem on distance-hereditary graphs

In this paper, we solve the two-fixed-endpoint Hamiltonian path problem on distance-hereditary graphs efficiently in parallel. Let T_d(|V|,|E|) and P_d(|V|,|E|) denote the parallel time and processor complexities, respectively, required to construct a decomposition tree of a distance-hereditary graph G=(V,E) on a PRAM model M_d. We show that this problem can be solved in O(T_d(|V|,|E|)+log|V|) time using O(P_d(|V|,|E|)+(|V|+|E|)/log|V|) processors on M_d. Moreover, if G is represented by its decomposition tree form, the problem can be solved optimally in O(log|V|) time using O((|V|+|E|)/log|V|) processors on an EREW PRAM. We also obtain a linear-time algorithm which is faster than the previous known O(|V|³) sequential algorithm.     

A parity domination problem in graphs with bounded treewidth and distance-hereditary graphs

This paper concerns a domination problem in graphs with parity constraints. The task is to find a subset of the vertices with minimum cost such that for every vertex the number of chosen vertices in its neighbourhood has a prespecified parity. This problem is known to be ${\mathcal NP}$ -hard for general graphs. A linear time algorithm was developed for series-parallel graphs and trees with unit cost and restricted to closed neighbourhoods. We present a linear time algorithm for the parity domination problem with open and closed neighbourhoods and arbitrary cost functions on graphs with bounded treewidth and distance-hereditary graphs.     

A simple parallel algorithm to draw cubic graphs

The main contribution of this work is to offer a simple and cost-efficient parallel algorithm that, given an arbitrary n-vertex cubic graph G as input, produces an orthogonal grid drawing of G in O(log n) time, using n processors on an EREW PRAM. Our algorithm matches the time and cost performance of the best previously-known algorithm while at the same time improving the constant factors involved in two important metrics: layout area and number of bends. More importantly, however, our algorithm stands out by its conceptual simplicity and ease of implementation.     

A self-stabilizing algorithm for coloring planar graphs

Summary This paper describes an algorithm for coloring the nodes of a planar graph with no more than six colors using a self-stabilizing approach. The first part illustrates the coloring algorithm on a directed acyclic version of the given planar graph. The second part describes a selfstabilizing algorithm for generating the directed acyclic version of the planar graph, and combines the two algorithms into one. Sukumar Ghosh received his Ph.D. degree in Computer Science from Calcutta University in 1971. From 1969 to 1984, he taught at Jadavpur University, Calcutta. During 1976–77, he was a Fellow of the Alexander von Humboldt Foundation at the University of Dortmund, Germany. Since 1984, he is with the Department of Computer Science of the University of Iowa. His current research interests are in the areas of Distributed Systems, Petri Nets and Self-Stabilizating Systems. Mehmet Hakan Karaata received the Sc. B. degree in Computer Science and Engineering from Hacettepe University in Turkey in 1987, and the M.S. degree in Computer Science from the University of Iowa in 1990. He is currently studying towards his Ph.D. at the same university. His research interests are in the areas of Distributed Systems, Self-Stabilizing Systems and Database Systems.This research was supported in part by the National Science Foundation under grant CCR-9109078, and the Old Gold Summer Fellowship of the University of Iowa. An abstract of this paper was presented at the 29th Allerton Conference on Control, Communication & Computing in October 1991.     

A note on relaxed equitable coloring of graphs

In this note we introduce the concept of equitable d-relaxed coloring. We prove that each graph with maximum degree at most r admits an equitable 1-relaxed r-coloring and provide a polynomial-time algorithm for constructing such a coloring.     

A hierarchical parallel genetic approach for the graph coloring problem

Graph Coloring Problems (GCPs) are constraint optimization problems with various applications including time tabling and frequency allocation. The GCP consists in finding the minimum number of colors for coloring the graph vertices such that adjacent vertices have distinct colors. We propose a hierarchical approach based on Parallel Genetic Algorithms (PGAs) to solve the GCP. We call this new approach Hierarchical PGAs (HPGAs). In addition, we have developed a new operator designed to improve PGAs when solving constraint optimization problems in general and GCPs in particular. We call this new operator Genetic Modification (GM). Using the properties of variables and their relations, GM generates good individuals at each iteration and inserts them into the PGA population in the hope of reaching the optimal solution sooner. In the case of the GCP, the GM operator is based on a novel Variable Ordering Algorithm (VOA) that we propose. Together with the new crossover and the estimator of the initial solution we have developed, GM allows our solving approach to converge towards the optimal solution sooner than the well known methods for solving the GCP, even for hard instances. This was indeed clearly demonstrated by the experiments we conducted on the GCP instances taken from the well known DIMACS website.     

A framework for scalable greedy coloring on distributed-memory parallel computers

We present a scalable framework for parallelizing greedy graph coloring algorithms on distributed-memory computers. The framework unifies several existing algorithms and blends a variety of techniques for creating or facilitating concurrency. The latter techniques include exploiting features of the initial data distribution, the use of speculative coloring and randomization, and a BSP-style organization of computation and communication. We experimentally study the performance of several specialized algorithms designed using the framework and implemented using MPI. The experiments are conducted on two different platforms and the test cases include large-size synthetic graphs as well as real graphs drawn from various application areas. Computational results show that implementations that yield good speedup while at the same time using about the same number of colors as a sequential greedy algorithm can be achieved by setting parameters of the framework in accordance with the size and structure of the graph being colored. Our implementation is freely available as part of the Zoltan parallel data management and load-balancing library.     

随机图的点可区别V-全染色算法

图[G]的点可区别V-全染色就是相邻的边、顶点与其关联边必须染不同的颜色,同时要求所有顶点的色集合也不相同,所用的最少颜色数称为图[G]的点可区别V-全色数。根据点可区别V-全染色的约束规则,设计了一种启发式的点可区别V-全染色算法,该算法借助染色矩阵及色补集合逐步迭代交换,每次迭代交换后判断目标函数值,当目标函数值满足要求时染色成功。给出了算法的详细描述、算法分析和算法测试结果,对给定点数的图进行了点可区别V-全染色猜想的验证。实验结果表明,该算法有很好的执行效率并可以得到给定图的点可区别V-全色数,并且算法的时间复杂度不超过[O(n3)]。     

Approximation algorithm for coloring of dotted interval graphs

Dotted interval graphs were introduced by Aumann et al. [Y. Aumann, M. Lewenstein, O. Melamud, R. Pinter, Z. Yakhini, Dotted interval graphs and high throughput genotyping, in: ACM-SIAM Symposium on Discrete Algorithms, SODA 2005, pp. 339-348] as a generalization of interval graphs. The problem of coloring these graphs found application in high-throughput genotyping. Jiang [M. Jiang, Approximating minimum coloring and maximum independent set in dotted interval graphs, Information Processing Letters 98 (2006) 29-33] improves the approximation ratio of Aumann et al. [Y. Aumann, M. Lewenstein, O. Melamud, R. Pinter, Z. Yakhini, Dotted interval graphs and high throughput genotyping, in: ACM-SIAM Symposium on Discrete Algorithms, SODA 2005, pp. 339-348]. In this work we improve the approximation ratio of Jiang [M. Jiang, Approximating minimum coloring and maximum independent set in dotted interval graphs, Information Processing Letters 98 (2006) 29-33] and Aumann et al. [Y. Aumann, M. Lewenstein, O. Melamud, R. Pinter, Z. Yakhini, Dotted interval graphs and high throughput genotyping, in: ACM-SIAM Symposium on Discrete Algorithms, SODA 2005, pp. 339-348]. In the exposition we develop a generalization of the problem of finding the maximum number of non-attacking queens on a triangle.     

A very fast parallel object store for very fast applications

An architecture for a memory-resident, Parallel and Persistent Object STore (PPOST) is proposed. Different object-oriented data-bases might be built on top of PPOST. The term memory-resident (or main memory based) means that the primary storage device is main memory. Persistence is guaranteed automatically by managing secondary and stable storage devices (such as main memory with uninterrupted power supply, discs and tapes). The architecture is able to take advantage of available idle main memory in a parallel or distributed environment. Thus, transactions can be actually performed with memory-speed, without being limited by the size of the memory of a given computer. Such an architecture is especially advantageous for applications requiring very fast response, such as CAD or high-performance simulation.     

A note on 2-facial coloring of plane graphs

An l-facial coloring of a plane graph is a vertex coloring such that any two different vertices joined by a facial walk of length at most l receive distinct colors. It is known that every plane graph admits a 2-facial coloring using 8 colors [D. Král, T. Madaras, R. Škrekovski, Cyclic, diagonal and facial coloring, European J. Combin. 3-4 (26) (2005) 473-490]. We improve this bound for plane graphs with large girth and prove that if G is a plane graph with girth g?14 (resp. 10, 8) then G admits a 2-facial coloring using 5 colors (resp. 6, 7). Moreover, we give exact bounds for outerplanar graphs and K₄-minor free graphs.     

Randomly coloring simple hypergraphs

We study the problem of constructing a (near) uniform random proper q-coloring of a simple k-uniform hypergraph with n vertices and maximum degree Δ. (Proper in that no edge is mono-colored and simple in that two edges have maximum intersection of size one.) We show that if for some α<1 we have Δ?nα and q?Δ(1+α)/kα then Glauber dynamics will become close to uniform in time from a random (improper) start. Note that for k>1+α−1 we can take q=o(Δ).     

A fast algorithm for computing sparse visibility graphs

AnO(¦E¦log² n) algorithm is presented to construct the visibility graph for a collection ofn nonintersecting line segments, where ¦E¦ is the number of edges in the visibility graph. This algorithm is much faster than theO(n ²)-time andO(n ²)-space algorithms by Asanoet al., and by Welzl, on sparse visibility graphs. Thus we partially resolve an open problem raised by Welzl. Further, our algorithm uses onlyO(n) working storage.     

A fast algorithm for computing sparse visibility graphs

AnO(¦E¦log² n) algorithm is presented to construct the visibility graph for a collection ofn nonintersecting line segments, where ¦E¦ is the number of edges in the visibility graph. This algorithm is much faster than theO(n ²)-time andO(n ²)-space algorithms by Asanoet al., and by Welzl, on sparse visibility graphs. Thus we partially resolve an open problem raised by Welzl. Further, our algorithm uses onlyO(n) working storage.