An adaptive,multiple restarts neural network algorithm for graph coloring

The graph coloring problem is amongst the most difficult ones in combinatorial optimization, with a diverse set of significant applications in science and industry. Previous neural network attempts at coloring graphs have not worked well. In particular, they do not scale up to large graphs. Furthermore, experimental evaluations on real-world graphs have been lacking, and so have comparisons with state of the art conventional algorithms. In this paper we address all of these issues. We develop an improved neural network algorithm for graph coloring that scales well with graph size. The algorithm employs multiple restarts, and adaptively reduces the network's size from restart as it learns bettwe ways to color a given graph. Hence it gets faster and leaner as it evolves. We evaluate this algorithm on a structurally diverse set of graphs that arise in different applications. We compare its performance with that of a state of the art conventional algorithm on identical graphs. The conventional algorithm works better overall, though ours is not far behind. Ours works better on some graphs. The inherent parallel and distributed nature of our algorithm, especially within a neural network architecture, is a potential advantage for implementation and speed up.     

Path problems in generalized stars, complete graphs, and brick wall graphs

Path problems such as the maximum edge-disjoint paths problem, the path coloring problem, and the maximum path coloring problem are relevant for resource allocation in communication networks, in particular all-optical networks. In this paper, it is shown that maximum path coloring can be solved optimally in polynomial time for bidirected generalized stars, even in the weighted case. Furthermore, the maximum edge-disjoint paths problem is proved NP-hard for complete graphs (undirected or bidirected), a constant-factor off-line approximation algorithm is presented for the weighted case, and an on-line algorithm with constant competitive ratio is given for the unweighted case. Finally, an open problem concerning the existence of routings that simultaneously minimize the maximum load and the number of colors is solved: an example for a graph and a set of requests is given such that any routing that minimizes the maximum load requires strictly more colors for path coloring than a routing that minimizes the number of colors.     

Sequential and distributed graph coloring algorithms with performance analysis in random graph spaces

A sequential graph coloring algorithm and a strict distributed (broadcasting type) algorithm , and an analysis of their performance in scales of random graph spaces is presented. For a space of graphs with n vertices and a mean degree d(n), the number of colors produced is almost surely bounded by about d(n)/logd(n), which is almost surely not more than twice the chromatic number, and the distributed algorithm terminates in O(Max(d(n),logn)) steps.     

Distributed colorings for collision-free routing in sink-centric sensor networks

When the environment does not allow direct access to disseminated data, a sensor network could be one of the most appropriate solutions to retrieve the map of interesting areas. Based on existing approaches, we start our study from the standard random deployment of a sensor network and then we consider a coarse-grain localization algorithm that associates sensors with coordinates related to a central node, called the sink. Once each sensor is associated with an estimated position, it starts to send data to the sink according to a designed schedule of communications that minimizes energy consumption and time by means of collisions avoidance. The outcome is a challenging combinatorial coloring problem for a specific graph class. We propose a schedule of communications based on distributed and fast coloring algorithms. The proposed solutions solve the underlying problems for the graphs of interest by means of an optimal, and in some cases near-optimal, number of colors. Finally, as the localization provides coarse-grain coordinates, different sensors might be associated with the same coordinates. Hence, in order to avoid that all such sensors perform the same actions (i.e., waste energy), a leader-election mechanism is considered.     

求解图着色问题的量子蚁群算法

针对经典的图着色问题,在蚁群算法的基础上结合量子计算提出一种求解图着色问题的量子蚁群算法. 将量子比特和量子逻辑门引入到蚁群算法中,较好地避免了蚁群算法搜索易陷入局部极小的缺陷,并显著加快了算法的运算速度. 通过图着色实例的大量仿真实验,表明算法对图着色问题的求解是可行的、有效的,且具有通用性.     

Sub-exponential graph coloring algorithm for stencil-based Jacobian computations

Partial differential equations can be discretized using a regular Cartesian grid and a stencil-based method to approximate the partial derivatives. The computational effort for determining the associated Jacobian matrix can be reduced. This reduction can be modeled as a (grid) coloring problem. Currently, this problem is solved by using a heuristic approach for general graphs or by developing a formula for every single stencil. We introduce a sub-exponential algorithm using the Lipton–Tarjan separator in a divide-and-conquer approach to compute an optimal coloring. The practical relevance of the algorithm is evaluated when compared with an exponential algorithm and a greedy heuristic.     

An algorithm for road coloring

A coloring of edges of a finite directed graph turns the graph into a finite-state automaton. A synchronizing word of a deterministic automaton is a word in the alphabet of colors of its edges (regarded as letters) which maps the automaton to a single state. A coloring of edges of a directed graph of uniform outdegree (constant outdegree of any vertex) is synchronizing if the corresponding deterministic finite automaton possesses a synchronizing word.The road coloring problem is the problem of synchronizing coloring of a directed finite strongly connected graph of uniform outdegree if the greatest common divisor of lengths of all its cycles is one. Posed in 1970, it has evoked noticeable interest among the specialists in the theory of graphs, automata, codes, symbolic dynamics, and well beyond these areas.We present an algorithm for the road coloring of cubic worst-case time complexity and quadratic time complexity in the majority of studied cases. It is based on the recent positive solution of the road coloring problem. The algorithm was implemented in the freeware package TESTAS.     

An evolutionary approach for bandwidth multicoloring problems

In this paper we consider some generalizations of the vertex coloring problem, where distance constraints are imposed between adjacent vertices (bandwidth coloring problem) and each vertex has to be colored with more than one color (bandwidth multicoloring problem). We propose an evolutionary metaheuristic approach for the first problem, combining an effective tabu search algorithm with population management procedures. The approach can be applied to the second problem as well, after a simple transformation. Computational results on instances from the literature show that the overall algorithm is able to produce high quality solutions in a reasonable amount of time, outperforming the most effective algorithms proposed for the bandwidth coloring problem, and improving the best known solution of many instances of the bandwidth multicoloring problem.     

Solving Graph Coloring Problems with the Douglas-Rachford Algorithm

We present the Douglas-Rachford algorithm as a successful heuristic for solving graph coloring problems. Given a set of colors, these types of problems consist in assigning a color to each node of a graph, in such a way that every pair of adjacent nodes are assigned with different colors. We formulate the graph coloring problem as an appropriate feasibility problem that can be effectively solved by the Douglas-Rachford algorithm, despite the nonconvexity arising from the combinatorial nature of the problem. Different modifications of the graph coloring problem and applications are also presented. The good performance of the method is shown in various computational experiments.     

A GRASP for Coloring Sparse Graphs

We first present a literature review of heuristics and metaheuristics developed for the problem of coloring graphs. We then present a Greedy Randomized Adaptive Search Procedure (GRASP) for coloring sparse graphs. The procedure is tested on graphs of known chromatic number, as well as random graphs with edge probability 0.1 having from 50 to 500 vertices. Empirical results indicate that the proposed GRASP implementation compares favorably to classical heuristics and implementations of simulated annealing and tabu search. GRASP is also found to be competitive with a genetic algorithm that is considered one of the best currently available for graph coloring.     

Coloring planar graphs in parallel

We present NC algorithms for vertex and edge coloring planar graphs. The vertex coloring algorithm 5 colors any planar graph, and the edge coloring algorithm Δ edge colors planar graphs with Δ ≥ 23 (and Δ + 1 edge colors planar graphs with Δ < 23), where Δ is the maximum degree in the graph.     

An O(qn) algorithm to q-color a proper family of circular arcs

A family of arcs on a circle is proper if no arc is properly contained within another. While general minimal arc coloring is NP-complete, Orlin et al. recently obtained on O(n²) algorithm for q-coloring a proper family of arcs by modeling proper arc coloring as a shortest path problem in an associated network (with negative edges). This paper simplifies Orlin's shortest-path network to obtain an O(qn) time algorithm.     

Coloring Fuzzy Circular Interval Graphs

Computing the weighted coloring number of graphs is a classical topic in combinatorics and graph theory. Recently these problems have again attracted a lot of attention for the class of quasi-line graphs and more specifically fuzzy circular interval graphs.The problem is NP-complete for quasi-line graphs. For the subclass of fuzzy circular interval graphs however, one can compute the weighted coloring number in polynomial time using recent results of Chudnovsky and Ovetsky and of King and Reed. Whether one could actually compute an optimal weighted coloring of a fuzzy circular interval graph in polynomial time however was still open.We provide a combinatorial algorithm that computes weighted colorings and the weighted coloring number for fuzzy circular interval graphs efficiently. The algorithm reduces the problem to the case of circular interval graphs, then making use of an algorithm by Gijswijt to compute integer decompositions.     

On-line and first fit colorings of graphs

A graph coloring algorithm that immediately colors the vertices taken from a list without looking ahead or changing colors already assigned is called “on-line coloring.” The properties of on-line colorings are investigated in several classes of graphs. In many cases we find on-line colorings that use no more colors than some function of the largest clique size of the graph. We show that the first fit on-line coloring has an absolute performance ratio of two for the complement of chordal graphs. We prove an upper bound for the performance ratio of the first fit coloring on interval graphs. It is also shown that there are simple families resisting any on-line algorithm: no on-line algorithm can color all trees by a bounded number of colors.     

An ant-based algorithm for coloring graphs

This paper presents an ant-based algorithm for the graph coloring problem. An important difference that distinguishes this algorithm from previous ant algorithms is the manner in which ants are used in the algorithm. Unlike previous ant algorithms where each ant colors the entire graph, each ant in this algorithm colors just a portion of the graph using only local information. These individual coloring actions by the ants form a coloring of the graph. Even with the lack of pheromone laying capacity by the ants, the algorithm performed well on a set of 119 benchmark graphs. Furthermore, the algorithm produced very consistent results, having very small standard deviations over 50 runs of each graph tested.     

A Characterization of b‐Perfect Graphs

A b‐coloring is a coloring of the vertices of a graph such that each color class contains a vertex that has a neighbor in all other color classes, and the b‐chromatic number of a graph G is the largest integer k such that G admits a b‐coloring with k colors. A graph is b‐perfect if the b‐chromatic number is equal to the chromatic number for every induced subgraph of G. We prove that a graph is b‐perfect if and only if it does not contain as an induced subgraph a member of a certain list of 22 graphs. This entails the existence of a polynomial‐time recognition algorithm and of a polynomial‐time algorithm for coloring exactly the vertices of every b‐perfect graph. © 2011 Wiley Periodicals, Inc. J Graph Theory 71:95–122, 2012     

A Branch-and-Cut algorithm for graph coloring

In this paper a Branch-and-Cut algorithm, based on a formulation previously introduced by us, is proposed for the Graph Coloring Problem. Since colors are indistinguishable in graph coloring, there may typically exist many different symmetrical colorings associated with a same number of colors. If solutions to an integer programming model of the problem exhibit that property, the Branch-and-Cut method tends to behave poorly even for small size graph coloring instances. Our model avoids, to certain extent, that bottleneck. Computational experience indicates that the results we obtain improve, in most cases, on those given by the well-known exact solution graph coloring algorithm Dsatur.     

On the resolution of certain discrete univariate max–min problems

Graph coloring is a classical NP-hard combinatorial optimization problem with many practical applications. A broad range of heuristic methods exist for tackling the graph coloring problem: from fast greedy algorithms to more time-consuming metaheuristics. Although the latter produce better results in terms of minimizing the number of colors, the former are widely employed due to their simplicity. These heuristic methods are centralized since they operate by using complete knowledge of the graph. However, in real-world environmets where each component only interacts with a limited number of other components, the only option is to apply decentralized methods. This paper explores a novel and simple algorithm for decentralized graph coloring that uses a fixed number of colors and iteratively reduces the edge conflicts in the graph. We experimentally demonstrate that, for most of the tested instances, the new algorithm outperforms a recent and very competitive algorithm for decentralized graph coloring in terms of coloring quality. In our experiments, the fixed number of colors used by the new algorithm is controlled in a centralized manner.     

Parallel and On-Line Graph Coloring

We discover a surprising connection between graph coloring in two orthogonal paradigms: parallel and on-line computing. We present a randomized on-line coloring algorithm with a performance ratio ofO(n/log n), an improvement of factor over the previous best known algorithm of Vishwanathan. Also, from the same principles, we construct a parallel coloring algorithm with the same performance ratio, for the first such result. As a byproduct, we obtain a parallel approximation for the independent set problem.     

Constraint Propagation in Graph Coloring

In this paper we propose a method for integrating constraint propagation algorithms into an optimization procedure for vertex coloring with the goal of finding improved lower bounds. The key point we address is how to get instances of Constraint Satisfaction Problems (CSPs) from a graph coloring problem in order to give rise to new lower bounds outperforming the maximum clique bound. More precisely, the algorithms presented have the common goal of finding CSPs in the graph for which infeasibility can be proven. This is achieved by means of constraint propagation techniques which allow the algorithms to eliminate inconsistencies in the CSPs by updating domains dynamically and rendering such infeasibilities explicit. At the end of this process we use the largest CSP for which it has not been possible to prove infeasibility as an input for an algorithm which enlarges such CSP to get a feasible coloring. We experimented with a set of middle-high density graphs with quite a large difference between the maximum clique and the chromatic number.