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Restricted colorings of graphs
 in Surveys in Combinatorics 1993, London Math. Soc. Lecture Notes Series 187
, 1993
"... The problem of properly coloring the vertices (or edges) of a graph using for each vertex (or edge) a color from a prescribed list of permissible colors, received a considerable amount of attention. Here we describe the techniques applied in the study of this subject, which combine combinatorial, al ..."
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Cited by 75 (15 self)
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The problem of properly coloring the vertices (or edges) of a graph using for each vertex (or edge) a color from a prescribed list of permissible colors, received a considerable amount of attention. Here we describe the techniques applied in the study of this subject, which combine combinatorial, algebraic and probabilistic methods, and discuss several intriguing conjectures and open problems. This is mainly a survey of recent and less recent results in the area, but it contains several new results as well.
Greed is Good: Approximating Independent Sets in Sparse and . . .
, 1994
"... ... for short, is one of the ~implest, most efficient, and most thoroughly studied methods for finding independent sets in graphs. We show that it surprisingly achieves a performance ratio of (A+ 2)/3 for approximating independent sets in graphs with degree bounded by A. The analysis directs us tow ..."
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Cited by 55 (7 self)
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... for short, is one of the ~implest, most efficient, and most thoroughly studied methods for finding independent sets in graphs. We show that it surprisingly achieves a performance ratio of (A+ 2)/3 for approximating independent sets in graphs with degree bounded by A. The analysis directs us towards a simple parallel and distributed algorithm with identical performance, which on constantdegree graphs runs in O(log ” n) time using linear number of processors. We also analyze the Greedy algorithm when run in combination with a fractional relaxation technique of Nemhauser and Trotter, and obtain an improved (2Z + 3)/5 performance ratio on graphs with average degree ~. Finally, we introduce a generally applicable technique for improving the approximation ratios of independent set algorithms, and illustrate it by improving the performance ratio of Greedy for large ∆.
On Approximation Properties of the Independent Set Problem for Degree 3 Graphs
 In Proc. of Workshop on Algorithms and Data Structures
, 1995
"... . The main problem we consider in this paper is the Independent Set problem for bounded degree graphs. It is shown that the problem remains MAX SNPcomplete when the maximum degree is bounded by 3. Some related problems are also shown to be MAX SNPcomplete at the lowest possible degree bounds. N ..."
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Cited by 38 (0 self)
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. The main problem we consider in this paper is the Independent Set problem for bounded degree graphs. It is shown that the problem remains MAX SNPcomplete when the maximum degree is bounded by 3. Some related problems are also shown to be MAX SNPcomplete at the lowest possible degree bounds. Next we study better polytime approximation of the problem for degree 3 graphs, and improve the previously best ratio, 5 4 , to arbitrarily close to 6 5 . This result also provides improved polytime approximation ratios, B+3 5 + ffl, for odd degree B. 1 Introduction The area of efficient approximation algorithms for NPhard optimization problems has recently seen dramatic progress with a sequence of breakthrough achievements. Even when restricted only to the area of constant bound approximation the following remarkable results have been obtained in the last few years. The subclass of NP optimization problems, called MAX SNP, consisting solely of constant ratio approximable problems ...
On Brooks' theorem for sparse graphs
 Combinatorics, Probability and Computing
, 1995
"... Let G be a graph with maximum degree ∆(G). In this paper we prove that if the girth g(G) of G is greater than 4 then its chromatic number, χ(G), satisfies χ(G) ≤ (1 + o(1)) ∆(G) log ∆(G) where o(1) goes to zero as ∆(G) goes to infinity. (Our logarithms are base e.) More generally, we prove the same ..."
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Cited by 35 (4 self)
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Let G be a graph with maximum degree ∆(G). In this paper we prove that if the girth g(G) of G is greater than 4 then its chromatic number, χ(G), satisfies χ(G) ≤ (1 + o(1)) ∆(G) log ∆(G) where o(1) goes to zero as ∆(G) goes to infinity. (Our logarithms are base e.) More generally, we prove the same bound for the listchromatic (or choice) number: provided g(G)> 4. 1
Distributed Packet Switching in Arbitrary Networks
 In Proceedings of the 28th Annual ACM Symposium on Theory of Computing
, 1996
"... In a seminal paper Leighton, Maggs, and Rao consider the packet scheduling problem when a single packet has to traverse each path. They show that there exists a schedule where each packet reaches its destination in O(C + D) steps, where C is the congestion and D is the dilation. The proof relies o ..."
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Cited by 34 (2 self)
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In a seminal paper Leighton, Maggs, and Rao consider the packet scheduling problem when a single packet has to traverse each path. They show that there exists a schedule where each packet reaches its destination in O(C + D) steps, where C is the congestion and D is the dilation. The proof relies on the Lov'asz Local Lemma, and hence is not algorithmic. In a followup paper Leighton and Maggs use an algorithmic version of the Local Lemma due to Beck to give centralized algorithms for the problem. Leighton, Maggs, and Rao also give a distributed randomized algorithm where all packets reach their destinations with high probability in O(C +D log n) steps. In this paper we develop techniques to guarantee the high probability of delivering packets without resorting to the Lov'asz Local Lemma. We improve the distributed algorithm for problems with relatively high dilation to O(C) + (log n) O(log n) D + poly(log n). We extend the techniques to handle the case of infinite streams of ...
On the Relative Complexity of Approximate Counting Problems
, 2000
"... Two natural classes of counting problems that are interreducible under approximationpreserving reductions are: (i) those that admit a particular kind of ecient approximation algorithm known as an \FPRAS," and (ii) those that are complete for #P with respect to approximationpreserving reducibili ..."
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Cited by 34 (12 self)
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Two natural classes of counting problems that are interreducible under approximationpreserving reductions are: (i) those that admit a particular kind of ecient approximation algorithm known as an \FPRAS," and (ii) those that are complete for #P with respect to approximationpreserving reducibility. We describe and investigate not only these two classes but also a third class, of intermediate complexity, that is not known to be identical to (i) or (ii). The third class can be characterised as the hardest problems in a logically dened subclass of #P. Research Report 370, Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK. This work was supported in part by the EPSRC Research Grant \Sharper Analysis of Randomised Algorithms: a Computational Approach" and by the ESPRIT Projects RANDAPX and ALCOMFT. y dyer@scs.leeds.ac.uk, School of Computer Studies, University of Leeds, Leeds LS2 9JT, United Kingdom. z leslie@dcs.warwick.ac.uk, http://www.dcs.warw...
Coloring Graphs With Sparse Neighborhoods
, 1998
"... It is shown that the chromatic number of any graph with maximum degree d in which the number of edges in the induced subgraph on the set of all neighbors of any vertex does not exceed d²/f is at most O(d/log f). This is tight (up to a constant factor) for all admissible values of d and f. ..."
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Cited by 30 (15 self)
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It is shown that the chromatic number of any graph with maximum degree d in which the number of edges in the induced subgraph on the set of all neighbors of any vertex does not exceed d²/f is at most O(d/log f). This is tight (up to a constant factor) for all admissible values of d and f.
Mathematical foundations of the Markov chain Monte Carlo method
 in Probabilistic Methods for Algorithmic Discrete Mathematics
, 1998
"... 7.2 was jointly undertaken with Vivek Gore, and is published here for the first time. I also thank an anonymous referee for carefully reading and providing helpful comments on a draft of this chapter. 1. Introduction The classical Monte Carlo method is an approach to estimating quantities that a ..."
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Cited by 30 (1 self)
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7.2 was jointly undertaken with Vivek Gore, and is published here for the first time. I also thank an anonymous referee for carefully reading and providing helpful comments on a draft of this chapter. 1. Introduction The classical Monte Carlo method is an approach to estimating quantities that are hard to compute exactly. The quantity z of interest is expressed as the expectation z = ExpZ of a random variable (r.v.) Z for which some efficient sampling procedure is available. By taking the mean of some sufficiently large set of independent samples of Z, one may obtain an approximation to z. For example, suppose S = \Phi (x; y) 2 [0; 1] 2 : p i (x; y) 0; for all i \Psi<F12