Randomized algorithms have become a central part of the algorithms curriculum based on their increasingly widespread use in modern applications. This book presents a coherent and unified treatment of probabilistic techniques for obtaining high- probability estimates on the performance of randomized algorithms. It covers the basic tool kit from the Chernoff-Hoeffding (CH) bounds to more sophisticated techniques like Martingales and isoperimetric inequalities, as well as some recent developments like Talagrand's inequality, transportation cost inequalities, and log-Sobolev inequalities. Along the way, variations on the basic theme are examined, such as CH bounds in dependent settings. The authors emphasize comparative study of the different methods, highlighting respective strengths and weaknesses in concrete example applications. The exposition is tailored to discrete settings sufficient for the analysis of algorithms, avoiding unnecessary measure-theoretic details, thus making the book accessible to computer scientists as well as probabilists and discrete mathematicians.

/pdf/concentration-of-measure-for-the-analysis-of-randomized-468a4p8blg.pdf

Concentration of Measure for the Analysis of Randomized Algorithms

From social networks such as Facebook, the World Wide Web and the Internet, to the complex interactions between proteins in the cells of our bodies, we constantly face the challenge of understanding the structure and development of networks. The theory of random graphs provides a framework for this understanding, and in this book the authors give a gentle introduction to the basic tools for understanding and applying the theory. Part I includes sufficient material, including exercises, for a one semester course at the advanced undergraduate or beginning graduate level. The reader is then well prepared for the more advanced topics in Parts II and III. A final part provides a quick introduction to the background material needed. All those interested in discrete mathematics, computer science or applied probability and their applications will find this an ideal introduction to the subject.

/pdf/introduction-to-random-graphs-3cfvwg09nu.pdf

Introduction to random graphs

The differential equation method for random graph processes and greedy algorithms

It is not hard to write a first order formula which is true for a given graph G but is false for any graph not isomorphic to G. The smallest number D(G) of nested quantifiers in such a formula can serve as a measure for the “first order complexity” of G. Here, this parameter is studied for random graphs. We determine it asymptotically when the edge probability p is constant; in fact, D(G) is of order log n then. For very sparse graphs its magnitude is T(n). On the other hand, for certain (carefully chosen) values of p the parameter D(G) can drop down to the very slow growing function loga n, the inverse of the TOWER-function. The general picture, however, is still a mystery. © 2004 Wiley Periodicals, Inc. Random Struct. Alg., 2005

pdf/how-complex-are-random-graphs-in-first-order-logic-n8ugowfj9v.pdf

How complex are random graphs in first order logic

The focus of this monograph is on symmetry breaking problems in the message-passing model of distributed computing. In this model a communication network is represented by a n-vertex graph G = (V,E), whose vertices host autonomous processors. The processors communicate over the edges of G in discrete rounds. The goal is to devise algorithms that use as few rounds as possible. A typical symmetry-breaking problem is the problem of graph coloring. Denote by [delta] the maximum degree of G. While coloring G with [delta]+ 1 colors is trivial in the centralized setting, the problem becomes much more challenging in the distributed one. One can also compromise on the number of colors, if this allows for more efficient algorithms. Other typical symmetry-breaking problems are the problems of computing a maximal independent set (MIS) and a maximal matching (MM). The study of these problems dates back to the very early days of distributed computing. The founding fathers of distributed computing laid firm foundations for the area of distributed symmetry breaking already in the eighties. In particular, they showed that all these problems can be solved in randomized logarithmic time. Also, Linial showed that an O([delta]2)-coloring can be solved very efficiently deterministically. However, fundamental questions were left open for decades. In particular, it is not known if the MIS or the ([delta] + 1)-coloring can be solved in deterministic polylogarithmic time. Moreover, until recently it was not known if in deterministic polylogarithmic time one can color a graph with significantly fewer than [delta]2 colors. Additionally, it was open (and still open to some extent) if one can have sublogarithmic randomized algorithms for the symmetry breaking problems. Recently, significant progress was achieved in the study of these questions. More efficient deterministic and randomized ([delta] + 1)-coloring algorithms were achieved. Deterministic [delta]1 + o(1)-coloring algorithms with polylogarithmic running time were devised. Improved (and often sublogarithmic-time) randomized algorithms were devised. Drastically improved lower bounds were given. Wide families of graphs in which these problems are solvable much faster than on general graphs were identified. The objective of our monograph is to cover most of these developments, and as a result to provide a treatise on theoretical foundations of distributed symmetry breaking in the message-passing model. We hope that our monograph will stimulate further progress in this exciting area. Table of Contents: Acknowledgments / Introduction / Basics of Graph Theory / Basic Distributed Graph Coloring Algorithns / Lower Bounds / Forest-Decomposition Algorithms and Applications / Defective Coloring / Arbdefective Coloring / Edge-Coloring and Maximal Matching / Network Decompositions / Introduction to Distributed Randomized Algorithms / Conclusion and Open Questions / Bibliography / Authors' Biographies

https://www.cs.bgu.ac.il/~elkinm/book.pdf

Distributed Graph Coloring: Fundamentals and Recent Developments

We give a distributed randomized algorithm for graph edge colouring. Let G be a Δ-regular graph with n nodes. Here we prove: • • If e>0 is fixed and Δ⪢log n, the algorithm almost always colours G with (1 + e)Δ colours in time O(log n). • • If s>0 is fixed, there exists a positive constant k such that if Δ⪢logk n, the algorithm almost always colours G with Δ + Δ/logs n colours in time O(log n + logs n log log n). By “almost always” we mean that the algorithm may either use more than the claimed number of colours or run longer than the claimed time, but that the probability that either of these sorts of failure occurs can be made arbitrarily close to 0. The algorithm is based on the nibble method, a probabilistic strategy introduced by Vojtěch Rodl. The analysis makes use of a powerful large deviation inequality for functions of independent random variables.

Near-optimal, distributed edge colouring via the nibble method

An extremely simple distributed randomized edge colouring algorithm is given which produces with high probability a proper edge colouring of a given graph G using (1 + {epsilon}){Delta} (G) colours, for any {epsilon} > 0. The algorithm is very fast. In particular, for graphs with sufficiently large vertex degrees (larger than polylog n, but smaller than any positive power of n), the algorithm requires only O(log log n) communication rounds. The algorithm is inherently distributed, but can be implemented on the PRAM, where it requires O(m{Delta}) processors and O(log {Delta} log log n) time, or in a sequential setting, where it requires O(m{Delta}) time.

Nearly optimal distributed edge colouring in O(log log n) rounds

Nearly optimal distributed edge coloring in O (log log n ) rounds

Fast Distributed Algorithms for Brooks-Vizing Colorings

Nearly-perfect hypergraph packing is in NC

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