Paperback : $75.98
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 toolkit from the Chernoff-Hoeffding 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 Chernoff-Hoeffding bounds in dependent settings. The authors emphasise 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.
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 toolkit from the Chernoff-Hoeffding 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 Chernoff-Hoeffding bounds in dependent settings. The authors emphasise 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.
1. Chernoff–Hoeffding bounds; 2. Applying the CH-bounds; 3. CH-bounds with dependencies; 4. Interlude: probabilistic recurrences; 5. Martingales and the MOBD; 6. The MOBD in action; 7. Averaged bounded difference; 8. The method of bounded variances; 9. Interlude: the infamous upper tail; 10. Isoperimetric inequalities and concentration; 11. Talagrand inequality; 12. Transportation cost and concentration; 13. Transportation cost and Talagrand's inequality; 14. Log–Sobolev inequalities; Appendix A. Summary of the most useful bounds.
This book presents a coherent and unified account of classical and more advanced techniques for analyzing the performance of randomized algorithms.
Devdatt P. Dubhashi is Professor in the Department of Computer Science and Engineering at Chalmers University, Sweden. He earned a Ph.D. in computer science from Cornell University and held positions at the Max-Planck-Institute for Computer Science in Saarbruecken, BRICS, the University of Aarhus and IIT Delhi. Dubhashi has published widely at international conferences and in journals, including many special issues dedicated to best contributions. His research interests span the range from combinatorics to probabilistic analysis of algorithms, and more recently, to computational systems biology and distributed information systems such as the Web. Alessandro Panconesi is Professor of Computer Science at Sapienza University of Rome. He earned a Ph.D. in computer science from Cornell University and is the recipient of the 1992 ACM Danny Lewin Award. Panconesi has published more than 50 papers in international journals and selective conference proceedings and he is the associate editor of the Journal of Discrete Algorithms and the director of BiCi, the Bertinoro International Center of Informatics. His research spans areas of algorithmic research as diverse as randomized algorithms, distributed computing, complexity theory, experimental algorithmics, wireless networking and Web information retrieval.
Review of the hardback: 'It is beautifully written, contains all
the major concentration results, and is a must to have on your
desk.' Richard Lipton
Review of the hardback: 'Concentration bounds are at the core of
probabilistic analysis of algorithms. This excellent text provides
a comprehensive treatment of this important subject, ranging from
the very basic to the more advanced tools, including some recent
developments in this area. The presentation is clear and includes
numerous examples, demonstrating applications of the bounds in
analysis of algorithms. This book is a valuable resource for both
researchers and students in the field.' Eli Upfal, Brown
University
Review of the hardback: 'Concentration inequalities are an
essential tool for the analysis of algorithms in any probabilistic
setting. There have been many recent developments on this subject,
and this excellent text brings them together in a highly accessible
form.' Alan Frieze, Carnegie Mellon University
Review of the hardback: 'The book does a superb job of describing a
collection of powerful methodologies in a unified manner; what is
even more striking is that basic combinatorial and probabilistic
language is used in bringing out the power of such approaches. To
summarize, the book has done a great job of synthesizing diverse
and important material in a very accessible manner. Any student,
researcher, or practitioner of computer science, electrical
engineering, mathematics, operations research, and related fields,
could benefit from this wonderful book. The book would also make
for fruitful classes at the undergraduate and graduate levels. I
highly recommend it.' Aravind Srinivasan, SIGACT News
Review of the hardback: '… the strength of this book is that it is
appropriate for both the beginner as well as the experienced
researcher in the field of randomized algorithms … The exposition
style […] combines informal discussion with formal definitions and
proofs, giving first the intuition and motivation for the
probabalistic technique at hand. … I highly recommend this book
both as an advanced as well as an introductory textbook, which can
also serve the needs of an experienced researcher in algorithmics.'
Yannis C. Stamatiou, Mathematical Reviews
Reviews of the hardback: 'This timely book brings together in a
comprehensive and accessible form a sophisticated toolkit of
powerful techniques for the analysis of randomized algorithms,
illustrating their use with a wide array of insightful examples.
This book is an invaluable resource for people venturing into this
exciting field of contemporary computer science research.'
Prabhakar Ragahavan, Yahoo Research
 |
Ask a Question About this Product More... |
|
