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Concentration of Measure for the Analysis of Randomized Algorithms This book presents a coherent and unified account of classical and more advanced techniques for analyzing the performance of randomized algorithms. Full description
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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
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 account of classical and more advanced techniques for analyzing the performance of such algorithms. The presentation emphasizes discrete settings and elementary notions of probability, making it accessible to computer scientists and applied discrete mathematicians.
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Book Description Cambridge University Press, 2009. Condition: New. book. Seller Inventory # M0521884276
Book Description Cambridge University Press, 2019. Hardcover. Condition: New. Brand New! This item is printed on demand. Seller Inventory # VIB0521884276
Book Description CAMBRIDGE UNIVERSITY PRESS, United Kingdom, 2014. Hardback. Condition: New. Language: English. Brand new Book. 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. Seller Inventory # LHB9780521884273
Book Description Cambridge University Press, 2009. HRD. Condition: New. New Book. Shipped from US within 10 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Seller Inventory # IQ-9780521884273
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Book Description Cambridge University Press. Hardcover. Condition: New. 0521884276 Brand New ,Original Book , Direct from Source , Express 5-8 business days worldwide delivery. Seller Inventory # CBG#A69649
Book Description Cambridge University Press, 2009. HRD. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Seller Inventory # IQ-9780521884273
Book Description Cambridge University Press, 2009. Hardcover. Condition: New. 1. Seller Inventory # DADAX0521884276
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Book Description Cambridge Univ Pr, 2009. Hardcover. Condition: Brand New. 1st edition. 248 pages. 8.90x6.10x0.90 inches. In Stock. Seller Inventory # __0521884276