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The book gives an accessible account of modern probabilistic methods for analyzing combinatorial structures and algorithms. It will be an useful guide for graduate students and researchers. Special features included: a simple treatment of Talagrand's inequalities and their applications; an overview and many carefully worked out examples of the probabilistic analysis of combinatorial algorithms; a discussion of the "exact simulation" algorithm (in the context of Markov Chain Monte Carlo Methods); a general method for finding asymptotically optimal or near optimal graph colouring, showing how the probabilistic method may be fine-tuned to exploit the structure of the underlying graph; a succinct treatment of randomized algorithms and derandomization techniques.
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Local Lemma, Monte Carlo, First Moment Method, Foundations of Computer Science, New York, Mathematical Programming, Operations Research, Las Vegas, Discrete Algorithms, Simple Concentration Bound, Academic Press, John Wiley, Lecture Notes, Cambridge University Press, Proceedings of the Cambridge Philosophical Society, Annual Symposium, Discrete Applied Mathematics, Journal of Algorithms, Technical Report, Annals of Applied Probability, Annals of Probability, Computer Society Press, Journal of Combinatorial Theory, University of California New!
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Local Lemma, Monte Carlo, First Moment Method, Foundations of Computer Science, New York, Mathematical Programming, Operations Research, Las Vegas, Discrete Algorithms, Simple Concentration Bound, Academic Press, John Wiley, Lecture Notes, Cambridge University Press, Proceedings of the Cambridge Philosophical Society, Annual Symposium, Discrete Applied Mathematics, Journal of Algorithms, Technical Report, Annals of Applied Probability, Annals of Probability, Computer Society Press, Journal of Combinatorial Theory, University of California New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
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This book cites 46
books:
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21
books
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- The Probabilistic Method (Wiley-Interscience Series in Discrete Mathematics and Optimization) by Noga Alon on 4 pages
- Computer Science by Sinclair on 4 pages
- Randomized Algorithms by Rajeev Motwani on page 93, page 114, and page 247
- Combinatorial Optimization: Annotated Bibliographies by M. O'Heigeartaigh on page 90, and page 114
- Computer Science by Andrew Meyenn on page 193, and page 194
See all 46 books this book cites
- Probability on Discrete Structures by Harry Kesten on page 70, and page 120
- Combinatorial Matrix Classes (Encyclopedia of Mathematics and its Applications) by Richard A. Brualdi on page 308, and Back Matter
- Advanced Lectures on Machine Learning: ML Summer Schools 2003, Canberra, Australia, February 2-14, 2003, Tübingen, Germany, August 4-16, 2003, Revised ... / Lecture Notes in Artificial Intelligence) by Olivier Bousquet on page 206, and Back Matter
- Average Case Analysis of Algorithms on Sequences (Wiley-Interscience Series in Discrete Mathematics and Optimization) by Wojciech Szpankowski in Back Matter
- Proceedings of the Sixth International Conference on Difference Equations Augsburg, Germany 2001: New Progress in Difference Equations by Bernd Aulbach on page 60
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