Synopsis:
The leading reference on probabilistic methods in combinatorics-now expanded and updated
When it was first published in 1991, The Probabilistic Method became instantly the standard reference on one of the most powerful and widely used tools in combinatorics. Still without competition nearly a decade later, this new edition brings you up to speed on recent developments, while adding useful exercises and over 30% new material. It continues to emphasize the basic elements of the methodology, discussing in a remarkably clear and informal style both algorithmic and classical methods as well as modern applications.
The Probabilistic Method, Second Edition begins with basic techniques that use expectation and variance, as well as the more recent martingales and correlation inequalities, then explores areas where probabilistic techniques proved successful, including discrepancy and random graphs as well as cutting-edge topics in theoretical computer science. A series of proofs, or "probabilistic lenses," are interspersed throughout the book, offering added insight into the application of the probabilistic approach. New and revised coverage includes:
* Several improved as well as new results
* A continuous approach to discrete probabilistic problems
* Talagrand's Inequality and other novel concentration results
* A discussion of the connection between discrepancy and VC-dimension
* Several combinatorial applications of the entropy function and its properties
* A new section on the life and work of Paul Erdös-the developer of the probabilistic method
From the Publisher:
One of the most powerful and popular tools used in combinatorics is the probabilistic method. Describes current algorithmic techniques, applying both the classical method and the modern tools it uses. Along with a detailed description of the techniques used in probabilistic arguments, it includes basic methods which utilize expectation and variance plus recent applications of martingales and correlation inequalities. Examines discrepancy and random graphs and covers such topics as theoretical computer science, computational geometry, derandomization of randomized algorithms and more. A study of various topics using successful probabilistic techniques is included along with an Open Problems Appendix by Paul Erdos, the founder of the probabilistic method.
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