Hardcover. Boolean functions are perhaps the most basic objects of study in theoretical computer science. They also arise in other areas of mathematics, including combinatorics, statistical physics, and mathematical social choice. The field of analysis of Boolean functions seeks to understand them via their Fourier transform and other analytic methods. This text gives a thorough overview of the field, beginning with the most basic definitions and proceeding to advanced topics such as hypercontractivity and isoperimetry. Each chapter includes a 'highlight application' such as Arrow's theorem from economics, the GoldreichLevin algorithm from cryptography/learning theory, Hastad's NP-hardness of approximation results, and 'sharp threshold' theorems for random graph properties. The book includes roughly 450 exercises and can be used as the basis of a one-semester graduate course. It should appeal to advanced undergraduates, graduate students and researchers in computer science theory and related mathematical fields. This graduate-level text gives a thorough overview of the field, beginning with the most basic definitions and proceeding to advanced topics such as hypercontractivity and isoperimetry. Each chapter includes a 'highlight application' such as Arrow's theorem, the GoldreichLevin algorithm, hardness of approximation, and 'sharp threshold' theorems for random graphs. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

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Bibliographic Details

Title: Analysis of Boolean Functions (Hardcover)
Publisher: Cambridge University Press, Cambridge
Publication Date: 2014
Language: English
ISBN 10: 1107038324
ISBN 13: 9781107038325
Binding: Hardcover
Condition: new

About this title

Synopsis

Boolean functions are perhaps the most basic objects of study in theoretical computer science. They also arise in other areas of mathematics, including combinatorics, statistical physics, and mathematical social choice. The field of analysis of Boolean functions seeks to understand them via their Fourier transform and other analytic methods. This text gives a thorough overview of the field, beginning with the most basic definitions and proceeding to advanced topics such as hypercontractivity and isoperimetry. Each chapter includes a “highlight application” such as Arrow's theorem from economics, the Goldreich-Levin algorithm from cryptography/learning theory, H�stad's NP-hardness of approximation results, and “sharp threshold” theorems for random graph properties. The book includes roughly 450 exercises and can be used as the basis of a one-semester graduate course. It should appeal to advanced undergraduates, graduate students, and researchers in computer science theory and related mathematical fields.

About the Author

Ryan O'Donnell is an Associate Professor in the Computer Science Department at Carnegie Mellon University.

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