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Analysis of Boolean Functions

$70.00

  • Date Published: June 2014
  • availability: Available
  • format: Hardback
  • isbn: 9781107038325

$70.00
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  • 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, Håstad'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.

    • The first textbook on the field of analysis of Boolean functions
    • Appeals not just to researchers in theoretical computer science but also to mathematicians working on combinatorics, random graph theory, social choice and other areas
    • Accompanied by a blog/website with book content, 23 80-minute lecture videos, additional news content, comments and interaction
    • Includes roughly 500 exercises and can be used as the basis of a one-semester graduate course
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    Product details

    • Date Published: June 2014
    • format: Hardback
    • isbn: 9781107038325
    • length: 444 pages
    • dimensions: 229 x 152 x 25 mm
    • weight: 0.76kg
    • contains: 13 b/w illus. 450 exercises
    • availability: Available
  • Table of Contents

    1. Boolean functions and the Fourier expansion
    2. Basic concepts and social choice
    3. Spectral structure and learning
    4. DNF formulas and small-depth circuits
    5. Majority and threshold functions
    6. Pseudorandomness and F2-polynomials
    7. Property testing, PCPPs, and CSPs
    8. Generalized domains
    9. Basics of hypercontractivity
    10. Advanced hypercontractivity
    11. Gaussian space and invariance principles.

  • Author

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

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