Harmonic analysis on finite groups: representation theory, Gelfand pairs and Markov chains

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Introduction to "Harmonic Analysis on Finite Groups: Representation Theory, Gelfand Pairs, and Markov Chains"

"Harmonic Analysis on Finite Groups: Representation Theory, Gelfand Pairs, and Markov Chains" serves as an authoritative and comprehensive guide for readers interested in exploring the fascinating interplay between abstract algebra, probability theory, and the analytical tools of harmonic analysis. This book is crafted for advanced students, researchers, and professionals in mathematics, computer science, and related fields seeking a deeper understanding of finite groups, representation theory, and their applications to Markov chains and probability processes.

Written by Tullio Ceccherini-Silberstein, Fabio Scarabotti, and Filippo Tolli, the text offers a rigorous yet accessible approach to finite group theory while highlighting its profound connections to harmonic analysis and theoretical computer science. Whether you're a mathematician drawn to representation theory or a data scientist exploring Markov processes, this book provides the fundamental tools and insights you need to navigate these interconnected topics.

Detailed Summary of the Book

The book begins by introducing finite groups with a review of basic group theory, setting the stage for a deeper dive into representations of finite groups in subsequent chapters. This foundational knowledge is essential for understanding how group representations can be used to analyze functions on groups—a core principle of harmonic analysis.

One major focus of the book is the study of Gelfand pairs, which play an essential role in harmonic analysis on finite groups. Gelfand pairs enable the decomposition of functions on finite groups into harmonic components, streamlining computations in relevant algebraic and probabilistic applications. Readers are guided through both the theoretical framework and practical applications of Gelfand pairs, including their role in studying symmetric structures and expanding functions in terms of irreducible characters.

Another highlight is the connection between harmonic analysis and the theory of Markov chains. By leveraging tools from harmonic analysis and group representations, the authors provide new insights into the behavior and convergence properties of Markov processes, particularly in the context of random walks on groups. These insights have significant implications for algorithms, especially in fields such as combinatorics, cryptography, and optimization.

The book concludes with advanced topics that offer readers an opportunity to explore cutting-edge applications and modern developments in these fields. By the end, readers will have acquired a firm grasp of the interplay between algebraic structures, harmonic analysis, and stochastic processes.

Key Takeaways

  • A clear understanding of the representation theory of finite groups and its role in harmonic analysis.
  • Comprehensive knowledge of Gelfand pairs and their applications in decomposing functions on groups.
  • Insights into the relationship between harmonic analysis and Markov chains, especially for analyzing random walks on groups.
  • Practical examples and exercises that connect theoretical concepts to real-world applications.
  • Exposure to modern research and advanced topics that continue to influence the study of finite groups and probability.

Famous Quotes from the Book

"Representation theory provides not just a powerful tool for studying groups, but also a unifying perspective that links symmetries across mathematics, physics, and beyond."

Tullio Ceccherini-Silberstein, Fabio Scarabotti, Filippo Tolli

"The theory of Gelfand pairs illuminates the hidden harmony of structures, allowing the complexity of finite groups to dissolve into simplicity."

Authors of the Book

Why This Book Matters

This book holds special significance for anyone interested in understanding the deep connections between mathematics and applications in engineering, physics, and computer science. By offering a structured yet expansive treatment, the authors bridge abstract concepts and practical applications in an accessible manner, making it an invaluable resource for both theoretical and applied disciplines.

Additionally, the integration of Markov chains and harmonic analysis highlights the book's relevance to contemporary topics like machine learning, network analysis, and quantum computing. As these fields continue to expand, the analytical techniques presented in this book are likely to grow even more influential in solving complex, multidisciplinary problems.

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