Compressed Sensing Approach to Systems and Control
Compressed sensing, also known as sparse representation or sparse modeling, has experienced substantial growth in research fields such as signal processing, machine learning, and statistics. In recent years, this powerful tool has been successfully applied to the design of control systems.

This book provides a comprehensive guide to compressed sensing-based techniques, focusing primarily on their application to systems and control. This book is intended for graduate students and researchers who already have a foundational understanding of basic calculus and linear algebra. Its primary objective is to equip readers with the practical skills to apply compressed sensing techniques to a range of engineering problems, with a particular emphasis on systems and control. It presents a comprehensive collection of efficient algorithms for addressing the problems discussed in the text. Moreover, the book includes accompanying Python programs, which enable readers to actively experiment with these algorithms first-hand. By engaging with these practical examples, readers will develop a deeper understanding of compressed sensing techniques and their applications to systems and control.

This book is the second edition of the author’s previous work, Sparsity Methods for Systems and Control, published by Now Publishers in 2020. This edition incorporates significant updates to reflect the latest advancements in the field. Notably, it includes new chapters and sections covering the following key topics: Distributed optimization, Sparse system identification, Sparse controller design, and Distributed hands-off control.
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Compressed Sensing Approach to Systems and Control
Compressed sensing, also known as sparse representation or sparse modeling, has experienced substantial growth in research fields such as signal processing, machine learning, and statistics. In recent years, this powerful tool has been successfully applied to the design of control systems.

This book provides a comprehensive guide to compressed sensing-based techniques, focusing primarily on their application to systems and control. This book is intended for graduate students and researchers who already have a foundational understanding of basic calculus and linear algebra. Its primary objective is to equip readers with the practical skills to apply compressed sensing techniques to a range of engineering problems, with a particular emphasis on systems and control. It presents a comprehensive collection of efficient algorithms for addressing the problems discussed in the text. Moreover, the book includes accompanying Python programs, which enable readers to actively experiment with these algorithms first-hand. By engaging with these practical examples, readers will develop a deeper understanding of compressed sensing techniques and their applications to systems and control.

This book is the second edition of the author’s previous work, Sparsity Methods for Systems and Control, published by Now Publishers in 2020. This edition incorporates significant updates to reflect the latest advancements in the field. Notably, it includes new chapters and sections covering the following key topics: Distributed optimization, Sparse system identification, Sparse controller design, and Distributed hands-off control.
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Compressed Sensing Approach to Systems and Control

Compressed Sensing Approach to Systems and Control

by Masaaki Nagahara
Compressed Sensing Approach to Systems and Control

Compressed Sensing Approach to Systems and Control

by Masaaki Nagahara

Hardcover

$125.00 
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Overview

Compressed sensing, also known as sparse representation or sparse modeling, has experienced substantial growth in research fields such as signal processing, machine learning, and statistics. In recent years, this powerful tool has been successfully applied to the design of control systems.

This book provides a comprehensive guide to compressed sensing-based techniques, focusing primarily on their application to systems and control. This book is intended for graduate students and researchers who already have a foundational understanding of basic calculus and linear algebra. Its primary objective is to equip readers with the practical skills to apply compressed sensing techniques to a range of engineering problems, with a particular emphasis on systems and control. It presents a comprehensive collection of efficient algorithms for addressing the problems discussed in the text. Moreover, the book includes accompanying Python programs, which enable readers to actively experiment with these algorithms first-hand. By engaging with these practical examples, readers will develop a deeper understanding of compressed sensing techniques and their applications to systems and control.

This book is the second edition of the author’s previous work, Sparsity Methods for Systems and Control, published by Now Publishers in 2020. This edition incorporates significant updates to reflect the latest advancements in the field. Notably, it includes new chapters and sections covering the following key topics: Distributed optimization, Sparse system identification, Sparse controller design, and Distributed hands-off control.

Product Details

ISBN-13: 9781638285045
Publisher: Now Publishers
Publication date: 04/07/2025
Series: Nowopen
Pages: 274
Product dimensions: 6.14(w) x 9.21(h) x 0.63(d)

Table of Contents

Preface
Notation
Chapter 1. Introduction
Chapter 2. What is Sparsity?
Chapter 3. Sparse Optimization
Chapter 4. Algorithms for Convex Optimization
Chapter 5. Greedy Algorithms
Chapter 6. Distributed Optimization
Chapter 7. Applications of Compressed Sensing
Chapter 8. Dynamical Systems and Optimal Control
Chapter 9. Maximum Hands-off Control
Chapter 10. Numerical Optimization by Time Discretization
Chapter 11. Advanced Topics
References
Index
About the Author
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