Low-Rank Approximation Algorithms, Implementation, Applications /

• Main Author: Markovsky, Ivan (Author, http://id.loc.gov/vocabulary/relators/aut)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Edition: 2nd ed. 2019.
• Series: Communications and Control Engineering,
• Subjects: Control engineering.; Robotics.; Mechatronics.; System theory.; Computer science-Mathematics.; Mathematical models.; Artificial intelligence.; Signal processing.; Image processing.; Speech processing systems.; Control, Robotics, Mechatronics.; Systems Theory, Control.; Symbolic and Algebraic Manipulation.; Mathematical Modeling and Industrial Mathematics.; Artificial Intelligence.; Signal, Image and Speech Processing.

This book is a comprehensive exposition of the theory, algorithms, and applications of structured low-rank approximation. Local optimization methods and effective suboptimal convex relaxations for Toeplitz, Hankel, and Sylvester structured problems are presented. A major part of the text is devoted...

Table of Contents:

• Chapter 1. Introduction
• Part I: Linear modeling problems
• Chapter 2. From data to models
• Chapter 3. Exact modelling
• Chapter 4. Approximate modelling
• Part II: Applications and generalizations
• Chapter 5. Applications
• Chapter 6. Data-driven filtering and control
• Chapter 7. Nonlinear modeling problems
• Chapter 8. Dealing with prior knowledge
• Index. .