Ivan Markovsky obtained Ph.D. in Electrical Engineering from the Katholieke Universiteit Leuven in 2005. Since then, he is teaching and doing research in control and system theory at the School of Electronics and Computer Science (ECS) of the University of Southampton and the Department of Fundamental Electricity and Instrumentation (ELEC) of the Vrije Universiteit Brussel, where he is currently an associate processor. His research interests are structured low-rank approximation, system identification, and data-driven control, topics on which he has published 70 peer-reviewed papers, 7 book chapters, and 2 monographs. He is an associate editor of the International Journal of Control and the SIAM Journal of Matrix Analysis and Applications. In 2011, Ivan Markovsky was awarded an ERC starting grant on the topic of structured low-rank approximation.

“Exercises in each section and the corresponding solutions provided will help the reader to practice with the presented algorithms. There is a great deal of well-established approximation methods and algorithms in data science. This book may prepare the reader in finding the appropriate approaches for solving the particular problems of interest. It can be recommended to both Ph.D. researchers and experienced scientists working on processing and analysis of large complex data.” (Boris N. Khoromskij, SIAM Review, Vol. 63 (4), December, 2021)“Markovsky’s book is certainly well suited for graduate students and more experienced readers, and should also be useful to people who need to apply LRA methods in their daily work.” (Kai Diethelm, Computing Reviews, July 18, 2019)

• 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.

Ivan Markovsky obtained Ph.D. in Electrical Engineering from the Katholieke Universiteit Leuven in 2005. Since then, he is teaching and doing research in control and system theory at the School of Electronics and Computer Science (ECS) of the University of Southampton and the Department of Fundamental Electricity and Instrumentation (ELEC) of the Vrije Universiteit Brussel, where he is currently an associate processor. His research interests are structured low-rank approximation, system identification, and data-driven control, topics on which he has published 70 peer-reviewed papers, 7 book chapters, and 2 monographs. He is an associate editor of the International Journal of Control and the SIAM Journal of Matrix Analysis and Applications. In 2011, Ivan Markovsky was awarded an ERC starting grant on the topic of structured low-rank approximation.

“Exercises in each section and the corresponding solutions provided will help the reader to practice with the presented algorithms. There is a great deal of well-established approximation methods and algorithms in data science. This book may prepare the reader in finding the appropriate approaches for solving the particular problems of interest. It can be recommended to both Ph.D. researchers and experienced scientists working on processing and analysis of large complex data.” (Boris N. Khoromskij, SIAM Review, Vol. 63 (4), December, 2021)“Markovsky’s book is certainly well suited for graduate students and more experienced readers, and should also be useful to people who need to apply LRA methods in their daily work.” (Kai Diethelm, Computing Reviews, July 18, 2019)

• 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.

Ivan Markovsky obtained Ph.D. in Electrical Engineering from the Katholieke Universiteit Leuven in 2005. Since then, he is teaching and doing research in control and system theory at the School of Electronics and Computer Science (ECS) of the University of Southampton and the Department of Fundamental Electricity and Instrumentation (ELEC) of the Vrije Universiteit Brussel, where he is currently an associate processor. His research interests are structured low-rank approximation, system identification, and data-driven control, topics on which he has published 70 peer-reviewed papers, 7 book chapters, and 2 monographs. He is an associate editor of the International Journal of Control and the SIAM Journal of Matrix Analysis and Applications. In 2011, Ivan Markovsky was awarded an ERC starting grant on the topic of structured low-rank approximation.

“Exercises in each section and the corresponding solutions provided will help the reader to practice with the presented algorithms. There is a great deal of well-established approximation methods and algorithms in data science. This book may prepare the reader in finding the appropriate approaches for solving the particular problems of interest. It can be recommended to both Ph.D. researchers and experienced scientists working on processing and analysis of large complex data.” (Boris N. Khoromskij, SIAM Review, Vol. 63 (4), December, 2021)“Markovsky’s book is certainly well suited for graduate students and more experienced readers, and should also be useful to people who need to apply LRA methods in their daily work.” (Kai Diethelm, Computing Reviews, July 18, 2019)

• 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.