Dr. Ivan Markovsky completed his PhD in the Electrical Engineering Department of the Katholieke Universiteit Leuven, Belgium under the supervision of S. Van Huffel, B. De Moor, and J.C. Willems. He was a postdoctoral researcher at the same department, and since January 2007, he has been a lecturer at the School of Electronics and Computer Science of the University of Southampton. His research interests are in system identification in the behavioural setting, total least squares, errors-in-variables estimation, and data-driven control; topics on which he has published 23 journal papers and one monograph (with SIAM). Dr. Markovsky won Honorable Mention in the Alston Householder Prize for best dissertation in numerical linear algebra. He is a co-organiser of the Fourth International Workshop on Total Least Squares and Errors-in-Variables Modelling, a guest editor of Signal Processing for a special issue on total least squares, and an associate editor of the International Journal of Control.

From the reviews: "This is a carefully-elaborated monographic work on low rank approximation. It covers the state of the art in this field (key theoretical topics accompanied by the description of the associated algorithms) and discusses various classes of applications. The book provides a rigorous and self-contained material, including numerical examples implemented in MATLAB and a collection of relevant problems. The exposition corresponds to a postgraduate level." (Octavian Pastravanu, Zentralblatt MATH, Vol. 1245, 2012) "This book gently takes the reader from the basic ideas of LRA to the most critical concepts, with an adequate number of examples to explain things along the way. ... Markovsky has presented LRA in a way that is unifying and cross-disciplinary. The pages abound with code, examples, applications, and problems, from which readers can pick according to their own interests and without the risk of losing the main thread of the book. ... it is a good reference for students, practitioners, and researchers." (Corrado Mencar, ACM Computing Reviews, December, 2012)

• Introduction.- From Data to Models.- Applications in System and Control Theory.- Applications in Signal Processing.- Applications in Computer Algebra.- Applications in Machine Learing.- Subspace-type Algorithms.- Algorithms Based on Local Optimization.- Data Smoothing and Filtering.- Recursive Algorithms.

Dr. Ivan Markovsky completed his PhD in the Electrical Engineering Department of the Katholieke Universiteit Leuven, Belgium under the supervision of S. Van Huffel, B. De Moor, and J.C. Willems. He was a postdoctoral researcher at the same department, and since January 2007, he has been a lecturer at the School of Electronics and Computer Science of the University of Southampton. His research interests are in system identification in the behavioural setting, total least squares, errors-in-variables estimation, and data-driven control; topics on which he has published 23 journal papers and one monograph (with SIAM). Dr. Markovsky won Honorable Mention in the Alston Householder Prize for best dissertation in numerical linear algebra. He is a co-organiser of the Fourth International Workshop on Total Least Squares and Errors-in-Variables Modelling, a guest editor of Signal Processing for a special issue on total least squares, and an associate editor of the International Journal of Control.

From the reviews: "This is a carefully-elaborated monographic work on low rank approximation. It covers the state of the art in this field (key theoretical topics accompanied by the description of the associated algorithms) and discusses various classes of applications. The book provides a rigorous and self-contained material, including numerical examples implemented in MATLAB and a collection of relevant problems. The exposition corresponds to a postgraduate level." (Octavian Pastravanu, Zentralblatt MATH, Vol. 1245, 2012) "This book gently takes the reader from the basic ideas of LRA to the most critical concepts, with an adequate number of examples to explain things along the way. ... Markovsky has presented LRA in a way that is unifying and cross-disciplinary. The pages abound with code, examples, applications, and problems, from which readers can pick according to their own interests and without the risk of losing the main thread of the book. ... it is a good reference for students, practitioners, and researchers." (Corrado Mencar, ACM Computing Reviews, December, 2012)

• Introduction.- From Data to Models.- Applications in System and Control Theory.- Applications in Signal Processing.- Applications in Computer Algebra.- Applications in Machine Learing.- Subspace-type Algorithms.- Algorithms Based on Local Optimization.- Data Smoothing and Filtering.- Recursive Algorithms.

Dr. Ivan Markovsky completed his PhD in the Electrical Engineering Department of the Katholieke Universiteit Leuven, Belgium under the supervision of S. Van Huffel, B. De Moor, and J.C. Willems. He was a postdoctoral researcher at the same department, and since January 2007, he has been a lecturer at the School of Electronics and Computer Science of the University of Southampton. His research interests are in system identification in the behavioural setting, total least squares, errors-in-variables estimation, and data-driven control; topics on which he has published 23 journal papers and one monograph (with SIAM). Dr. Markovsky won Honorable Mention in the Alston Householder Prize for best dissertation in numerical linear algebra. He is a co-organiser of the Fourth International Workshop on Total Least Squares and Errors-in-Variables Modelling, a guest editor of Signal Processing for a special issue on total least squares, and an associate editor of the International Journal of Control.

From the reviews: "This is a carefully-elaborated monographic work on low rank approximation. It covers the state of the art in this field (key theoretical topics accompanied by the description of the associated algorithms) and discusses various classes of applications. The book provides a rigorous and self-contained material, including numerical examples implemented in MATLAB and a collection of relevant problems. The exposition corresponds to a postgraduate level." (Octavian Pastravanu, Zentralblatt MATH, Vol. 1245, 2012) "This book gently takes the reader from the basic ideas of LRA to the most critical concepts, with an adequate number of examples to explain things along the way. ... Markovsky has presented LRA in a way that is unifying and cross-disciplinary. The pages abound with code, examples, applications, and problems, from which readers can pick according to their own interests and without the risk of losing the main thread of the book. ... it is a good reference for students, practitioners, and researchers." (Corrado Mencar, ACM Computing Reviews, December, 2012)

• Introduction.- From Data to Models.- Applications in System and Control Theory.- Applications in Signal Processing.- Applications in Computer Algebra.- Applications in Machine Learing.- Subspace-type Algorithms.- Algorithms Based on Local Optimization.- Data Smoothing and Filtering.- Recursive Algorithms.