Matrix Analysis and Applications

The theory, methods and applications of matrix analysis are presented here in a novel theoretical framework.

Xian-Da Zhang (Author)

9781108417419, Cambridge University Press

Hardback, published 5 October 2017

756 pages
25.4 x 17.9 x 4.6 cm, 1.48 kg

'This book provides various new topics on Matrix Analysis and Applications, which are not covered by some popular books on matrix analysis and computation. The text is well written, easy to be understood. There are several classical books on matrix analysis and computation on my bookshelf. But I will be happy to have this book on my bookshelf too.' Liqun Qi, Hong Kong Polytechnic University

This balanced and comprehensive study presents the theory, methods and applications of matrix analysis in a new theoretical framework, allowing readers to understand second-order and higher-order matrix analysis in a completely new light. Alongside the core subjects in matrix analysis, such as singular value analysis, the solution of matrix equations and eigenanalysis, the author introduces new applications and perspectives that are unique to this book. The very topical subjects of gradient analysis and optimization play a central role here. Also included are subspace analysis, projection analysis and tensor analysis, subjects which are often neglected in other books. Having provided a solid foundation to the subject, the author goes on to place particular emphasis on the many applications matrix analysis has in science and engineering, making this book suitable for scientists, engineers and graduate students alike.

Preface
Notation
List of abbreviations
List of algorithms
Part I. Matrix Algebra: 1. Introduction to matrix algebra
2. Special matrices
3. Matrix differential
Part II. Matrix Analysis: 4. Gradient analysis and optimization
5. Singular value analysis
6. Solving matrix equations
7. Eigenanalysis
8. Subspace analysis and tracking
9. Projection analysis
Part III. Higher-Order Matrix Analysis: 10. Tensor analysis
References
Index.

Subject Areas: Signal processing [UYS], Machine learning [UYQM], Mathematical physics [PHU], Mathematical modelling [PBWH]