Introduction to Applied Linear Algebra
Vectors, Matrices, and Least Squares

A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples.

Stephen Boyd (Author), Lieven Vandenberghe (Author)

9781316518960, Cambridge University Press

Hardback, published 7 June 2018

474 pages
25.3 x 19.5 x 2.5 cm, 1.18 kg

'… this book … could be used either as the textbook for a first course in applied linear algebra for data science or (using the first half of the book to review linear algebra basics) the textbook for a course in linear algebra for data science that builds on a prior to introduction to linear algebra … This is a very well written textbook that features significant mathematics, algorithms, and applications. I recommend it highly.' Brian Borchers, MAA Reviews

This groundbreaking textbook combines straightforward explanations with a wealth of practical examples to offer an innovative approach to teaching linear algebra. Requiring no prior knowledge of the subject, it covers the aspects of linear algebra - vectors, matrices, and least squares - that are needed for engineering applications, discussing examples across data science, machine learning and artificial intelligence, signal and image processing, tomography, navigation, control, and finance. The numerous practical exercises throughout allow students to test their understanding and translate their knowledge into solving real-world problems, with lecture slides, additional computational exercises in Julia and MATLAB®, and data sets accompanying the book online. Suitable for both one-semester and one-quarter courses, as well as self-study, this self-contained text provides beginning students with the foundation they need to progress to more advanced study.

Part I. Vectors: 1. Vectors
2. Linear functions
3. Norm and distance
4. Clustering
5. Linear independence
Part II. Matrices: 6. Matrices
7. Matrix examples
8. Linear equations
9. Linear dynamical systems
10. Matrix multiplication
11. Matrix inverses
Part III. Least Squares: 12. Least squares
13. Least squares data fitting
14. Least squares classification
15. Multi-objective least squares
16. Constrained least squares
17. Constrained least squares applications
18. Nonlinear least squares
19. Constrained nonlinear least squares
Appendix A
Appendix B
Appendix C
Appendix D
Index.

Subject Areas: Pattern recognition [UYQP], Machine learning [UYQM], Maths for engineers [TBJ], Optimization [PBU], Probability & statistics [PBT], Algebra [PBF], Econometrics [KCH]