Introduction to Applied Linear Algebra
Vectors, Matrices, and Least Squares
(Sprache: Englisch)
A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples.
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Produktinformationen zu „Introduction to Applied Linear Algebra “
A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples.
Klappentext zu „Introduction to Applied Linear Algebra “
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.
Inhaltsverzeichnis zu „Introduction to Applied Linear Algebra “
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.
Autoren-Porträt von Stephen Boyd, Lieven Vandenberghe
Boyd, StephenStephen Boyd is the Samsung Professor of Engineering, and Professor of Electrical Engineering at Stanford University,California, with courtesy appointments in the Department of Computer Science, and the Department of Management Science and Engineering. He is the co-author of Convex Optimization (Cambridge, 2004), written with Lieven Vandenberghe. Vandenberghe, Lieven
Lieven Vandenberghe is a Professor in the Electrical and Computer Engineering Department at the University of California, Los Angeles, with a joint appointment in the Department of Mathematics. He is the co-author, with Stephen Boyd, of Convex Optimization (Cambridge, 2004).
Bibliographische Angaben
- Autoren: Stephen Boyd , Lieven Vandenberghe
- 2018, 474 Seiten, Maße: 20,8 x 26 cm, Gebunden, Englisch
- Verlag: Cambridge University Press
- ISBN-10: 1316518965
- ISBN-13: 9781316518960
- Erscheinungsdatum: 21.02.2020
Sprache:
Englisch
Pressezitat
'Introduction to Applied Linear Algebra fills a very important role that has been sorely missed so far in the plethora of other textbooks on the topic, which are filled with discussions of nullspaces, rank, complex eigenvalues and other concepts, and by way of 'examples', typically show toy problems. In contrast, this unique book focuses on two concepts only, linear independence and QR factorization, and instead insists on the crucial activity of modeling, showing via many well-thought out practical examples how a deceptively simple method such as least-squares is really empowering. A must-read introduction for any student in data science, and beyond!' Laurent El Ghaoui, University of California, Berkeley
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