Boyd / Vandenberghe Introduction to Applied Linear Algebra

Vectors, Matrices, and Least Squares
Erscheinungsjahr 2018
ISBN: 978-1-108-69394-3
Verlag: Cambridge University Press
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 0 Seiten

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. It is 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.

Autoren/Hrsg.

Boyd, Stephen

Vandenberghe, Lieven

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik Mathematik Algebra
• Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Ökonometrie
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Optimierung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung

Inhaltsverzeichnis

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; 18. Nonlinear least squares; 19. Constrained nonlinear least squares; Appendix A; Appendix B; Appendix C; Appendix D; Index.

Über Autor(innen)

Boyd, Stephen
Stephen P. Boyd is the Samsung Professor of Engineering, and Professor of Electrical Engineering at Stanford University with courtesy appointments in the Department of Computer Science, and the Department of Management Science and Engineering. He is the co-author ofConvex Optimization, written with Lieven Vandenberghe and published by Cambridge University Press in 2004.

Vandenberghe, Lieven
Lieven Vandenberghe is a Professor in the Electrical and Computer Engineering Department at UCLA, with a joint appointment in the Department of Mathematics. He is the co-author, with Stephen Boyd, of Convex Optimization (Cambridge, 2004).