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

Stephen Boyd

ISBN 10: 1316518965 / ISBN 13: 9781316518960

Published by Cambridge University Press, 2018

About this Item

2018. 1st Edition. hardcover. . . . . .

Seller Inventory # V9781316518960

Bibliographic Details

• Title: Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares
• Publisher: Cambridge University Press
• Publication Date: 2018
• Language: English
• ISBN 10: 1316518965
• ISBN 13: 9781316518960
• Binding: Hardcover
• Edition: 1st Edition
• Condition: New

About this title

Synopsis

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.

About the Authors

Stephen 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.

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).