Description
Connects fundamental mathematical theory with real-world problems, through efficient and scalable optimization algorithms.
About the Author
John Wright is an Associate Professor in the Electrical Engineering Department and the Data Science Institute at Columbia University. Yi Ma is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He is a Fellow of the IEEE, ACM, and SIAM.
Reviews
'Students will learn a lot from reading this book ... They will learn about mathematical reasoning, they will learn about data models and about connecting those to reality, and they will learn about algorithms. The book also contains computer scripts so that we can see ideas in action, and carefully crafted exercises making it perfect for upper-level undergraduate or graduate-level instruction. The breadth and depth make this a reference for anyone interested in the mathematical foundations of data science.' Emmanuel Candes, Stanford University (from the foreword)
'At the very core of our ability to process data stands the fact that sources of information are structured. Modeling data, explicitly or implicitly, is our way of exposing this structure and exploiting it, being the essence of the fields of signal and image processing and machine learning. The past two decades have brought a revolution to our understanding of these facts, and this 'must-read' book provides the foundations of these recent developments, covering theoretical, numerical, and applicative aspects of this field in a thorough and clear manner.' Michael Elad, Technion - Israel Institute of Technology
Book Information
ISBN 9781108489737
Author John Wright
Format Hardback
Page Count 650
Imprint Cambridge University Press
Publisher Cambridge University Press
Weight(grams) 1430g
Dimensions(mm) 251mm * 175mm * 36mm