An Introduction to Matrix Concentration Inequalities

Joel A. Tropp

ISBN 10: 1601988389 / ISBN 13: 9781601988386

Published by Now Publishers Inc, 2015

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Random matrices now play a role in many areas of theoretical, applied, and computational mathematics. It is therefore desirable to have tools for studying random matrices that are flexible, easy to use, and powerful. Over the last fifteen years, researchers have developed a remarkable family of results, called matrix concentration inequalities, that achieve all of these goals.This monograph offers an invitation to the field of matrix concentration inequalities. It begins with some history of random matrix theory; it describes a flexible model for random matrices that is suitable for many problems; and it discusses the most important matrix concentration results. To demonstrate the value of these techniques, the presentation includes examples drawn from statistics, machine learning, optimization, combinatorics, algorithms, scientific computing, and beyond.

Seller Inventory # 9781601988386

Bibliographic Details

• Title: An Introduction to Matrix Concentration Inequalities
• Publisher: Now Publishers Inc
• Publication Date: 2015
• Language: English
• ISBN 10: 1601988389
• ISBN 13: 9781601988386
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

Random matrices now play a role in many areas of theoretical, applied, and computational mathematics. It is therefore desirable to have tools for studying random matrices that are flexible, easy to use, and powerful. Over the last fifteen years, researchers have developed a remarkable family of results, called matrix concentration inequalities, that achieve all of these goals. This monograph offers an invitation to the field of matrix concentration inequalities. It begins with some history of random matrix theory; it describes a flexible model for random matrices that is suitable for many problems; and it discusses the most important matrix concentration results. To demonstrate the value of these techniques, the presentation includes examples drawn from statistics, machine learning, optimization, combinatorics, algorithms, scientific computing, and beyond.