Convex Optimization / Edition 1

Convex Optimization / Edition 1

ISBN-10:
0521833787
ISBN-13:
9780521833783
Pub. Date:
03/08/2004
Publisher:
Cambridge University Press

Hardcover

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Overview

Convex Optimization / Edition 1

Convex optimization problems arise frequently in many different fields. A comprehensive introduction to the subject, this book shows in detail how such problems can be solved numerically with great efficiency. The focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. The text contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance, and economics.

Product Details

ISBN-13: 9780521833783
Publisher: Cambridge University Press
Publication date: 03/08/2004
Edition description: New Edition
Pages: 727
Sales rank: 353,117
Product dimensions: 9.96(w) x 7.76(h) x 1.57(d)

About the Author

Stephen Boyd received his PhD from the University of California, Berkeley. Since 1985 he has been a member of the Electrical Engineering Department at Stanford University, where he is now Professor and Director of the Information Systems Laboratory. He has won numerous awards for teaching and research, and is a Fellow of the IEEE. He was one of the co-founders of Barcelona Design, and is the co-author of two previous books Linear Controller Design: Limits of Performance and Linear Matrix Inequalities in System and Control Theory.

Lieven Vandenberghe received his PhD from the Katholieke Universiteit, Leuven, Belgium, and is a Professor of Electrical Engineering at the University of California, Los Angeles. He has published widely in the field of optimization and is the recipient of a National Science Foundation CAREER award.

Table of Contents

Preface; 1. Introduction; Part I. Theory: 2. Convex sets; 3. Convex functions; 4. Convex optimization problems; 5. Duality; Part II. Applications: 6. Approximation and fitting; 7. Statistical estimation; 8. Geometrical problems; Part III. Algorithms: 9. Unconstrained minimization; 10. Equality constrained minimization; 11. Interior-point methods; Appendices.

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