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Convex Optimization

Convex Optimization

$99.00 (C)

  • Date Published: March 2004
  • availability: In stock
  • format: Hardback
  • isbn: 9780521833783

$ 99.00 (C)

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About the Authors
  • 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.

    • Gives comprehensive details on how to recognize convex optimization problems in a wide variety of settings
    • Provides a broad range of practical algorithms for solving real problems
    • Contains hundreds of worked examples and homework exercises
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    Reviews & endorsements

    "Boyd and Vandenberghe have written a beautiful book that I strongly recommend to everyone interested in optimization and computational mathematics: Convex Optimization is a very readable and inspiring introduction to this modern field of research...The book will be accessible not only to mathematicians but also to researchers and students who want to use convex optimization in applied fields like engineering, computer science, economics, statistics, or others. I recommend it as one of the best optimization textbooks that have appeared in the last years."
    Mathematical Methods of Operations Research

    "...this concisely writen book is useful in many regards: as a primary textbook for convex optimization with engineering applications or as an alternate text for a more traditional course on linear or nonlinear optimization."
    Journal of the American Statistical Association, Hans-Jakob Luethi, Swiss Federal Institute of Technology Zurich

    "The book by Boyd and Vandenberghe reviewed here is one of … the best I have ever seen … it is a gentle, but rigorous, introduction to the basic concepts and methods of the field … this book is meant to be a 'first book' for the student or practitioner of optimization. However, I think that even the experienced researcher in the field has something to gain from reading this book: I have very much enjoyed the easy to follow presentation of many meaningful examples and suggestive interpretations meant to help the student's understanding penetrate beyond the surface of the formal description of the concepts and techniques. For teachers of convex optimization this book can be a gold mine of exercises."

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    Product details

    • Date Published: March 2004
    • format: Hardback
    • isbn: 9780521833783
    • length: 727 pages
    • dimensions: 254 x 197 x 40 mm
    • weight: 1.68kg
    • contains: 337 exercises
    • availability: In stock
  • Table of Contents

    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

  • Instructors have used or reviewed this title for the following courses

    • Advance topics in Engineering
    • Advanced Optimization
    • Advanced Optimization Methods
    • Algorithmics ll
    • Array Signal Processing
    • Cellular Mobile Communications
    • Cognitive Radios and Cognitive Radio Networks
    • Computer Algorithms
    • Computer Vision
    • Control Theory 2
    • Convex Optimization
    • Convex and Robust Optimization
    • Engineering Optimization Techniques
    • Foundations of Networks
    • Introduction to Optimization: Models and Methods
    • Linear Optimization and Convex Analysis
    • MIMO
    • Mathematical Computations ll -- Optimization
    • Neural signal processing
    • Nonlinear Optimization
    • Nonlinear programming
    • Optimal Control Systems
    • Optimization
    • Optimization Methods
    • Optimization for Machine Learning
    • Optimization of Wireless Networks
    • Real-Time Computing
    • Seepage and Earth Dams
    • Signal detection and estimation
    • Water Resources Engineering
  • Authors

    Stephen Boyd, Stanford University, California
    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, University of California, Los Angeles
    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.

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