Convex Optimization
£84.99
- Authors:
- Stephen Boyd, Stanford University, California
- Lieven Vandenberghe, University of California, Los Angeles
- Date Published: March 2004
- availability: In stock
- format: Hardback
- isbn: 9780521833783
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Convex optimization problems arise frequently in many different fields. This book provides a comprehensive introduction to the subject, and shows in detail how such problems can be solved numerically with great efficiency. The book begins with the basic elements of convex sets and functions, and then describes various classes of convex optimization problems. Duality and approximation techniques are then covered, as are statistical estimation techniques. Various geometrical problems are then presented, and there is detailed discussion of unconstrained and constrained minimization problems, and interior-point methods. The focus of the book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. It 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.
Read more- 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
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 introduction to this modern field of research.' Mathematics of Operations Research
See more reviews'… a beautiful book that I strongly recommend to everyone interested in optimization and computational mathematics … a very readable and inspiring introduction to this modern field of research. I recommend it as one of the best optimization textbooks that have appeared in the last years.' Mathematical Methods of Operations Research
'I highly recommend it either if you teach nonlinear optimization at the graduate level for a supplementary reading list and for your library, or if you solve optimization problems and wish to know more about solution methods and applications.' International Statistical institute
'… the whole book is characterized by clarity. … a very good pedagogical book … excellent to grasp the important concepts of convex analysis [and] to develop an art in modelling optimization problems intelligently.' Matapli
'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. MathSciNet
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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
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.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
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