Convex Optimization

This book helps the reader develop a working knowledge of convex optimization, i.e. to develop the skills and background needed to recognize, formulate, and solve convex optimization problems.

**Tag(s):**
Artificial Intelligence

**Publication date**: 01 Mar 2004

**ISBN-10**:
0521833787

**ISBN-13**:
0521833787

**Paperback**:
730 pages

**Views**: 19,191

Convex Optimization

This book helps the reader develop a working knowledge of convex optimization, i.e. to develop the skills and background needed to recognize, formulate, and solve convex optimization problems.

Terms and Conditions:

Book Excerpts:

This book is about convex optimization, a special class of mathematical optimization problems, which includes least-squares and linear programming problems. It is well known that least-squares and linear programming problems have a fairly complete theory, arise in a variety of applications, and can he solved numerically very efficiently. The basic point of this book is that the same can be said for the larger class of convex optimization problems.

The book's main goal is to help the reader develop a working knowledge of convex optimization, i.e., to develop the skills and background needed to recognize, formulate, and solve convex optimization problems.

There are several books on linear programming, and general nonlinear programming, that focus on problem formulation, modeling, and applications. Several other books cover the theory of convex optimization or interior-point methods and their complexity analysis. This book is meant to be something in between, a book on general convex optimization that focuses on problem formulation and modeling.

Intended Audience:

This book is meant for the researcher, scientist, or engineer who uses mathematical optimization. or more generally, computational mathematics. This includes, naturally, those working directly in optimization and operations research, and also many others who use optimization, in fields like computer science, economics, finance, statistics, data mining, and many fields of science and engineering. The book's primary focus is on the latter group, the potential users of convex optimization, and not the (less numerous) experts in the field of convex optimization.

The only background required of the reader is a good knowledge of advanced calculus and linear algebra. If the reader has seen basic mathematical analysis (e.g.. norms, convergence, elementary topology), and basic probability theory, he or she should he able to follow every argument and discussion in the book. Hopefully that readers who have not seen analysis and probability, however, can still get all of the essential ideas and important points. Prior exposure to numerical computing or optimization is not needed, since this book has all of the needed material from these areas in the text or appendices.

Review(s):

Amazon.com

:) "I think this book is a really good compromise between theory and practice: it can please the more mathematics-oriented with proofs, definitions, and bibliography; as well as the more application-oriented with examples, implementations, and heuristics."

:) "The book excels in readability and style. A perfect balance on the theoretical and practical aspets of the convex optimization."

Stephen Boyd wrote:Copyright in this book is held by Cambridge University Press, who have kindly agreed to allow us to keep the book available on the web.

Book Excerpts:

This book is about convex optimization, a special class of mathematical optimization problems, which includes least-squares and linear programming problems. It is well known that least-squares and linear programming problems have a fairly complete theory, arise in a variety of applications, and can he solved numerically very efficiently. The basic point of this book is that the same can be said for the larger class of convex optimization problems.

The book's main goal is to help the reader develop a working knowledge of convex optimization, i.e., to develop the skills and background needed to recognize, formulate, and solve convex optimization problems.

There are several books on linear programming, and general nonlinear programming, that focus on problem formulation, modeling, and applications. Several other books cover the theory of convex optimization or interior-point methods and their complexity analysis. This book is meant to be something in between, a book on general convex optimization that focuses on problem formulation and modeling.

Intended Audience:

This book is meant for the researcher, scientist, or engineer who uses mathematical optimization. or more generally, computational mathematics. This includes, naturally, those working directly in optimization and operations research, and also many others who use optimization, in fields like computer science, economics, finance, statistics, data mining, and many fields of science and engineering. The book's primary focus is on the latter group, the potential users of convex optimization, and not the (less numerous) experts in the field of convex optimization.

The only background required of the reader is a good knowledge of advanced calculus and linear algebra. If the reader has seen basic mathematical analysis (e.g.. norms, convergence, elementary topology), and basic probability theory, he or she should he able to follow every argument and discussion in the book. Hopefully that readers who have not seen analysis and probability, however, can still get all of the essential ideas and important points. Prior exposure to numerical computing or optimization is not needed, since this book has all of the needed material from these areas in the text or appendices.

Review(s):

Amazon.com

:) "I think this book is a really good compromise between theory and practice: it can please the more mathematics-oriented with proofs, definitions, and bibliography; as well as the more application-oriented with examples, implementations, and heuristics."

:) "The book excels in readability and style. A perfect balance on the theoretical and practical aspets of the convex optimization."

Tweet

About The Author(s)

Stephen P. Boyd is the Samsung Professor of Engineering, and Professor of Electrical Engineering in the Information Systems Laboratory at Stanford University. He has courtesy appointments in the Department of Management Science and Engineering and the Department of Computer Science, and is member of the Institute for Computational and Mathematical Engineering. His current research focus is on convex optimization applications in control, signal processing, finance, and circuit design.

Lieven Vandenberghe is Professor of Electrical Engineering and Mathematics at the University of California, Los Angeles.

Book Categories

Computer Science
Introduction to Computer Science
Introduction to Computer Programming
Algorithms and Data Structures
Artificial Intelligence
Computer Vision
Machine Learning
Neural Networks
Game Development and Multimedia
Data Communication and Networks
Coding Theory
Computer Security
Information Security
Cryptography
Information Theory
Computer Organization and Architecture
Operating Systems
Image Processing
Parallel Computing
Concurrent Programming
Relational Database
Document-oriented Database
Data Mining
Big Data
Data Science
Digital Libraries
Compiler Design and Construction
Functional Programming
Logic Programming
Object Oriented Programming
Formal Methods
Software Engineering
Agile Software Development
Information Systems
Geographic Information System (GIS)

Mathematics
Mathematics
Algebra
Abstract Algebra
Linear Algebra
Number Theory
Numerical Methods
Precalculus
Calculus
Differential Equations
Category Theory
Proofs
Discrete Mathematics
Theory of Computation
Graph Theory
Real Analysis
Complex Analysis
Probability
Statistics
Game Theory
Queueing Theory
Operations Research
Computer Aided Mathematics

Supporting Fields
Web Design and Development
Mobile App Design and Development
System Administration
Cloud Computing
Electric Circuits
Embedded System
Signal Processing
Integration and Automation
Network Science
Project Management

Operating System
Programming/Scripting
Ada
Assembly
C / C++
Common Lisp
Forth
Java
JavaScript
Lua
Microsoft .NET
Rexx
Perl
PHP
Python
R
Rebol
Ruby
Scheme
Tcl/Tk

Miscellaneous
Sponsors