Convex optimization with corrections 2008
Rating:
4,8/10
1982
reviews

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. Finally, I purchased a copy of the book at a student bookstore and just wanted to recommend it here. Seller Inventory 59011 Book Description Condition: New. Under the local error bound condition which is weaker than nonsingularity, the method has cubic convergence. This book trains you to recognize convexity, gives you the associated tools, also has a few chapters on the details of the tools. About the Author: Stephen Boyd received his PhD from the University of California, Berkeley.

For teachers of convex optimization this book can be a gold mine of exercises. I recommend it as one of the best optimization textbooks that have appeared in the last years. Most books I have seen on linear programming or non-linear programming tackle a few standard problems, introduce what is necessary in terms of definitions and proofs, and then focus on the algorithms that solve these standard problems conjugate gradient et. It was published by Cambridge University Press and has a total of 727 pages in the book. This material is somewhat tangential to my research, but I learned a ton by reading it. A perfect balance on the theoretical and practical aspets of the convex optimization. This particular edition is in a Hardcover format.

Brand New Paperback International Edition. Convex optimization problems arise frequently in many different fields. 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. It's simple with many examples and figures. A local convergence analysis shows that the algorithm is superlinearly convergent under a local error bound condition.

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. The focus of the book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. We present a regularization algorithm to solve a smooth unconstrained minimization problem. There is no need for the starting vector x 1 to be close to the solution. You will also end up knowing what to do when your problem is not convex.

A comprehensive introduction to the subject, this book shows in detail how such problems can be solved numerically with great efficiency. Provides necessary mathematical background in the first part---not as deeply as a gradute level convex analysis book---and therefore helps reader build a working knowledge. Excellent choice for engineers, mathematicians might find it incomplete, but what can we do, that's life. 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. Basic calculus may also be useful. The focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them.

Book Description: Convex optimization problems arise frequently in many different fields. 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. B69 2004 Dewey Decimal 519. Perfect for self-study as well as classroom use. In sum, all things considered, a great text. If something is not covered in this part but essential for a working knowledge, then it is in the appendices for sure.

Numerical results show that the algorithm is very efficient and robust. My reasoning in giving it such praise is my preference for the rather unusual methodology it takes in introducing you to optimization. In order that the approximation is useful, the change in x made by each iteration is subject to a bound that is also revised automatically. Provides a wealth of examples, exercises, and applications. The book excels in readability and style. As the name implies, and also as the authors put in preface, it is about recognizing, formulating, and solving convex optimization problems. I bought the book after downloading it because it is worth its price.

I had the pdf of this book for years but was never able to really appreciate the book because this book is designed to be browsed. However, the proofs are very sloppy and overly complicated at best; if you're an engineer then you probably don't care about mathematical proofs but for me, and others like me, proofs reinforce concepts and reading good proofs is a great way to connecting different areas of mathematics. For teachers of convex optimization this book can be a gold mine of exercises. For example, here is a problem I was working on. However, if you are accompanying your study with the problems at the end of each chapter, you're certain to get practice and demystify the concepts. We show that if the gradient and Hessian of the objective function are Lipschitz continuous, then the method has a global convergence property. A comprehensive introduction to the subject, this book shows in detail how such problems can be solved numerically with great efficiency.