Introduction to Linear Optimization (Athena Scientific Series in Optimization and Neural Computation, 6)

Overall, the book is written fairly well and can be easy to follow. However, the book will frequently condense a rather in-depth set of steps into a single sentence, leaving you to figure it out. While not necessarily bad, this will force you to take potshots in the dark to understand something which can significantly extend the learning time of the material . For example, in section 4.1, (Duality Theory) it summarizes many aspects of dual equations without showing the math. As my math professors have always told me, "If the book doesn't show the math, regardless of how easy it may be, it isn't a good book."Also, the book will make the assumption that you have an unbelievable mastery of previous chapters when doing examples in a given chapter. This will most likely lead you to have to reference the previous chapters a lot. Most good textbooks will just do the math again to save the student the wasted energy but this one doesn't. Not the worst thing I've seen, but definitely something to keep in mind since it means that you'll probably be hit with a lot of time losses due to, "Crud, I don't remember that so I need to re-learn it to learn this new material." Last I checked, people tend to forget things after not looking at it for a couple of weeks so expect to lose a lot of time to this.The biggest negative in my opinion is the end of chapter problems are quite terrible. Simply put, the authors would prefer to expound on the concepts you learn as opposed to helping cement the critical material in front of you. I found that most students would attempt the problems and simply be confused since they would say, "This wasn't what was covered in the chapter." Problems are meant to first cement the concepts and, second expand on the material. At least for me, this resulted in me spending far more time trying to figure out what exactly the problem was getting at as opposed to spending the time developing a better understanding of the material.The second biggest negative in my opinion is the lack of solutions. I think it is fairly sad when what is regarded as the best book to learn from doesn't have problems with solutions to it. Sure, you can attempt the problems but you will never have any efficient way of checking whether you actually learned the material. Overall, I'm a little shocked that people would recommend a book with 4+ stars, when it doesn't offer solutions to problems, considering almost all math courses that I've ever taken have made it a point to offer solutions.As many people posted, the book is a little wordy but at the expense of just doing the math in front of you. This general presentation style is best for reference books, since you'll be able to read it and then go, "Oh yeah, that's how you do it" as opposed to going through the steps necessary to learn the material effectively.This book is okay. It's fairly well-written, covers a good amount of topics and does generally summarize things in an understandable way. However, it has a significant amount of flaws as I've listed above. I think it says more about how terrible textbooks are in general if a book that doesn't even offer solutions to problems is considered one of the best textbooks in a field.

The best part of this book is the first half, where the foundations of linear programming are presented in a clear yet relatively rigorous fashion, accompanied by numerous intuitive geometrical explanations of the abstract general concepts. This approach, supplementing mathematics with graphical insights, works extremely well for this topic.The quality goes down somewhat, perhaps neccessarily, in the latter half of the book as topics are presented less carefully, and in a somewhat rushed manner in order to cover all of the material the authors decided to include. Given that the fundamentals are covered so well, perhaps this is a fair trade.The only real negative I can think of is that it's a small crime for professors to create their own publishing companies (Athena only publishes works by a small group of MIT professors) and then still charge outrageous amounts for the books. This would be completely unacceptable were it not for the fact that, unlike most self-published work, this book's production quality is on par with that of the large publishers.

Este libro lo ocupo para estudiar

I cannot find better words to describe this book than simply amazing. It helped me build a deep understanding of linear optimization, from some basic linear algebra skills, through simplex and more modern linear optimization methods, to applications in network flows and integer programming, step by step, proof by proof, so I exactly knew what's going on all the time.I've gave a browse to a couple of other books and internet available courses before finding this one, but they were mostly touching the surface of the subject from a practitioner perspective or assumed a big amount of skills up front. This book allowed me to understand linear optimization deeply without prior preparation.The only disadvantage of this book I can think of is the price.

I very much like the style this book is written in. The proofs are clear and concise, while still leading the reader through the necessary steps with conversational tone. The text is organized well, and multiple viewpoints are given on most every topic to aid people who learn in different ways. I would recommend.

This is a very good introduction to linear programming. I use this a lot for my graduate research and is always the default text when I need to look something up. It only really lacks a discussion on problem reduction- i.e. identifying constraints which are redundant or variables that do not influence the solution which can be removed.But otherwise, it is very well written and provides great insight into how to visualize problems, applications, and building up algorithms (primal and dual simplex, interior point methods) for solving linear programs.

I don't think there's any other book to learn graduate level linear programming other than this book. Although if you notice that most graduate books always say "Introduction" this is a bit heavier for undergraduate introduction to linear programming/optimization. I would recommend: Introduction to Mathematical Programming: Applications and Algorithms, Volume 1 by Wayne L. Winston, Munirpallam Venkataramanan for an undergraduate introductory book, but if you are a bit more advanced undergraduate or graduate student, then this is the book to learn linear programming theory.

If you had to choose only one book on linear programming this is the book. Very well written and up to date. Contrast for example with books that are populated with why? and how? in the text forcing the teacher (or the student) to stop reading and try to solve the question. This book is direct, didactic, and spends 2 pages when the author thinks it is necessary instead of 1 paragraph and 1 why? (to the reader).The book covers linear programming theory and algorithms, network flows, an introduction to interior point methods and some modeling topics.

Very cool book

Este libro me salvo la vida en mi clase de Optimizacion. Es un tema duro, pero muy interesante. Tiene el rigor necesario para maestría en matemáticas. Me sirvió mucho en la sección de Degeneracy, basic solutions y un poco la parte de los costos. No he podido leerlo más a detalle para los demás temas (Interior point y más allá...) pero sin duda, valió la pena la compra.

Book in excellent condition!

good quality

Livro excelente, mas o pacote veio sem proteção alguma e com as quinas amassadas.