Editorial Review:
This book is specifically designed to change the way deterministic optimization is taught to introductory students. Toward this end, it exposes students to the broad scope of the topic, reinforces the basic principles, sparks students' enthusiasm about the field, provides tools of immediate relevance and develops the skills necessary to use those tools.
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Customer Reviews
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Book Contents  2006-11-02
The "Search inside this book" feature was not available for this book when this review was published. Hope it helps.
Table of Contents
1. Problem Solving with Mathematical Models
2. Deterministic Optimization Models in Operations Research
3. Improving Search
4. Linear Programming Models
5. Simplex Search for Linear Programming
6. Interior Point Methods for Linear Programming
7. Duality and Sensitivity in Linear Programming
9. Shortest Path and Discrete Dynamic Programming
10. Network Flows
11. Discrete Optimization Models
12. Discrete Optimization Methods
13. Unconstrained Nonlinear Programming
14. Constrained Nonlinear Programming
If you need more information, Professor Rardin (Purdue University) maintains a website that can be easily located using any web search tool.
A Clear and Concise Text for OR 2006-09-17
Rardin was the first book I used for OR and I keep a copy in my personal library. It offers a series of examples that are followed up throughout the book, chapter by chapter, to provide insight into the application of mathematics to real world problems. By building the level of complexity, on an ogoing basis through the use of specific examples, Rardin shows the extrmely practical side to why Operations Research is such a fundamental use of applied mathematics. The book is easy to read and should easily meet the needs of any upperlevel undergraduate course in Operations Research.
Good operations research book 2005-06-10
This book presents the subjects in a different and novel way which provides many new insights.
In it, there is a great concern with the practical, professional use of operations research, as can be easily seen in the modeling examples. This book could be named "Optimization theory with realistic applications". This book certainly enables the students to apply the theory learned in practical situations, while providing the necessary mathematical foundations.
Rardin exposes the subject in a very clear and non-orthodox manner, unifying all algorithms through the use of the improving-search framework. The text is also innovative, containing sections on Genetic Algorithms, Simulated Annealing, Tabu Search and Branch and Cut.
But if you want to go deeper in some subject (linear programming for example) you will need another book.
Master piece 2005-05-16
It is both useful for graduates and for undergraduates.
Explanations are easy to follow but at the same time they don't lack detail or correctness. The book is full of examples and it covers different fields of OR.
For me, the best is Rardin's approach to teach OR: he begins from the base and he builds newer contents over that base. In this way, you feel like "that works!". And for graduates, there are some sections called "primers" where Rardin explains subjects outside the scope of the book, but very useful for beginners.
The book is very well written. A good big effort.
The only bad point I found is the book's font/typeset is not very good (I'd prefer a more TeXified style).
PhD student in IE 2004-03-15
Review after 2 years of using this book: AMAZING BOOK. There has never been a better book (and probably never will be) in explaining OR.
Previous Review upon purchase:
If you are taking a graduate or an undergraduate course in OR, this book is a must! I have not seen ANY book able to present OR with such simple, direct examples and WITHOUT sacrificing theory.
This is the best written textbook I have ever read. When I compare it with the hundereds of dollars I spend on badly written books, even as a PG (poor graduate) student I would gladly pay twice of what this book is priced at.
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