Customer Reviews

A First Course in Optimization Theory

5 star:		(8)
4 star:		(3)
3 star:		(1)
2 star:		(1)
1 star:		(0)

 

 

This book gives a nice introduction to the theory of optimization from a purely mathematical standpoint. The computational and algorithmic aspects of the subject are not treated, with emphasis instead placed on existencetheorems for various optimization problems. The author does an effective job of detailing the mathematical formalism needed in optimization theory. After a brief review of background mathematics in the first chapter, the author outlines the objectives of optimization theory in Chapter Two. He also gives some examples of optimization problems, such as utility maximization, expenditure minimization, profit maximization, cost minimization, and portfolio choice. All of these examples are extremely important in industrial, logistical, and financial applications. The author is also careful in this chapter to outline his intentions in later chapters, namely, that of finding the existence of solutions to optimization problems, and also in the characterization of the set of optimal points. The existence question is outlined in Chapter Three using only elementary calculus, and the Weierstrass theorem is proved. Necessary conditions for unconstrained optima are examined in the next chapter, again using only elementary calculus and linear algebra. Lagrange multipliers and how they are used in constrained optimization problems are effectively discussed in Chapter 5. To discuss how optimization problems vary with a set of parameters, in particular if they vary continuously with the set of parameters, the author introduces the concept of a corespondence. This is essentially a map that assigns sets to points. His discussion of upper and lower-semicontinuity is very clear and I think one of the best presentations given at this level. He then proves a maximum theorem, showing that parametrized optimization problems can have continuous solutions under certain conditions. A game-theoretic application follows along with statements, but not proofs, of the Kakutani and Brouwer Fixed Point theorems. The author introduces an order relation on the parameter space and discusses parametric monotonicity in the next chapter. Again a game theory application is given along with a statement (but not a proof) of the Tarski Fixed Point theorem. The last two chapters cover dynamic programming and these are the most interesting chapters of the book. It is here that the author makes the connection with more advanced treatments of optimization theory, via Banach spaces and nonlinear functional analysis. With further reading in real analysis and topology, readers will be well on their way to understanding more advanced treatments of optimization theory that use nonlinear functional analysis and differential topology.

This book was organized and written with perfection. The explanations are remarkable and the "cookbook" procedures for Lagrange and K-T methods were great. I especially admired the fact that the author actually mentioned how these procedures could fail to yield an optimized value. This is worthwhile in today's university mathematics where one is simply taught to plug numbers into formulae and algorithms to get the desired answer. The book also slants towards optimization problems in economic theory as well as other disciplines. Finally, in an age when textbooks can easily run over $100, it was nice to see this book, filled with a wealth of information, so moderately priced.

This is an excellent book for anybody interested in non-linear optimization within economics framework. The book is self-contained and includes all the basic theory one needs to know to understand optimization. To my knowledge, this is the only book merging non-linear optimization with game theory and such concepts as supermodularity and parametric monotonicity.

I'm a applied mathematician with over 40 quarter hours of theoretical math under my belt, and frankly I feel this book would be rough going for anyone who does not have a rigid math theory background. In other words, if you're not a graduate student or a theoretical practioner in the field of optimization, this is NOT the book for you (most likely). But I also have two other problems with this book.

First, it is touted to have numerious examples of both theory and applications. Theory, as I mentioned above, it has in abundance. But it is very thin on practical applications.

Second, this book has numerious problems at the ends of the chapters WITH NONE OF THEM WORKED OUT! Frankly, I'm not really interested in paying almost $30 for a paperback book that is unfinished.

Perhaps I was expecting much more than what I got after reading the glowing reviews above; and in hindsight, I really should have paid more attention to the title as "Theory" is indeed the operative word. My irritation is not in the book itself, as the author states in his forward that he is writing a book aimed the graduate school set; but is aimed at the reviewers above which led me to think that this text was much wider based than it turned out to be.

A first course in Optimization theory - that is what the book is. The target audience is those who are inetersted in the theory of optimization. Some familiarity with Mathematical Analysis and Matrix Algebra would be helpful; however the first chapter lays the mathematical foundation and a careful reading would enable the reader to tackle the rest of the book.

Previous reviews have made a chapter by chapter analysis of the book and hence I will just highlight some of the things I liked about the approach used by the author. Whenever a theorem is stated different examples are given to emphasize the points. For example when stating the Lagrange Theorem and Kuhn-Tucker theorem the author points out when the theorems fail and gives detailed examples to illustrate the ideas. The author often draws from examples in finance to illustrate the practical importance of the theory. The one I liked most was how a cost minimization problem was solved by reducing the solution space to a compact space and then applying the Weierstrass theorem. The author also shows how some of the "cookbook" procedures really work and warns the readers against potential pitfalls in applying such procedures. If you are planning to study optimization theory and are looking for a good entry point into the subject this book is for you.

glaring mistakes all over the book.
i've found at least 3 wrong definition of convexity in the book. some contradicing within a paragraph. wow do some proof reading?
along with wrong definition for implicit function theorem in chapter 1 - missing minus sign in front.
and look at page where they talk about epigraph and subgraph. and ... you get the point.

i can't believe it has such basic definitions wrong.
not to mention uncommon notations used for some analysis...
not happy with the book.
for optimization stick with Stephen Boyd, Bertsekas, or Luenberger.
and for applying optimization tech to economics get simon, and blume

2 stars might not seem a big deal to you but look at my rating history.
i rarely go under 4 stars.

stay away from this book. although if the author fixes those deluge of stupid mathematical mistakes this book has a potential to be 5 stars.

Excellent introduction to optimization techniques with a special emphasis to induce the student to an active and positive attitude towards the rigoruous demonstration of every proposition behind theorems and economic models.
This is not a book for beginners, but an excellent one that helps to develop the abilities required to understand modern textbooks and papers on micro and macroeconomics.
With an excellent presentation, and interesting end-of-chapter exercises, this book cannot be out of every economist's toolbox.

I am a student at Penn econ. The book fits the requirement of the department very well. It is a nice treatment of the topic, both on the theoretical and applied sides. However, there're a considerable amount of typos in it. For instance, the statement of the Lagrange's Theorem takes the function g(i) to R(k), which should be R(1), etc.. So be careful. Mine is 15th printing version, but no revisions so far.. I think the press has earned enough from the book and should consider a second edition, right? haha..

If you are studying Economics or Applied Mathematics, especially in Operations and Information Management, this book is an great overview of what you should master in order to engage interesting problems.

If you're a graduate student in economics, or perhaps computer science, buy this book as soon as possible. It will make your life much, much easier. Lagrangeans and Kuhn-Tucker optimization are the bread and butter of microeconomics, and yet few professors will bother to go into them in detail. This book succeeds in making these abstract mathematical procedures feel tangible and intuitive, defining them rigorously, explaining their usefulness and providing examples. This is one of very few books that I keep on my shelf for reference.

For a broader (and equally necessary) introduction to the math that you'll need for advanced study in economics, I recommend Simon and Blume's Mathematics for Economists.