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Isbn: 0072417412
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Isbn: 0131406388
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Isbn: 0534209718
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Isbn: 0130323748
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Isbn: 0072535105
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Isbn: 0804743991
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Isbn: 0387982175
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Isbn: 013221797X
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Reviews (9)

extremely well written
I read this book while taking an advanced class in heuristics. I found the book to be extremely well written and very compelling to read. Although dealing with advanced topics, the authors' friendly and clear writing style makes it accessible to anyone with a CS background.

The first half of the book is on search heuristics, covering methods such as traditional searches (exhaustive search, greedy algorithms, divide and conquer, dynamic programming, A*, etc), methods to escape local optima (simulated annealing, tabu search), and, perhaps most interesting of all, evolutionary algorithms. I later found out that these topics are typically taught in undergraduate artificial intelligence courses, an elective I never took. The second half of the book covers even more advanced areas, such as contraint-handling, neural networks, and fuzzy systems.

The authors use three recurring example applications to demonstrate each search technique: the boolean satisfiability problem (SAT), travelling salesman (TSP), and a nonlinear programming problem (NLP). I really liked the consistent use of these three examples, as they give a sense of continuity throughout the book that helps the reader compare search techniques clearly. I had of course studied the TSP problem in my undergraduate algorithms class but neverin the context of such interesting approximation algorithms. In my heuristics class we had assignments to implement the TSP search problem using the Lin-Kernighan method, dynamic programming, and an evolutionary algorithm.

The written English in this book is simply outstanding and crystal-clear, which was something of a shock since I was unable to even pronounce the first author's name. The writing is in a very friendly tone with elements of humour dispersed throughout. Interestingly, in the summary chapter, there is an anecdote on the 1980s TV show Magnum PI (I even remember the mentioned scene myself), further revealing the friendly, plain-English tone of the book. Perhaps the best part of the book is that the numerical mathematical discourse is kept at a minimum (used largely for the NLP problems), so people who haven't taken calculus in ages (like me) can easily enjoy the book.

As an added bonus(!), between each chapter is a brain-teaser problem like those found in those legendary Microsoft interview questions.

My only complaint is that there is no simple analysis of the running time complexity of each algorithm, which even in its simplest form would have been a great thing to read about.

In summary, this book is an excellent read if you enjoy the topics covered. Highly recommended.

Fine-tuning to common sense
Beside the great ideas provided in this book for problem solving, it provides a deep wisdom of for piecing some of the puzzles of our life. I recommend it.

Useful overview of methods
I first ordered this book thinking it was George Polya 's book "How to solve it", then I realized it wasn't and I bought it anyway since I thought it might turn out as a "must read" book, just like Polys'a book.

One one hand it was a dissapointment, because the books are not written in the same manner and don't attact similar problelsm.

But then, this book makes you look into problems, and realize that usually we people are usually good in solving problems of the sort we learned how to (well... duh!), but surprisingly, we have a hard time solving even trivial problems if they are not placed in the context we got used to seeing them.

This book comes and tries to make things better in this department, showing you some general methods for solving problems, and also showing problems and suggested solutions along with a long discussion.

You should be able, once you've read the book and put your mind to it, to be better in understanding problems, understanding which tool to use for solving them and finally, understanding the tools enough to be able to actually solve the problem.

I enjoyed the overview of methods, and there are many such methods throughout the book (perhaps a complementary book for learning which "machine learning" methods are available these days and what sorts of problems they are useful for solving would be Tom Mitchell's "Machine Learning" book).

Reviews (2)

A great sequel to Garey and Johnson
This book is a great sequel to Garey and Johnson. The appendix of this book gives a list of all NP optimisation problems together with their current approximability (or inapproximability results) in a Garey Johnson fashion.

Developing approximation algorithms for NP hard problems is now a very active field in Mathematical Programming and Theoretical Computer Science. There have been a number of exciting developments like semidefinite programming , the Goemans Williamson algorithm for max cut et al.

On the other hand, from a theoretical computer science point of view, we now have a proof that many of these problems cannot have polynomial approximation algorithms unless P=NP.

This book provides an excellent introduction to both areas. A worthy supplement to Garey and Johnson,Papadimitriou's books on combinatorial optimisation and computational complexity, Hochbaum's book on approximation algorithms, Alon and Spencer's book on the probabilistic method and finally Motwani and Raghavan's book on randomised algorithms.

A great sequel to Garey and Johnson
This book is a great sequel to Garey and Johnson. The appendix of this book gives a list of all NP optimisation problems together with their current approximability (or inapproximability results) in a Garey Johnson fashion.

Developing approximation algorithms for NP hard problems is now a very active field in Mathematical Programming and Theoretical Computer Science. There have been a number of exciting developments like semidefinite programming , the Goemans Williamson algorithm for max cut et al.

On the other hand, from a theoretical computer science point of view, we now have a proof that many of these problems cannot have polynomial approximation algorithms unless P=NP.

Editorial Review

This book's introduction features a humorous story of a man with a line of people behind him, who explains to his boss, "I can't find an efficient algorithm, but neither can all these famous people." This man illustrates an important quality of a class of problems, namely, the NP-complete problems: if you can prove that a problem is in this class, then it has no known polynomial-time solution that is guaranteed to work in general. This quality implies that the problem is difficult to deal with in practice.

Reviews (2)

comprehensive and clear
This book is highly-recommended for upper-level undergraduate/first-year graduate students in economics.It seems that the authors know well the mathematical requirements needed for students majoring in economics.The writing is clear and the proofs are rigorously explained.Even the answers to selected problems are explained thoroughly. The problems are quite easy though, but I think it is a very good way of building confidence for students who are not so mathematically-inclined. For students who find Chiang's textbook outdated and Simon/Blume's textbook somewhat incomplete, this is a much better alternative.

Easy and Complete. The best for upper/intermediate.
I bought this book to review some maths and get ready for a master in economics. It was very useful. The topics are explained in a very accessible way.

There are a lot of examples and solved exercises thattourned to be useful for an advanced course in Microeconomics and/orMacroeconomics.

Reviews (2)

Nice book!
This book is really easy to understand. Its language is so simple. Anyone can read and understand it so well, even if your first language is not English. It gives nice examples with details to explain the solutions of the problems. It's about 19 chapters and they are: 0. Algebra Refresher. 1. Equations. 2. Applications of Equations and Inequalities. 3. Functions and Graphs. 4. Line, Parbolas, and Systems. 5. Exponential and Logarthimic Functions. 6. Matrix Algebra. 7. Linear Programming. 8. Mathematics of Finance. 9. Introduction of Probability and Statistics. 10. Additional Topics in Probablity. 11. Limits and Continuity. 12. Differentiation. 13. Additional Differentiation Topics 14. Curve Sketching. 15. Applications of Differentiation 16. Integration. 17. Methods of Applications of Integration. 18. Continuous Random Variables. 19. Multivariable Calculus. I recommend this book for students or readers for business because it teaches the basics of the topics (above)

Isbn: 0471322032
Sales Rank: 795263
Subjects:  1. Business & Economics    2. Business / Economics / Finance    3. Business Mathematics    4. Computer Books: Spreadsheets    5. Finite Mathematics    6. General    7. Management - General    8. Mathematics    9. Problems, exercises, etc    10. Social Sciences Research    11. Social sciences    12. Mathematics / Finite Mathematics    13. Mathematics for scientists & engineers   

Reviews (9)

An excellent book for practioner and truely advanced learner
MBA students alike may not even have the patient to read through it. But, as a management consultant in China ( i used to work for 2 of big 4 accounting firms, and now working as a freelance to help local companies to solve their managerial costing problems), this book is my primary reference for my work. I applied ideas in this book helping my clients building ABC and transfer pricing and costing control models. When you really need to solve true business problems (rather than reading in the school), you will find this is "the" book. In my view, you have to read Kaplan and Cooper's staff if you want to get some true knowledge about managerial accounting.

This book is not what I expected....
I have studied,practiced and taught managerial accounting for more than 17 years at work, using excellent supporting text and case books from recognized US authors. But to be honest Dr. Kaplan is making easy concepts to appear complicated and cumbersome. His case readings as well as the text materials are very length and hard to "digest" since it does not capture the attention of the reader.I would not recommend the book neither for and undergraduate or graduate degree, despite the well known and worldwide recognition of Dr. Kaplan. His book is far from being pedagogical in nature.

Reviews (1)

Reviews (25)

Verbose
I used this book for the Cornell Econ 301 course, a calculus based intro to microeconomics. The professor taught everything in lecture so that the book was used only as a supplement. I read part of the book and found that it is too verbose and repetitive. The author could have explained everything in less than half of the book. The exercises are not helpful. Moreover, this little book is sold for more than a 100 dollars. Not worth the money at all.

Okay
I think this book is okay but as many reviewers have said the math is a little lacking.While math is important to economics, it isn't necessarily for the math's sake that that is so.Incorporating both the math and specific examples utilizing math really clarifies the theory.I found myself confused about what microeconomic theory meant when there wasn't a lot of numerical examples to back it up.There are far better text books to get a really thorough grasp of microeconomic theory, but if you already have that fundamental grasp I think this one might refresh your memory.But I would recommend others if you are looking to learn the theory for the first time.