How to Solve It: Modern Heuristics

Rating:
Summary: Fine-tuning to common sense
Review: 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.

Rating:
Summary: Zen and the art of problem solving.
Review: Don't think that this book is just another version of numerical recipes or "how to" for optimization methods. For me it is about something absolutely different. About breaking old, bad habits in problem solving and looking for the simplest and the most elegant solutions for the given problem. Sometimes it will be something complicated, like competitive neural network, but sometimes the solution will be just: "let's assume that there's no river" (see page 185 of this book). Don't put artificial intelligence where just the common sense will be absolutely enough. I remember some of the problems presented there from my high school years. I had more problems with solving them today than it was many years ago. It looks that we are loosing somewhere, in the process of education, the possibility to simplify problems and rather try to solve them by "brute force". This book may give you this fresh look again (I hope).

Rating:
Summary: Makes spinach taste good
Review: I am a computer scientist, but have gotten impatient over the years with the needless formalization that occurs in algorithmic texts. This is a delightful breath of fresh air in terms of balancing erudition with attempts to be "user friendly". If you want the latest and greatest twist to a well known technique, this book won't provide it. But it does a great job of competently and lucidly explaining the value proposition behind each optimization method and how to gradually upgrade from applying it naively to the more intricately optimized applications. Well done!

Rating:
Summary: Useful overview of methods
Review: 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).

I wasn't sorry for buying this book. I'm happy I was fortunate enough to bump into it.

Rating:
Summary: Useful overview of methods
Review: 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).

I wasn't sorry for buying this book. I'm happy I was fortunate enough to bump into it.

Rating:
Summary: extremely well written
Review: 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 never in 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.


Rating:
Summary: things that make you go hmm...
Review: READ: this is not just another optimization book! Instead of spoon-feeding one technique after another (do a search on "optimization" and you will know what i mean), it challenges you to think CREATIVELY. It says, "if you have a hammer, everything looks like a nail." Read and find out why the more textbooks you read, the more a screw looks like a nail! (and remedy to return to reality)

Despite working on algorithms for years in graduate school, for the first time there is a book that looks at problem solving with a fresh, unbiased perspective. Definitely my best buy in years.

Rating:
Summary: Must buy...
Review: This book provides a very accessible and contemporary treatment of optimization. Of particular interest is the problem solving orientation of the book as opposed to a tool-based approach to optimization and heuristics. The writing style of the book makes the book very interesting and readable - a rare thing to say about technical books! I used this book in a Master's class on Heuristics (Systems Engineering, University of Virginia) and received the most positive textbook reviews I have seen in my fifteen years of teaching. The book is an excellent choice for a course on heuristics, mathematical modeling, optimization, etc., and could be used in an advanced undergraduate class or a graduate class. In addition, the book is ideal for practitioners who may not have had exposure to modern heuristics in their education or practice, or those who want to get updated on the latest developments in the field.

Rating:
Summary: A comprehensive overview of problem solving techniques
Review: This book provides one of the most comprehensive views of modern techniques in problem solving. The authors use a number of classic problems to illustrate conventional heuristics as well as giving you a solid and working knowledge of more modern evolutionary techniques. The appendicies provide a good introduction to background information on probability theory and statistics used throughout the book, as well as projects for further exploration. Scattered throughout the text are complete and up-to-date references that can be used by the reader to delve deeper into certain topic areas. This book is written to be read and understood by both students and experienced researchers in the field.