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| The Traveling Salesman Problem: A Computational Study (Princeton Series in Applied Mathematics) | 
| Authors: David L. Applegate, Robert E. Bixby, Vasek Chvatal, William J. Cook Publisher: Princeton University Press Category: Book
List Price: $46.95 Buy New: $31.95 You Save: $15.00 (32%)
New (21) from $31.95
Avg. Customer Rating: 1 reviews Sales Rank: 358716
Media: Hardcover Number Of Items: 1 Pages: 606 Shipping Weight (lbs): 2.1 Dimensions (in): 9.2 x 6.4 x 1.7
ISBN: 0691129932 Dewey Decimal Number: 511.6 EAN: 9780691129938 ASIN: 0691129932
Publication Date: January 15, 2007 Availability: Usually ships in 1-2 business days Condition: New, ship immediately
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| Editorial Reviews:
Product Description
This book presents the latest findings on one of the most intensely investigated subjects in computational mathematics--the traveling salesman problem. It sounds simple enough: given a set of cities and the cost of travel between each pair of them, the problem challenges you to find the cheapest route by which to visit all the cities and return home to where you began. Though seemingly modest, this exercise has inspired studies by mathematicians, chemists, and physicists. Teachers use it in the classroom. It has practical applications in genetics, telecommunications, and neuroscience. The authors of this book are the same pioneers who for nearly two decades have led the investigation into the traveling salesman problem. They have derived solutions to almost eighty-six thousand cities, yet a general solution to the problem has yet to be discovered. Here they describe the method and computer code they used to solve a broad range of large-scale problems, and along the way they demonstrate the interplay of applied mathematics with increasingly powerful computing platforms. They also give the fascinating history of the problem--how it developed, and why it continues to intrigue us.
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| Customer Reviews:
Twenty years in the making July 14, 2007 7 out of 7 found this review helpful
The coauthors have been working at solving large scale traveling saleman problem instances for more than 20 years. This book, along with the publicly available Concorde code, is the culmination of that twenty years of work.
The first four chapters of the book (130 pages or so) are an extremely readable description of the use and history of the traveling salesman problem. For our field, the traveling salesman problem has been an exemplar of a hard combinatorial problem, commonly used to test new ideas in problem solving. It is no coincidence that the first papers on simulated annealing, DNA computing, and other approaches for combinatorial problems describe their methods in the context of the TSP: it is the most well known of all the problems in operations research.
The authors' primary emphasis is on computation: how can optimal tours be found? The history of TSP computation is very much the history of computational combinatorial optimization. From the fundamental work of Dantzig, Fulkerson, and Johnson in solving the famous 42-city example, through Held and Karp's relaxations andLin-Kernighan's improvement heuristics, to modern-day branch-and-cut, the TSP has been at the forefront of computational methods in our field. The description of this history is outstanding, and appropriately nontechnical, suitable for reading by beginners in operations research.
The main part of the book is on the computational approaches needed to solve large TSPs. This part is well-written, though beyond a beginner level. Despite that, it has broad interest in the way it melds computational issues (like data structures and heuristic ordering) with the theory.
At the end, this book is an exemplar for how to do research in computational operations research. While not aimed at the non-specialist, it is perfectly readable by those who have gone through an introduction in optimization or operations research.
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