The Traveling Salesman Problem: A Computational Study by William J. Cook, David L. Applegate, Robert E. Bixby, Vasek

By William J. Cook, David L. Applegate, Robert E. Bixby, Vasek Chvátal

This ebook provides the newest findings on probably the most intensely investigated matters in computational mathematics--the touring salesman challenge. It sounds basic adequate: given a collection of towns and the price of trip among every one pair of them, the matter demanding situations you in finding the most affordable course wherein to go to the entire towns and go back domestic to the place you begun. although probably modest, this workout has encouraged experiences by way of mathematicians, chemists, and physicists. academics use it within the school room. It has functional purposes in genetics, telecommunications, and neuroscience.

The authors of this e-book are an identical pioneers who for almost 20 years have led the research into the touring salesman challenge. they've got derived options to just about eighty-six thousand towns, but a basic technique to the matter has but to be came upon. the following they describe the strategy and laptop code they used to resolve a large diversity of large-scale difficulties, and alongside the best way they exhibit the interaction of utilized arithmetic with more and more strong computing structures. in addition they supply the interesting heritage of the problem--how it constructed, and why it maintains to intrigue us.

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Extra resources for The Traveling Salesman Problem: A Computational Study

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30. The Mona Lisa TSP has 10,000 cities and the Colosseum TSP has 11,999 cities. In each case the drawn tour was computed with Concorde, using a heuristic search module (the tours are most likely not optimal). C HALLENGE P ROBLEMS AND THE TSPLIB Geometric examples have long been the primary source of challenge problems for TSP researchers. The example solved in 1954 by Dantzig, Fulkerson, and Johnson [151] consists of finding a tour through 49 cities in the United States. This began a tradition of creating instances by selecting sets of actual cities and defining the cost of travel as the distance between the city locations.

In our example, a simple bound is the sum of the travel costs for all edges that we have insisted be in the tours; much better bounds are available, but we do not want to go into the details here. The purpose of the bounding step is to attempt to avoid a fruitless search of a subproblem that contains no solution better than those we have already discovered. The idea is that if the bound is greater than or equal to the cost of a tour we have already found, then we can discard the subproblem without any danger of missing a better tour.

So Menger observed that it is possible to solve the TSP by simply checking each tour, one after another, and choosing the cheapest. He immediately calls for better solution methods, however, not being satisfied with a technique that is finite but clearly impractical. The notion of a “better than finite” solution method is a subtle concept and is not considered in the usual settings of classical mathematics. Hints of the subject appear in other TSP works, including the following statement in Flood’s 1956 paper [182].

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