1,601 research outputs found
On the computational complexity of branch and bound search strategies
Many important problems in operations research, artificial intelligence, combinatorial algorithms, and other areas seem to require search in order to find an optimal solution. A branch and bound procedure, which imposes a tree structure on the search, is often the most efficient known means for solving these problems. While for some branch and bound algorithms a worst case complexity bound is known, the average case complexity is usually unknown despite the fact that it gives more information about the performance of the algorithm. In this dissertation the branch and bound method is discussed and a proabilistic model of its domain is given, namely a class of trees with an associated probability measure. The best bound first and depth-first search strategies are discusses and results on the expected time and space complexity of these strategies are presented and compared. The best-bound search strategy is shown to be optimal in both time and space. These results are illustrated by data from random traveling salesman problems. Evidence is presented which suggests that the asymmetric traveling salesman problem can be solved exactly in time O(nï¾³lnï¾²(n)) on thePrepared for: National Science Foundation; Washington, D.C. 20550http://archive.org/details/oncomputationalc00smitNSF Grant MCS74-14445-A0
Approximating ATSP by Relaxing Connectivity
The standard LP relaxation of the asymmetric traveling salesman problem has
been conjectured to have a constant integrality gap in the metric case. We
prove this conjecture when restricted to shortest path metrics of node-weighted
digraphs. Our arguments are constructive and give a constant factor
approximation algorithm for these metrics. We remark that the considered case
is more general than the directed analog of the special case of the symmetric
traveling salesman problem for which there were recent improvements on
Christofides' algorithm.
The main idea of our approach is to first consider an easier problem obtained
by significantly relaxing the general connectivity requirements into local
connectivity conditions. For this relaxed problem, it is quite easy to give an
algorithm with a guarantee of 3 on node-weighted shortest path metrics. More
surprisingly, we then show that any algorithm (irrespective of the metric) for
the relaxed problem can be turned into an algorithm for the asymmetric
traveling salesman problem by only losing a small constant factor in the
performance guarantee. This leaves open the intriguing task of designing a
"good" algorithm for the relaxed problem on general metrics.Comment: 25 pages, 2 figures, fixed some typos in previous versio
Lin-Kernighan Heuristic Adaptations for the Generalized Traveling Salesman Problem
The Lin-Kernighan heuristic is known to be one of the most successful
heuristics for the Traveling Salesman Problem (TSP). It has also proven its
efficiency in application to some other problems. In this paper we discuss
possible adaptations of TSP heuristics for the Generalized Traveling Salesman
Problem (GTSP) and focus on the case of the Lin-Kernighan algorithm. At first,
we provide an easy-to-understand description of the original Lin-Kernighan
heuristic. Then we propose several adaptations, both trivial and complicated.
Finally, we conduct a fair competition between all the variations of the
Lin-Kernighan adaptation and some other GTSP heuristics. It appears that our
adaptation of the Lin-Kernighan algorithm for the GTSP reproduces the success
of the original heuristic. Different variations of our adaptation outperform
all other heuristics in a wide range of trade-offs between solution quality and
running time, making Lin-Kernighan the state-of-the-art GTSP local search.Comment: 25 page
Phase Transitions and Backbones of the Asymmetric Traveling Salesman Problem
In recent years, there has been much interest in phase transitions of
combinatorial problems. Phase transitions have been successfully used to
analyze combinatorial optimization problems, characterize their typical-case
features and locate the hardest problem instances. In this paper, we study
phase transitions of the asymmetric Traveling Salesman Problem (ATSP), an
NP-hard combinatorial optimization problem that has many real-world
applications. Using random instances of up to 1,500 cities in which intercity
distances are uniformly distributed, we empirically show that many properties
of the problem, including the optimal tour cost and backbone size, experience
sharp transitions as the precision of intercity distances increases across a
critical value. Our experimental results on the costs of the ATSP tours and
assignment problem agree with the theoretical result that the asymptotic cost
of assignment problem is pi ^2 /6 the number of cities goes to infinity. In
addition, we show that the average computational cost of the well-known
branch-and-bound subtour elimination algorithm for the problem also exhibits a
thrashing behavior, transitioning from easy to difficult as the distance
precision increases. These results answer positively an open question regarding
the existence of phase transitions in the ATSP, and provide guidance on how
difficult ATSP problem instances should be generated
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