E. L. Lawler, J. K. Lenstra, A. H. G. Rinnooy Kan, D. B. Shmoys. The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization. Wiley, 1985. Home/Search Document Not in Database Summary Related Articles Check |
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Data Compression for System-on-Chip Testing using ATE - Karimi Meleis Navabi (Correct)
....(3) The objective is to nd a new ordering of the test set that minimizes the sum of the Hamming distances between consecutive vectors so that the size of the blocks can likely be increased. Reordering can be mapped to the problem of nding a minimal tour for the Traveling Salesman Problem (TSP) [12]. To perform the mapping between compression by reordering and the TSP, each vector in the set is associated to a node in the graph; the distance between each pair of nodes is given by the weight of the arc connecting the nodes (i.e. the weight is their Hamming distance) Then an utilitity ....
E.L. Lawler, A. H. Rinnooy-Kan, \The traveling salesman problem : a guided tour of combinatorial optimization," John Wiley & Sons, 1985.
Teaching Integer Programming Formulations - Using The Traveling (Correct)
....start. They can be generated as needed by a separation algorithm: one can start with the formulation (2.1) then generate subtour inequalities that are violated by the current LP solution. The separation algorithm for subtour constraints is based on network ow techniques; for further details, see [4]. 3. The comparison. 3.1. The strength of the two formulations. Solving a reasonably large (with at least, say 50 cities) problem to optimality is only possible using the subtour formulation at least we are not aware of any published computational studies that use the pure MTZ formulation. ....
E. Lawler, J. K. Lenstra, A. H. G. Rinnooy Kan, and D. B. Shmoys eds. The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization, Wiley, 1985
Genetic Algorithm Solution of the TSP Avoiding Specila Crossover.. - Ucoluk (Correct)
....in section 4 which is followed by the conclusion. 2 Conventional Approach The rst GA approach on TSP was by Brady [1] which was then followed by Grefenstette et al. 4] Goldberg and Linge [6] and Oliver et al. For a detailed discussion on TSP a good reference is the study of Lawler et al. [11]. A perfect review article on GA for TSP is by Larranaga et al. 10] In the conventional approach a chromosome which is devised to represent a solution constitutes of N (count of the cities) genes. Each gene holds a number which is a label of a city. So the n th gene holds the label of the city ....
Lawler, E.L., J.K. Lenstra, A.H.G. Rinnooy Kan, and D.B. Shmoys (Eds.). The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization. Wiley, Chichester, 1985.
The Symmetric Generalized Travelling Salesman Polytope - Fischetti, Jos (1995) (Correct)
....set of constraints. The best known problem of this type is the classical Travelling Salesman Problem (TSP) calling for a minimum cost Hamiltonian cycle on a given graph. This problem has been extensively studied in the last decades; see the book edited by Lawler, Lenstra, Rinnooy Kan, and Shmoys [10] for a comprehensive cover of the literature up to 1985. In several applications a feasible cycle is allowed to visit only a subset of the nodes of the graph, chosen according to some speci ed criterion. For example, in the Prize Collecting TSP each node has an associated prize, and a salesman ....
E.L. Lawler, J.K. Lenstra, A.H.G. Rinnooy Kan, D.B. Shmoys (Editors), The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization, Wiley, 1985.
Optimal Composition of Real-Time Systems - And (Correct)
....a large number of cities. Iterative improvement algorithms can find a good approximation to an optimal solution, and naturally yield an interruptible anytime algorithm. The anytime traveling salesman algorithm is a randomized algorithm that repeatedly tries to perform a tour improvement step [20, 22]. In the general case of tour improvement procedures, r edges in a feasible tour are exchanged for r edges not in that solution as long as the result remains a tour and the cost of that tour is less than the cost of the previous tour. The simplest case is when r = 2. Figure 5 demonstrates one step ....
E. L. Lawler et al., eds., The traveling salesman problem: a guided tour of combinatorial optimization, New York (Wiley, 1987).
The Moving-Target Traveling Salesman Problem - Helvig, Robins, Zelikovsky (Correct)
....with constant velocities. We propose approximate and exact algorithms for several natural variants of Moving Target TSP. 1 Introduction The classical Traveling Salesman Problem (TSP) has been studied extensively, and many TSP heuristics have been proposed over the years (see surveys such as [8] and [11] Previous works on TSP have assumed that the cities targets to be visited are stationary. However, several practical scenarios give rise to TSP instances where the targets to be visited are themselves in motion (e.g. when a supply ship resupplies patrolling boats, or when an aircraft ....
....as well as its quadratic runtime. 2. 2 Heuristics for Few Moving Targets In this subsection, we consider Moving Target TSP when only some of the targets are moving (while the majority are stationary) From among the many existing approximation algorithms for classical (stationary) TSP [8], choose one such heuristic, having performance bound ff. Using this algorithm for stationary targets, we now show how to construct an efficient algorithm having a performance bound of 1 ff when the number of moving targets is sufficiently small. Theorem 6 Moving Target TSP where at most O( ....
E. L. Lawler, J. K. Lenstra, A. H. G. Rinnooy, and D. B. Shmoys, The Traveling Salesman Problem: a Guided Tour of Combinatorial Optimization, John Wiley and Sons, Chichester, New York, 1985.
Operational Rationality through Compilation of Anytime Algorithms - Zilberstein (1993) (62 citations) (Correct)
....algorithm a b c d f e a b c d f e (b) b c d e f Figure 4.3: The operation of randomized tour improvement is described below. The anytime traveling salesman algorithm is a randomized algorithm that repeatedly tries to perform a tour improvement step [Lin and Kernighan, 1973; Lawler et al. 1987] In the general case of tour improvement procedures, edges in a feasible tour are exchanged for edges not in that solution as long as the result remains a tour and the cost of that tour is less than the cost of the previous tour. I have implemented the algorithm for the case where . ....
E. L. Lawler et al. (eds.). The traveling salesman problem: a guided tour of combinatorial optimization. New York: Wiley, 1987.
Generalized Network Design Problems - Feremans, Labbé, Laporte (2002) (Correct)
....The Minimum Spanning Tree Problem (MSTP) see e.g. Magnanti and Wolsey [45] The MSTP is to determine a minimum cost tree on G that includes all the vertices of V . This problem is polynomially solvable. 2. The Traveling Salesman Problem (TSP) see e.g. Lawler, Lenstra, Rinnooy Kan and Shmoys [42]) The TSP consists of finding a minimum cost cycle that passes through each vertex exactly once. This problem is NP hard. 3. The Minimum Perfect Matching Problem (MPMP) see e.g. Cook, Cunningham, Pulleyblank and Schrijver [8] A matching M E is a subset of edges such that each vertex of M ....
E.L. Lawler, J.K. Lenstra, A.H.G. Rinnooy Kan, D.B. Shmoys (Editors). The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization. Wiley, New York (1985).
An Effective Implementation of the Lin-Kernighan Traveling.. - Helsgaun (2000) (3 citations) (Correct)
....assumed to be known by the salesman. Distance can be replaced by another notion, such as time or money. In the following the term cost is used to represent any such notion. This problem, the traveling salesman problem (TSP) is one of the most widely studied problems in combinatorial optimization [2]. The problem is easy to state, but hard to solve. Mathematically, the problem may be stated as follows: Given a cost matrix C = c ij ) where c ij represents the cost of going from city i to city j, i, j = 1, n) find a permutation (i 1 , i 2 , i 3 , i n ) of the integers from 1 ....
....This approach is simple, but often too greedy. The first distances in the construction process are reasonable short, whereas the distances at the end of the process usually will be rather long. A lot of other construction algorithms have been developed to remedy this problem (see for example [2], 12] and [13] The tour improvement algorithms, however, have achieved the greatest success. A simple example of this type of algorithm is the so called 2 opt algorithm: Start with a given tour. Replace 2 links of the tour with 2 other links in such a way that the new tour length is shorter. ....
E. L. Lawler, J. K. Lenstra, A. H. G. Rinnooy Kan & D. B. Shmoys (eds.), The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization , Wiley, New York (1985).
Adaptive Scheduling of Master/Worker Applications on Distributed.. - Shao (2001) (10 citations) (Correct)
....ranged widely from 7.7 to 93 seconds running on test environment workstations. 5.2. 6 Genetic algorithm for solving a traveling salesman problem The ga tsp application is an implementation of a distributed genetic algorithm [70] approach to solving the well known traveling salesman problem (TSP) [51]. In a genetic algorithm, a population of candidate solutions to a problem are encoded as a sequence of characters, much like a genetic sequence. These sequences are then adapted in stages called generations to produce new and hopefully better solutions in each passing generation. For each new ....
Lawler, E. L., Lenstra, J. K., Rinnooy-Kan, A. H. G., and Shmoys, D. B., Eds. The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization. John Wiley and Sons, Chichester, West Sussex; New York, New York, Sept. 1985.
Structure and Applications of Totally Decomposable Metrics - Christopher (1997) (Correct)
....algorithm exists for 3 solving any NP complete problem, many algorithms have been developed to solve special cases of NP complete problems. For example many algorithms exist for solving special cases of the traveling salesman problem. For an overview of these special cases, see Lawler et al. [30] or Burkard et al. 18] Researchers also design algorithms that exploit the structure of a metric to speed up polynomial time algorithms. For example, consider the development of single source shortest path algorithms. The Bellman Ford algorithm developed from Bellman s 1958 paper [12] This ....
E.L. Lawler, J.K. Lenstra, A.H.G. Rinooy Kan, and D.B. Shmoys, The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization, Wiley (1985).
Improved Large-Step Markov Chain Variants for the Symmetric TSP - Inki Hong Andrew (1995) (6 citations) (Correct)
....of all inter city distances, the symmetric traveling salesman problem (TSP) seeks a shortest tour which visits each city exactly once. The symmetric TSP is NP hard [10] and has been extensively studied both in terms of its combinatorial structure and as a testbed for exact and heuristic methods [14] [13] Studies such as [12] point to greedy local search (e.g. using the fast 3 Opt [5] or the Lin Kernighan (LK) 15] neighborhood structure) as the most effective approach for practical instances. Over the past decade, iterated descent [3] 4] has been shown to be an effective means of applying ....
E. L. Lawler, J. K. Lenstra, A. Rinnooy-Kan, and D. Shmoys. The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization. Wiley, Chichester, 1985.
The Mathematics of Algorithm Design - Kleinberg (Correct)
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E. L. Lawler, J. K. Lenstra, A. H. G. Rinnooy Kan, D. B. Shmoys. The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization. Wiley, 1985.
On The Maximum Scatter Tsp - To Appear In (Correct)
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E.L. Lawler, J.K. Lenstra, A.H.G. Rinooy Kan, and D.B. Shmoys (eds.). The traveling salesman problem: A guided tour of combinatorial optimization. Wiley, New York, 1985.
DOI: 10.1007/s00453-004-1124-z - Algorithmica Of Of (Correct)
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E.L. Lawler, J.K. Lenstra, A.H.G. Rinooy Kan, and D.B. Shmoys (eds.). The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization. Wiley, New York, 1985.
On Providing ATM Multipoint Services Survivability - Yurcik (1998) (Correct)
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E.L. Lawler, J.K. Lenstra, A.H.G. Rinnooy Kan, and D.B. Shmoys (editors), The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization, John Wiley and Sons, New York 1985.
Generally Applicable Heuristics for Global Optimisation: An.. - Telfar (1994) (3 citations) (Correct)
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E. L. Lawler, J. K. Lenstra, A. H. G. Rinnooy Kan, and D. B. Shmoys, editors. The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization. John Wiley & Sons, 1985.
Mobile Element Scheduling for Efficient Data.. - Somasundara.. (2004) (Correct)
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E. L. Lawler, J. K. Lenstra, R.-K. A. H. G., and D. B. Shmoys. Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization. John Wiley & Sons, 1990.
Algorithms for Distributed and Mobile Sensing - Isler (2004) (Correct)
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E. Lawler, J. Lenstra, A. Rinnooy Kan, and D. Shmoys. The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization. Wiley, 1985. 174
Memetic Algorithms for Combinatorial Optimization Problems.. - Merz (2001) (8 citations) (Correct)
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E. L. Lawler, J. K. Lenstra, A. H. G. Rinnooy Kan, and D. B. Shmoys, The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization. New York: Wiley and Sons, 1985.
Job Grouping with Minimum Setup in PCB Assembly - Jouni (2003) (Correct)
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E. L. Lawler, J. K. Lenstra, A. H. G. Rinnooy Kan, and D. B. Shmoys, The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization, John Wiley & Sons, New York, NY, 1985.
Recognizing Finite Repetitive Scheduling Patterns.. - Hendriks, van den .. (Correct)
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E.L. Lawler, J.K. Lenstra, A.H.G. Rinnooy Kan, and D.B. (eds.) Shmoys. The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization. Wiley, New York, 1985.
Faster Decomposition of Totally Decomposable Metrics with.. - Christopher, Trick (1996) (Correct)
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E.L. Lawler, J.K. Lenstra, A.H.G. Rinooy Kan, and D.B. Shmoys, The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization, Wiley (1985).
Parallel Algorithm Design for Workstation Clusters - Magee, Cheung (1991) (2 citations) (Correct)
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E. L. Lawler, J. K. Lenstra, A. H. G. Rinnooy Kan and D. B. Shmoys, The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization, Wiley, 1985.
Branch, Cut, and Price: Sequential and Parallel - Ralphs, Ladanyi, Trotter, Jr. (Correct)
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Lawler, E.L., Lenstra, J.K., Rinnooy Kan, A.H.G., and Shmoys, D.B.: The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization. Wiley, New York, 1985
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