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Constrained TSP and low-power computing

  • Session 4B: Invited Lecture
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Algorithms and Data Structures (WADS 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1272))

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Abstract

In the precedence-constrained traveling salesman problem (PTSP) we are given a partial order on n nodes, each of which is labeled by one of k points in a metric space. We are to find a visit order consistent with the precedence constraints that minimizes the total cost of the corresponding path in the metric space. We give negative results on approximability by relating the problem to the Shortest Common Supersequence problem, helping to explain why there has been very little success in approximation algorithms for this problem. We also give approximation algorithms for a number of special cases, included cases appropriate for a problem in low-power computing; in the process, we show that algorithms for the k-server problem and the traveling salesman problem can be used to derive approximation algorithms for the PTSP. We give tight bounds on the approximation ratios achieved by natural classes of algorithms for this optimization problem (which include algorithms proposed and used in empirical studies of this problem). We briefly summarize results of experiments with several algorithms on a standard set of compiler benchmarks, comparing several known and new algorithms.

Supported by Stanford School of Engineering Groswith Fellowship, an ARO MURI Grant DAAH04-96-1-0007 and NSF Award CCR-9357849, with matching funds from IBM, Schlumberger Foundation, Shell Foundation, and Xerox Corporation.

Supported by an Alfred P. Sloan Research Fellowship, an IBM Faculty Partnership Award, an ARO MURI Grant DAAH04-96-1-0007, and NSF Young Investigator Award CCR-9357849, with matching funds from IBM, Mitsubishi, Schlumberger Foundation, Shell Foundation, and Xerox Corporation.

Supported by the Department of Defense, with partial support from ARO MURI Grant DAAH04-96-1-0007 and NSF Award CCR-9357849, with matching funds from IBM, Schlumberger Foundation, Shell Foundation, and Xerox Corporation.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Stanford University, 94305, Stanford, CA, USA

    Moses Charikar, Rajeev Motwani & Craig Silverstein

  2. IBM Almaden Research Center, 650 Harry Road, 95120-6099, San Jose, CA, USA

    Prabhakar Raghavan

Authors
  1. Moses Charikar
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  2. Rajeev Motwani
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  3. Prabhakar Raghavan
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  4. Craig Silverstein
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Editor information

Frank Dehne Andrew Rau-Chaplin Jörg-Rüdiger Sack Roberto Tamassia

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© 1997 Springer-Verlag Berlin Heidelberg

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Charikar, M., Motwani, R., Raghavan, P., Silverstein, C. (1997). Constrained TSP and low-power computing. In: Dehne, F., Rau-Chaplin, A., Sack, JR., Tamassia, R. (eds) Algorithms and Data Structures. WADS 1997. Lecture Notes in Computer Science, vol 1272. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63307-3_51

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  • DOI: https://doi.org/10.1007/3-540-63307-3_51

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63307-5

  • Online ISBN: 978-3-540-69422-9

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