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Constrained TSP and low-power computing

  • Moses Charikar
  • Rajeev Motwani
  • Prabhakar Raghavan
  • Craig Silverstein
Session 4B: Invited Lecture
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1272)

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.

Keywords

Approximation Algorithm Regular Solution Basic Block Competitive Ratio Precedence Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Moses Charikar
    • 1
  • Rajeev Motwani
    • 1
  • Prabhakar Raghavan
    • 2
  • Craig Silverstein
    • 1
  1. 1.Department of Computer ScienceStanford UniversityStanfordUSA
  2. 2.IBM Almaden Research CenterSan JoseUSA

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