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Online Graph Exploration with Advice

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7355)

Abstract

We study the problem of exploring an unknown undirected graph with non-negative edge weights. Starting at a distinguished initial vertex s, an agent must visit every vertex of the graph and return to s. Upon visiting a node, the agent learns all incident edges, their weights and endpoints. The goal is to find a tour with minimal cost of traversed edges. This variant of the exploration problem has been introduced by Kalyanasundaram and Pruhs in [18] and is known as a fixed graph scenario. There have been recent advances by Megow, Mehlhorn, and Schweitzer ([19]), however the main question whether there exists a deterministic algorithm with constant competitive ratio (w.r.t. to offline algorithm knowing the graph) working on all graphs and with arbitrary edge weights remains open. In this paper we study this problem in the context of advice complexity, investigating the tradeoff between the amount of advice available to the deterministic agent, and the quality of the solution. We show that Ω(n logn) bits of advice are necessary to achieve a competitive ratio of 1 (w.r.t. an optimal algorithm knowing the graph topology). Furthermore, we give a deterministic algorithm which uses O(n) bits of advice and achieves a constant competitive ratio on any graph with arbitrary weights. Finally, going back to the original problem, we prove a lower bound of 5/2 − ε for deterministic algorithms working with no advice, improving the best previous lower bound of 2 − ε of Miyazaki, Morimoto, and Okabe from [20]. In this case, significantly more elaborate technique was needed to achieve the result.

Keywords

  • Minimum Span Tree
  • Travel Salesman Problem
  • Travel Salesman Problem
  • Competitive Ratio
  • Deterministic Algorithm

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.

E. Markou was supported in part by a research grant offered by the Ministries of Education of Greece and Slovakia (Bilateral Exchange Programme for Researchers) and by THALES: ALGONOW project funded by the Greek Ministry of Education.

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References

  1. Asahiro, Y., Miyano, E., Miyazaki, S., Yoshimuta, T.: Weighted nearest neighbor algorithms for the graph exploration problem on cycles. Information Processing Letters 110(3), 93–98 (2010)

    CrossRef  MathSciNet  MATH  Google Scholar 

  2. Böckenhauer, H.-J., Komm, D., Královič, R., Královič, R.: On the Advice Complexity of the k-Server Problem. In: Aceto, L., Henzinger, M., Sgall, J. (eds.) ICALP 2011, Part I. LNCS, vol. 6755, pp. 207–218. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  3. Böckenhauer, H.-J., Komm, D., Královič, R., Královič, R., Mömke, T.: On the Advice Complexity of Online Problems. In: Dong, Y., Du, D.-Z., Ibarra, O. (eds.) ISAAC 2009. LNCS, vol. 5878, pp. 331–340. Springer, Heidelberg (2009)

    CrossRef  Google Scholar 

  4. Borodin, A., El-Yaniv, R.: Online Computation and Competitive Analysis, vol. 2. Cambridge University Press (1998)

    Google Scholar 

  5. Dereniowski, D., Pelc, A.: Drawing maps with advice. J. Parallel Distrib. Comput. 72(2), 132–143 (2012)

    CrossRef  Google Scholar 

  6. Dobrev, S., Královič, R., Pardubská, D.: Measuring the problem-relevant information in input. ITA 43(3), 585–613 (2009)

    MATH  Google Scholar 

  7. Emek, Y., Fraigniaud, P., Korman, A., Rosén, A.: Online computation with advice. Theor. Comput. Sci. 412(24), 2642–2656 (2011)

    CrossRef  MATH  Google Scholar 

  8. Flocchini, P., Mans, B., Santoro, N.: Sense of direction in distributed computing. Theor. Comput. Sci. 291(1), 29–53 (2003)

    CrossRef  MathSciNet  MATH  Google Scholar 

  9. Fraigniaud, P., Gavoille, C., Ilcinkas, D., Pelc, A.: Distributed computing with advice: information sensitivity of graph coloring. Distributed Computing 21(6), 395–403 (2009)

    CrossRef  Google Scholar 

  10. Fraigniaud, P., Ilcinkas, D., Pelc, A.: Impact of memory size on graph exploration capability. Discrete Applied Mathematics 156(12), 2310–2319 (2008)

    CrossRef  MathSciNet  MATH  Google Scholar 

  11. Fraigniaud, P., Ilcinkas, D., Pelc, A.: Communication algorithms with advice. J. Comput. Syst. Sci. 76(3-4), 222–232 (2010)

    CrossRef  MathSciNet  MATH  Google Scholar 

  12. Fraigniaud, P., Korman, A., Lebhar, E.: Local mst computation with short advice. Theory Comput. Syst. 47(4), 920–933 (2010)

    CrossRef  MathSciNet  MATH  Google Scholar 

  13. Fusco, E.G., Pelc, A.: Trade-offs between the size of advice and broadcasting time in trees. Algorithmica 60(4), 719–734 (2011)

    CrossRef  MathSciNet  MATH  Google Scholar 

  14. Gutin, G., Punnen, A.P.: The Traveling Salesman Problem and Its Variations. Springer, Heidelberg (2002)

    MATH  Google Scholar 

  15. Hromkovič, J., Královič, R., Královič, R.: Information Complexity of Online Problems. In: Hliněný, P., Kučera, A. (eds.) MFCS 2010. LNCS, vol. 6281, pp. 24–36. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

  16. Hurkens, C.A., Woeginger, G.J.: On the nearest neighbor rule for the traveling salesman problem. Operations Research Letters 32(1), 1–4 (2004)

    CrossRef  MathSciNet  MATH  Google Scholar 

  17. Ilcinkas, D., Kowalski, D.R., Pelc, A.: Fast radio broadcasting with advice. Theor. Comput. Sci. 411(14-15), 1544–1557 (2010)

    CrossRef  MathSciNet  MATH  Google Scholar 

  18. Kalyanasundaram, B., Pruhs, K.R.: Constructing competitive tours from local information. Theoretical Computer Science 130(1), 125–138 (1994)

    CrossRef  MathSciNet  MATH  Google Scholar 

  19. Megow, N., Mehlhorn, K., Schweitzer, P.: Online Graph Exploration: New Results on Old and New Algorithms. In: Aceto, L., Henzinger, M., Sgall, J. (eds.) ICALP 2011, Part II. LNCS, vol. 6756, pp. 478–489. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  20. Miyazaki, S., Morimoto, N., Okabe, Y.: The online graph exploration problem on restricted graphs. IEICE Transactions on Information and Systems 92(9), 1620–1627 (2009)

    CrossRef  Google Scholar 

  21. Rosenkrantz, D.J., Stearns, R.E., Lewis II, P.M.: An analysis of several heuristics for the traveling salesman problem. SIAM J. Comput. 6(3), 563–581 (1977)

    CrossRef  MathSciNet  MATH  Google Scholar 

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Dobrev, S., Královič, R., Markou, E. (2012). Online Graph Exploration with Advice. In: Even, G., Halldórsson, M.M. (eds) Structural Information and Communication Complexity. SIROCCO 2012. Lecture Notes in Computer Science, vol 7355. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31104-8_23

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  • DOI: https://doi.org/10.1007/978-3-642-31104-8_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31103-1

  • Online ISBN: 978-3-642-31104-8

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