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Computing and Scheduling with Explorable Uncertainty

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Sailing Routes in the World of Computation (CiE 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10936))

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Abstract

Explorable uncertainty refers to settings where parts of the input data are initially unknown, but can be obtained at a certain cost using queries. In a typical setting, initially only intervals that contain the exact input values are known, and queries can be made to obtain exact values. An algorithm must make queries one by one until it has obtained sufficient information to solve the given problem. We discuss two lines of work in this area: In the area of query-competitive algorithms, one compares the number of queries made by the algorithm with the best possible number of queries for the given input. In the area of scheduling with explorable uncertainty, queries may correspond to tests that can reduce the running-time of a job by an a priori unknown amount and are executed on the machine that also schedules the jobs, thus contributing directly to the objective value of the resulting schedule.

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References

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

    Google Scholar 

  2. Bruce, R., Hoffmann, M., Krizanc, D., Raman, R.: Efficient update strategies for geometric computing with uncertainty. Theory Comput. Syst. 38(4), 411–423 (2005)

    Article  MathSciNet  Google Scholar 

  3. Dürr, C., Erlebach, T., Megow, N., Meißner, J.: Scheduling with explorable uncertainty. In: Karlin, A.R. (ed.) ITCS 2018 9th Innovations in Theoretical Computer Science Conference, 11–14 January 2018, Cambridge, MA, USA. LIPIcs, vol. 94, pp. 30:1–30:14. Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik (2018). https://doi.org/10.4230/LIPIcs.ITCS.2018.30

  4. Erlebach, T., Hoffmann, M.: Minimum spanning tree verification under uncertainty. In: Kratsch, D., Todinca, I. (eds.) WG 2014. LNCS, vol. 8747, pp. 164–175. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12340-0_14

    Chapter  Google Scholar 

  5. Erlebach, T., Hoffmann, M.: Query-competitive algorithms for computing with uncertainty. Bull. EATCS 116, 22–39 (2015)

    MathSciNet  Google Scholar 

  6. Erlebach, T., Hoffmann, M., Kammer, F.: Query-competitive algorithms for cheapest set problems under uncertainty. Theor. Comput. Sci. 613, 51–64 (2016)

    Article  MathSciNet  Google Scholar 

  7. Erlebach, T., Hoffmann, M., Krizanc, D., Mihalák, M., Raman, R.: Computing minimum spanning trees with uncertainty. In: 25th International Symposium on Theoretical Aspects of Computer Science (STACS 2008). LIPIcs, vol. 1, pp. 277–288 (2008)

    Google Scholar 

  8. Kahan, S.: A model for data in motion. In: 23rd Annual ACM Symposium on Theory of Computing (STOC 1991), pp. 267–277 (1991)

    Google Scholar 

  9. Levi, R.: Practice driven scheduling models. Talk at Dagstuhl Seminar 16081: Scheduling (2016)

    Google Scholar 

  10. Megow, N., Meißner, J., Skutella, M.: Randomization helps computing a minimum spanning tree under uncertainty. SIAM J. Comput. 46(4), 1217–1240 (2017). https://doi.org/10.1137/16M1088375

    Article  MathSciNet  Google Scholar 

  11. Olston, C., Widom, J.: Offering a precision-performance tradeoff for aggregation queries over replicated data. In: 26th International Conference on Very Large Data Bases (VLDB 2000), pp. 144–155 (2000)

    Google Scholar 

  12. Shaposhnik, Y.: Exploration vs. Exploitation: reducing uncertainty in operational problems. Ph.D. thesis, Sloan School of Management, MIT (2016)

    Google Scholar 

  13. Singla, S.: The price of information in combinatorial optimization. In: Czumaj, A. (ed.) Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2018), pp. 2523–2532 (2018)

    Chapter  Google Scholar 

  14. Yamaguchi, Y., Maehara, T.: Stochastic packing integer programs with few queries. In: Czumaj, A. (ed.) Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2018), pp. 293–310 (2018)

    Chapter  Google Scholar 

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Correspondence to Thomas Erlebach .

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Erlebach, T. (2018). Computing and Scheduling with Explorable Uncertainty. In: Manea, F., Miller, R., Nowotka, D. (eds) Sailing Routes in the World of Computation. CiE 2018. Lecture Notes in Computer Science(), vol 10936. Springer, Cham. https://doi.org/10.1007/978-3-319-94418-0_16

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  • DOI: https://doi.org/10.1007/978-3-319-94418-0_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-94417-3

  • Online ISBN: 978-3-319-94418-0

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