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Solver Tuning and Model Configuration

Part of the Lecture Notes in Computer Science book series (LNAI,volume 11117)

Abstract

This paper addresses the problem of tuning parameters of mathematical solvers to increase their performance. We investigate how solvers can be tuned for models that undergo two types of configuration: variable configuration and constraint configuration. For each type, we investigate search algorithms for data generation that emphasizes exploration or exploitation. We show the difficulties for solver tuning in constraint configuration and how data generation methods affects a training sets learning potential.

Keywords

  • Tuning mathematical solvers
  • Mathematical solvers
  • Machine learning
  • Evolutionary algorithm
  • Novelty search

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References

  1. Barry, M., Schillinger, M., Weigt, H., Schumann, R.: Configuration of hydro power plant mathematical models. In: Gottwalt, S., König, L., Schmeck, H. (eds.) EI 2015. LNCS, vol. 9424, pp. 200–207. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25876-8_17

    CrossRef  Google Scholar 

  2. Barry, M., Schumann, R.: Dynamic and configurable mathematical modelling of a hydropower plant research in progress paper. In: Presented at the 29. Workshop “Planen, Scheduling und Konfigurieren, Entwerfen” (PuK 2015), September 2015

    Google Scholar 

  3. Baz, M., Hunsaker, B., Brooks, P., Gosavi, A.: Automated tuning of optimization software parameters. Technical Report TR2007-7. University of Pittsburgh, Department of Industrial Engineering (2007)

    Google Scholar 

  4. Baz, M., Hunsaker, B., Prokopyev, O.: How much do we “pay” for using default parameters? Comput. Optim. Appl. 48(1), 91–108 (2011)

    MathSciNet  CrossRef  Google Scholar 

  5. Boussaa, M., Barais, O., Sunyé, G., Baudry, B.: A novelty search approach for automatic test data generation. In: Proceedings of the Eighth International Workshop on Search-Based Software Testing, pp. 40–43. IEEE Press (2015)

    Google Scholar 

  6. Chawdhry, P.K., Roy, R., Pant, R.K.: Soft Computing in Engineering Design and Manufacturing. Springer, London (2012)

    Google Scholar 

  7. Cplex, G.: The solver manuals (2014)

    Google Scholar 

  8. Drud, A.: Conopt solver manual. ARKI Consulting and Development, Bagsvaerd, Denmark (1996)

    Google Scholar 

  9. Guo, H., Viktor, H.L.: Learning from imbalanced data sets with boosting and data generation: the databoost-IM approach. ACM SIGKDD Explor. Newsl. 6(1), 30–39 (2004)

    CrossRef  Google Scholar 

  10. Gurobi Optimization, Inc.: Gurobi optimizer reference manual (2016). http://www.gurobi.com

  11. He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009)

    CrossRef  Google Scholar 

  12. Hutter, F., Hoos, H.H., Leyton-Brown, K.: Automated configuration of mixed integer programming solvers. In: Lodi, A., Milano, M., Toth, P. (eds.) CPAIOR 2010. LNCS, vol. 6140, pp. 186–202. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_23

    CrossRef  Google Scholar 

  13. Hutter, F., Hoos, H.H., Leyton-Brown, K., Stützle, T.: Paramils: an automatic algorithm configuration framework. J. Artif. Intell. Res. 36, 267–306 (2009)

    CrossRef  Google Scholar 

  14. Hutter, F., Xu, L., Hoos, H.H., Leyton-Brown, K.: Algorithm runtime prediction: methods & evaluation. Artif. Intell. 206, 79–111 (2014)

    MathSciNet  CrossRef  Google Scholar 

  15. IBM: CPLEX Performance Tuning for Mixed Integer Programs (2016). http://www-01.ibm.com/support/docview.wss?uid=swg21400023

  16. Jain, A.K., Mao, J., Mohiuddin, K.M.: Artificial neural networks: a tutorial. Computer 29(3), 31–44 (1996)

    CrossRef  Google Scholar 

  17. Juslin, P., Winman, A., Olsson, H.: Naive empiricism and dogmatism in confidence research: a critical examination of the hard-easy effect. Psychol. Rev. 107(2), 384 (2000)

    CrossRef  Google Scholar 

  18. Kadioglu, S., Malitsky, Y., Sellmann, M., Tierney, K.: ISAC-instance-specific algorithm configuration. In: ECAI, vol. 215, pp. 751–756 (2010)

    Google Scholar 

  19. Karlik, B., Olgac, A.V.: Performance analysis of various activation functions in generalized MLP architectures of neural networks. Int. J. Artif. Intell. Expert Syst. 1(4), 111–122 (2011)

    Google Scholar 

  20. Knowles, J.: Parego: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Trans. Evol. Comput. 10(1), 50–66 (2006)

    CrossRef  Google Scholar 

  21. Kotthoff, L.: Algorithm selection for combinatorial search problems: a survey. AI Mag. 35(3), 48–60 (2014)

    CrossRef  Google Scholar 

  22. Lehman, J., Stanley, K.O.: Abandoning objectives: evolution through the search for novelty alone. Evol. Comput. 19(2), 189–223 (2011)

    CrossRef  Google Scholar 

  23. Lehmann, G., Blumendorf, M., Trollmann, F., Albayrak, S.: Meta-modeling runtime models. In: Dingel, J., Solberg, A. (eds.) MODELS 2010. LNCS, vol. 6627, pp. 209–223. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21210-9_21

    CrossRef  Google Scholar 

  24. López-Ibánez, M., Stützle, T.: Automatically improving the anytime behaviour of optimisation algorithms. Eur. J. Oper. Res. 235(3), 569–582 (2014)

    MathSciNet  CrossRef  Google Scholar 

  25. Preuss, M., Rudolph, G., Wessing, S.: Tuning optimization algorithms for real-world problems by means of surrogate modeling. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 401–408. ACM (2010)

    Google Scholar 

  26. Stefan Eggenschwiler, R.S.: Parameter tuning for the CPLEX. Bachelor Thesis (2016)

    Google Scholar 

  27. Xu, L., Hutter, F., Hoos, H.H., Leyton-Brown, K.: Hydra-MIP: automated algorithm configuration and selection for mixed integer programming. In: RCRA Workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion at the International Joint Conference on Artificial Intelligence (IJCAI), pp. 16–30 (2011)

    Google Scholar 

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Acknowledgment

Parts of this work have been funded by the Swiss National Science Foundation as part of the project 407040_153760 Hydro Power Operation and Economic Performance in a Changing Market Environment.

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Correspondence to Michael Barry .

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Barry, M., Abgottspon, H., Schumann, R. (2018). Solver Tuning and Model Configuration. In: Trollmann, F., Turhan, AY. (eds) KI 2018: Advances in Artificial Intelligence. KI 2018. Lecture Notes in Computer Science(), vol 11117. Springer, Cham. https://doi.org/10.1007/978-3-030-00111-7_13

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  • DOI: https://doi.org/10.1007/978-3-030-00111-7_13

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