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Methods to compare expensive stochastic optimization algorithms with random restarts

Author

Listed:
  • Warren Hare

    (University of British Columbia)

  • Jason Loeppky

    (University of British Columbia)

  • Shangwei Xie

    (University of British Columbia)

Abstract

We consider the challenge of numerically comparing optimization algorithms that employ random-restarts under the assumption that only limited test data is available. We develop a bootstrapping technique to estimate the incumbent solution of the optimization problem over time as a stochastic process. The asymptotic properties of the estimator are examined and the approach is validated by an out-of-sample test. Finally, three methods for comparing the performance of different algorithms based on the estimator are proposed and demonstrated with data from a real-world optimization problem.

Suggested Citation

  • Warren Hare & Jason Loeppky & Shangwei Xie, 2018. "Methods to compare expensive stochastic optimization algorithms with random restarts," Journal of Global Optimization, Springer, vol. 72(4), pages 781-801, December.
    • Handle: RePEc:spr:jglopt:v:72:y:2018:i:4:d:10.1007_s10898-018-0673-7
    DOI: 10.1007/s10898-018-0673-7
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    References listed on IDEAS

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    2. Butler, A. & Haynes, R.D. & Humphries, T.D. & Ranjan, P., 2014. "Efficient optimization of the likelihood function in Gaussian process modelling," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 40-52.
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