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Methods to compare expensive stochastic optimization algorithms with random restarts

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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.

Keywords

Random restarts Stochastic optimization Benchmarking Nonconvex optimization 

Notes

Acknowledgements

This work was supported by Natural Sciences and Engineering Research Council of Canada (NSERC) under Collaborative Research and Development (CRD) Grant #CRDPJ 411318-15. The Grant was sponsored by Softree Technical Systems Inc. This work was supported by Discovery Grants #355571-2013 (Hare) and #2015-03895 (Loeppky) from NSERC. Part of the research was performed in the Computer-Aided Convex Analysis (CA2) laboratory funded by a Leaders Opportunity Fund (LOF) from the Canada Foundation for Innovation (CFI) and by a British Columbia Knowledge Development Fund (BCKDF).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.MathematicsUniversity of British ColumbiaKelownaCanada
  2. 2.StatisticsUniversity of British ColumbiaKelownaCanada
  3. 3.Computer ScienceUniversity of British ColumbiaKelownaCanada

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