Methods to compare expensive stochastic optimization algorithms with random restarts

Warren Hare (), Jason Loeppky and Shangwei Xie
Additional contact information
Warren Hare: University of British Columbia
Jason Loeppky: University of British Columbia
Shangwei Xie: University of British Columbia

Journal of Global Optimization, 2018, vol. 72, issue 4, No 9, 801 pages

Abstract: 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 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10898-018-0673-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jglopt:v:72:y:2018:i:4:d:10.1007_s10898-018-0673-7

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/10898

DOI: 10.1007/s10898-018-0673-7

Access Statistics for this article

Journal of Global Optimization is currently edited by Sergiy Butenko

More articles in Journal of Global Optimization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().