Robust simplex algorithm for online optimization
Abstract
A new optimization algorithm is introduced for online optimization applications. The algorithm was modified from the popular NelderMead simplex method to make it noise aware and noise resistant. Simulation with an analytic function is used to demonstrate its performance. The algorithm has been successfully tested in experiments, which showed that the algorithm is robust for optimization problems with complex functional dependence, high crosscoupling between parameters, and high noise. Advantages of the new algorithm include high efficiency and that it does not require prior knowledge of the parameter space such as an initial conjugate direction set.
 Authors:

 SLAC National Accelerator Lab., Menlo Park, CA (United States)
 Publication Date:
 Research Org.:
 SLAC National Accelerator Lab., Menlo Park, CA (United States)
 Sponsoring Org.:
 USDOE
 OSTI Identifier:
 1477568
 Alternate Identifier(s):
 OSTI ID: 1490382
 Report Number(s):
 slacpub17288
Journal ID: ISSN 24699888; PRABCJ
 Grant/Contract Number:
 AC0276SF00515
 Resource Type:
 Journal Article: Published Article
 Journal Name:
 Physical Review Accelerators and Beams
 Additional Journal Information:
 Journal Volume: 21; Journal Issue: 10; Journal ID: ISSN 24699888
 Publisher:
 American Physical Society (APS)
 Country of Publication:
 United States
 Language:
 English
 Subject:
 43 PARTICLE ACCELERATORS
Citation Formats
Huang, Xiaobiao. Robust simplex algorithm for online optimization. United States: N. p., 2018.
Web. doi:10.1103/physrevaccelbeams.21.104601.
Huang, Xiaobiao. Robust simplex algorithm for online optimization. United States. doi:10.1103/physrevaccelbeams.21.104601.
Huang, Xiaobiao. Wed .
"Robust simplex algorithm for online optimization". United States. doi:10.1103/physrevaccelbeams.21.104601.
@article{osti_1477568,
title = {Robust simplex algorithm for online optimization},
author = {Huang, Xiaobiao},
abstractNote = {A new optimization algorithm is introduced for online optimization applications. The algorithm was modified from the popular NelderMead simplex method to make it noise aware and noise resistant. Simulation with an analytic function is used to demonstrate its performance. The algorithm has been successfully tested in experiments, which showed that the algorithm is robust for optimization problems with complex functional dependence, high crosscoupling between parameters, and high noise. Advantages of the new algorithm include high efficiency and that it does not require prior knowledge of the parameter space such as an initial conjugate direction set.},
doi = {10.1103/physrevaccelbeams.21.104601},
journal = {Physical Review Accelerators and Beams},
issn = {24699888},
number = 10,
volume = 21,
place = {United States},
year = {2018},
month = {10}
}
Free Publicly Available Full Text
Publisher's Version of Record at 10.1103/physrevaccelbeams.21.104601
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Cited by: 1 work
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Figures / Tables:
FIG. 1: Minimum function values in 1000 evaluations for the Rosenbrock problem in 100 optimization runs (sorted) with the NelderMead simplex algorithm (blue dashed line), NelderMead with N = 3 averaging (red dashdot line), the robust simplex algorithm without simplex rebuilding (RSim plex w/o Rebuild, solid yellow line) and withmore »
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