skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Robust simplex algorithm for online optimization

Abstract

A new optimization algorithm is introduced for online optimization applications. The algorithm was modified from the popular Nelder-Mead 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 cross-coupling 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:
 [1]
  1. 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):
slac-pub-17288
Journal ID: ISSN 2469-9888; PRABCJ
Grant/Contract Number:  
AC02-76SF00515
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 2469-9888
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 Nelder-Mead 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 cross-coupling 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 = {2469-9888},
number = 10,
volume = 21,
place = {United States},
year = {2018},
month = {10}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1103/physrevaccelbeams.21.104601

Citation Metrics:
Cited by: 1 work
Citation information provided by
Web of Science

Figures / Tables:

FIG. 1 FIG. 1: Minimum function values in 1000 evaluations for the Rosenbrock problem in 100 optimization runs (sorted) with the Nelder-Mead simplex algorithm (blue dashed line), Nelder-Mead with N = 3 averaging (red dash-dot line), the robust simplex algorithm without simplex rebuilding (RSim- plex w/o Rebuild, solid yellow line) and withmore » simplex rebuild- ing (RSimplex w/ Rebuild, solid magenta line).« less

Save / Share:
Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.