Author: Huang, X.
Paper Title Page
FRA2IO01 Development and Application of Online Optimization Algorithms 1287
 
  • X. Huang
    SLAC, Menlo Park, California, USA
 
  Funding: DOE
Automated tuning is an online optimization process. It can be faster and more efficient than manual tuning and can lead to better performance. Automated tuning is an online optimization process. It is more efficient than manual tuning and can lead to better performance. It may also substitute or improve upon model based methods. Noise tolerance is a fundamental challenge to online optimization algorithms. We discuss our experience in developing a high efficiency, noise-tolerant optimization algorithm, the RCDS method, and the successful application of the algorithm to various real-life accelerator problems. Experience with a few other online optimization algorithms are also discussed. A performance stabilizer and an interactive optimization GUI are presented.
 
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DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-NAPAC2016-FRA2IO01  
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