Open AccessOnline accelerator optimization with a machine learning-based stochastic algorithm
Author(s) -
Zhe Zhu,
Minghao Song,
Xiaobiao Huang
Publication year - 2020
Publication title -
machine learning: science and technology
Language(s) - English
Resource type - Journals
ISSN - 2632-2153
DOI - 10.1088/2632-2153/abc81e
Subject(s) - stochastic optimization , computer science , optimization algorithm , optimization problem , process (computing) , mathematical optimization , algorithm , mathematics , operating system
Online optimization is critical for realizing the design performance of accelerators. Highly efficient stochastic optimization algorithms are needed for many online accelerator optimization problems in order to find the global optimum in the non-linear, coupled parameter space. In this study, we propose to use the multi-generation Gaussian process optimizer for online accelerator optimization and demonstrate that the algorithm is significantly more efficient than other stochastic algorithms that are commonly used in the accelerator community.