Speaker
Description
The recent development of advanced black box optimization algorithms has promised order of magnitude improvements in optimization speed when solving accelerator physics problems. These algorithms have been implemented in the python package Xopt, which has been used to solve online and offline accelerator optimization problems at a wide number of facilities, including at SLAC, Argonne, BNL, DESY, ESRF, and others. In this work, we describe updates to the Xopt framework that expand its capabilities and improves optimization performance in solving online optimization problems. This includes significant improvements in Bayesian optimization algorithms, such as trust region Bayesian optimization and Bayesian algorithm execution for optimizing virtual objectives. We also discuss how Xopt has been incorporated into the Badger graphical user interface that allows easy access to these advanced control algorithms in the accelerator control room.
| Working group | WG5 : Beam sources, monitoring and control |
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