ABSTRACT
This chapter discusses the general considerations of the online optimization approach. Beam-based optimization is the approach of adjusting the control parameters of the accelerator to optimize its performance, using the real-time, measured beam performance as the guide for choosing the trial settings. Online optimization algorithms are essential to the strength of automated tuning. Because of the measurement errors that enter the performance metrics and other characteristics of the online application environment, online optimization algorithms need to address special challenges. Manual tuning is essentially an optimization process. The function to be optimized is the machine performance evaluated on the operating machine through measurements. Online optimization is similar to ordinary mathematical optimization in that it looks for the maximum or minimum of the objective function(s) within a certain parameter space. The random error has a significant impact over the behaviors of the optimization algorithms. Automated online optimization is executed by computer programs.
