Introduction

genopt is a python package, trying to serve as a solution of general multi-dimensional optimization. The core optimization algorithms employed inside are mainly provided by DAKOTA, which is the brief for Design Analysis Kit for Optimization and Terascale Applications, another tool written in C++.

The following image illustrates the general optimization framework by properly utilizing DAKOTA.

To apply this optimization framework, specific analysis drivers should be created first, e.g. flamedriver1, flamedriver2… indicate the dedicated executable drivers built from C++, for the application in accelerator commissioning, e.g. FRIB.

Note

flame is an particle envolope tracking code developed by C++, with the capbility of multi-charge particle states momentum space tracking, it is developed by FRIB; flamedriver(s) are user-customized executables by linking the flame core library (libflame_core.so) to accomplish various different requirements.

The intention of genopt is to provide a uniform interface to do the multi-dimensional optimization tasks. It provides interfaces to let the users to customize the optimization drivers, optimization methods, variables, etc. The optimized results are returned by clean interface. Dedicated analysis drivers should be created and tell the package to use. DakotaOC is a dedicated class designed for orbit correction for accelerator, which uses flame as the modeling tool.