CONFIG (CONstrained efFIcient Global optimization) is a data-driven efficient global optimization toolbox. By sequentially and adaptively evaluting different candidate solutions under the guidance of Gaussian process surrogates, CONFIG algorithm can efficiently identify globally optimal solution for constrained black-box optimization problems with potentially non-convex and multi-modal functions.
Red triangle represents the global optimum.
Green curve represents the ground truth
function.
CONFIG toolbox can be applied for general black-box optimization problems with a compact set of candidate solutions. It is particularly useful when the objective and constraint functions are expensive to evaluate. Typical application domains include set-points tuning in process control and PI controller tuning for energy systems, when constraint violations during the tuning process are not safety critical.
We recommend the CONFIG algorithm, which has demonstrated good convergence property to global optimal solution both in theory and in practice. But to allow more flexibility for user's choice, we also implement the following popular algorithms.
- Constrained EI.
- Primal-dual.
- EPBO.
Under the directory where README.md is, run pip install ..
In the directory "./examples", we provide a notebook to demonstrate the usage of the toolbox.
If you use this toolbox for your research that leads to publications, we would appreciate your recognition by citing the following paper.
- Xu, W., Jiang, Y., and Jones, C.N. (2022a). Constrained efficient global optimization of expensive black-box functions. doi:10.48550/ARXIV.2211.00162. URL https://arxiv.org/abs/2211.00162.