Constrained Bayesian Optimization for Problems with Piece-wise Smooth Constraints

Aliakbar Gorji Daronkolaei, Amir Hajian, Tonya Custis

Abstract: This paper proposes a new formulation of Gaussian process for constraints with piece-wise smooth conditions. Combining ideas from decision trees and Gaussian processes, it is shown that the new model can effectively identify the non-smooth regions and tackle the non-smoothness in piece-wise smooth constraint functions. A constrained Bayesian optimizer is then constructed to handle optimization problems with both noisy objective and constraint functions.