Julia Manger, Department of Computer Science, University of Kaiserslautern-Landau (RPTU)

Title: Bayesian Optimization with Inequality Constraints

Abstract:

Bayesian Optimization (BO) is an effective method for optimizing expensive-to-evaluate
black-box functions, often applied in engineering and machine learning. Many real-world
problems involve constraints that standard BO is not designed to manage.
This thesis explores the integration of inequality constraints in BO, extending it to con-
strained problems. In simulations of both one- and two-dimensional search spaces, con-
strained Bayesian Optimization (cBO) demonstrates an effective balance between explo-
ration and exploitation, simultaneously ensuring the feasibility of solutions.
cBO is also compared with the Quadratic Penalty (QPF) method and with a method
using projection to show cBO’s advantages compared to penalty- or projection-based
optimization.