Event Information:

• Thu

  22

  May

  2025

  SC Seminar: Julia Manger

  10:30Room 32-349

  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.