3 papers
math.OC2023
A Positive Semidefinite Safe Approximation of Multivariate Distributionally Robust Constraints Determined by Simple Functions
J. Dienstbier, F. Liers, J. Rolfes
Single-level reformulations of (non-convex) distributionally robust optimization (DRO) problems are often intractable, as they contain semiinfinite dual constraints. Based on such…
math.OC2023
Quality Control in Particle Precipitation via Robust Optimization
Martina Kuchlbauer, Jana Dienstbier, Adeel Muneer +4
In this work, we propose a robust optimization approach to mitigate the impact of uncertainties in particle precipitation. Our model incorporates partial differential equations, mo…
math.OC2023
A Safe Approximation Based on Mixed-Integer Optimization for Non-Convex Distributional Robustness Governed by Univariate Indicator Functions
Jana Dienstbier, Frauke Liers, Florian Rösel +1
In this work, we present an algorithmically tractable safe approximation of distributionally robust optimization (DRO) problems that contain univariate indicator functions. The lat…