11 citations · 23 across the 12 of their papers we have counts for
7 papers · 1 filter
Improving reconstructions in nanotomography for homogeneous materials via mathematical optimization
Sebastian Kreuz, Benjamin Apeleo Zubiri, Silvan Englisch +5
Compressed sensing is an image reconstruction technique to achieve high-quality results from limited amount of data. In order to achieve this, it utilizes prior knowledge about the…
A semidefinite programming hierarchy for covering problems in discrete geometry
Cordian Riener, Jan Rolfes, Frank Vallentin
In this paper we present a new semidefinite programming hierarchy for covering problems in compact metric spaces. Over the last years, these kind of hierarchies were developed prim…
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…
Alternating mixed-integer programming and neural network training for approximating stochastic two-stage problems
Jan Kronqvist, Boda Li, Jan Rolfes +1
The presented work addresses two-stage stochastic programs (2SPs), a broadly applicable model to capture optimization problems subject to uncertain parameters with adjustable decis…
Bounds on polarization problems on compact sets via mixed integer programming
Jan Rolfes, Robert Schüler, Marc Christian Zimmermann
Finding point configurations, that yield the maximum polarization (Chebyshev constant) is gaining interest in the field of geometric optimization. In the present article, we study…
A mixed-integer approximation of robust optimization problems with mixed-integer adjustments
Jan Kronqvist, Boda Li, Jan Rolfes
In the present article we propose a mixed-integer approximation of adjustable-robust optimization (ARO) problems, that have both, continuous and discrete variables on the lowest le…
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…