8 citations · 9 across the 6 of their papers we have counts for
11 papers · 1 filter
The complexity landscape of robust (integer) linear programming
Michael Poss, Jannis Kurtz, Marc Goerigk +1
We study the computational complexity of the decision versions of three classic robust optimization problems: static robust optimization, two-stage (adjustable) robust optimization…
Globalized Adversarial Regret Optimization: Robust Decisions with Uncalibrated Predictions
Jannis Kurtz, Bart P. G. van Parys
Optimization problems routinely depend on uncertain parameters that must be predicted before a decision is made. Classical robust and regret formulations are designed to handle err…
A Frank-Wolfe Algorithm for Oracle-based Robust Optimization
Mathieu Besançon, Jannis Kurtz
We tackle robust optimization problems under objective uncertainty in the oracle model, i.e., when the deterministic problem is solved by an oracle. The oracle-based setup is favor…
Bounding the Optimal Number of Policies for Robust K-Adaptability
Jannis Kurtz
In the realm of robust optimization the k-adaptability approach is one promising method to derive approximate solutions for two-stage robust optimization problems. Instead of allow…
Efficient and Robust Mixed-Integer Optimization Methods for Training Binarized Deep Neural Networks
Jannis Kurtz, Bubacarr Bah
Compared to classical deep neural networks its binarized versions can be useful for applications on resource-limited devices due to their reduction in memory consumption and comput…
Data-Driven Robust Optimization using Unsupervised Deep Learning
Marc Goerigk, Jannis Kurtz
Robust optimization has been established as a leading methodology to approach decision problems under uncertainty. To derive a robust optimization model, a central ingredient is to…