47 citations · 51 across the 5 of their papers we have counts for
5 papers
Pruning for efficient deterministic global optimization over trained ReLU neural networks
Giacomo Lastrucci, Tanuj Karia, Victor Schulte +2
Neural networks are increasingly used as surrogates in optimization problems to replace computationally expensive models. However, embedding ReLU neural networks in mathematical pr…
Accelerating Deterministic Global Optimization via GPU-parallel Interval Arithmetic
Hongzhen Zhang, Tim Kerkenhoff, Neil Kichler +4
Spatial Branch and Bound (B&B) algorithms are widely used for solving nonconvex problems to global optimality, yet they remain computationally expensive. Though some works have bee…
Cost-Optimal Power-to-Methanol: Flexible Operation or Intermediate Storage?
Simone Mucci, Alexander Mitsos, Dominik Bongartz
The synthesis of methanol from captured carbon dioxide and green hydrogen could be a promising replacement for the current fossil-based production. The major energy input and cost…
Optimization with Trained Machine Learning Models Embedded
Artur M. Schweidtmann, Dominik Bongartz, Alexander Mitsos
Trained ML models are commonly embedded in optimization problems. In many cases, this leads to large-scale NLPs that are difficult to solve to global optimality. While ML models fr…
Global Optimization of Gaussian processes
Artur M. Schweidtmann, Dominik Bongartz, Daniel Grothe +4
Gaussian processes~(Kriging) are interpolating data-driven models that are frequently applied in various disciplines. Often, Gaussian processes are trained on datasets and are subs…