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20242026
most citedDeterministic Global Optimization of the Acquisition Function in Bayesian Optimization: To Do or Not To Do?

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math.OC2026

Robust Design of Multi-Energy Systems Accounting for Mixed-Integer Operational Problems

Moritz Wedemeyer, Alexander Mitsos, Manuel Dahmen

Identifying robust designs for multi-energy systems is computationally challenging. As rigorous approaches are often computationally intractable, heuristics are employed to generat…

math.OC2025

Deterministic Global Optimization of the Acquisition Function in Bayesian Optimization: To Do or Not To Do?

Anastasia Georgiou, Daniel Jungen, Luise Kaven +4

Bayesian Optimization (BO) with Gaussian Processes relies on optimizing an acquisition function to determine sampling. We investigate the advantages and disadvantages of using a de…

math.OC2025

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…

math.OC2025

Robust Energy System Design via Semi-infinite Programming

Moritz Wedemeyer, Eike Cramer, Alexander Mitsos +1

Time-series information needs to be incorporated into energy system optimization to account for the uncertainty of renewable energy sources. Typically, time-series aggregation meth…

math.OC2025

Optimizing Flexibility in Power Systems by Maximizing the Region of Manageable Uncertainties

Aron Zingler, Stephane Fliscounakis, Patrick Panciatici +1

Motivated by the increasing need to hedge against load and generation uncertainty in the operation of power grids, we propose flexibility maximization during operation. We consider…