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Accelerating Deterministic Global Optimization via GPU-parallel Interval Arithmetic
Hongzhen Zhang (Department of Chemical Engineering, KU Leuven, Leuven, Belgium), Tim Kerkenhoff (Institute of Climate and Energy Systems - Energy Systems Engineering), Neil Kichler (Software and Tools for Computational Engineering), Manuel Dahmen (Institute of Climate and Energy Systems - Energy Systems Engineering), Alexander Mitsos (JARA-CSD, Aachen, Germany, Process Systems Enginering, Institute of C…

Accelerating Deterministic Global Optimization via GPU-parallel Interval Arithmetic
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 been carried out to speed up B&B via CPU parallelization, GPU parallelization is much less explored. In this work, we investigate the design of a spatial B&B algorithm that involves an interval-based GPU-parallel lower bounding solver: The domain of each B&B node is temporarily partitioned into numerous subdomains, then …

Replaced article(s) found for math.DS. https://arxiv.org/list/math.DS/new
[1/1]:
- Learning dynamical systems from data: Gradient-based dictionary optimization
Mohammad Tabish, Neil K. Chada, Stefan Klus

The Proportional Fair Scheduler in Wavelength-Multiplexed Quantum Networks
Sanidhay Bhambay, Siddarth Koduru Joshi, Thirupathaiah Vasantam, Neil Walton
https://arxiv.org/abs/2507.13999

The Proportional Fair Scheduler in Wavelength-Multiplexed Quantum Networks
We address the problem of optimal pumping strategies in quantum networks. These networks enable secure communication by distributing entangled photon pairs to user (or node) pairs. Quantum Key Distribution (QKD) protocols, like BBM92, generate secret keys from entangled photons. While secure communication and error correction are essential for any quantum communication channel, resource contention, optimization, and fairness issues are critical for networks. In this article, we analyze the perf…