Tootfinder

Randomized HyperSteiner: A Stochastic Delaunay Triangulation Heuristic for the Hyperbolic Steiner Minimal Tree
Aniss Aiman Medbouhi, Alejandro Garc\'ia-Castellanos, Giovanni Luca Marchetti, Daniel Pelt, Erik J Bekkers, Danica Kragic
https://arxiv.org/abs/2510.09328

Randomized HyperSteiner: A Stochastic Delaunay Triangulation Heuristic for the Hyperbolic Steiner Minimal Tree
We study the problem of constructing Steiner Minimal Trees (SMTs) in hyperbolic space. Exact SMT computation is NP-hard, and existing hyperbolic heuristics such as HyperSteiner are deterministic and often get trapped in locally suboptimal configurations. We introduce Randomized HyperSteiner (RHS), a stochastic Delaunay triangulation heuristic that incorporates randomness into the expansion process and refines candidate trees via Riemannian gradient descent optimization. Experiments on synthetic…

Where did heavy binaries go? Gravitational-wave populations using Delaunay triangulation with optimized complexity
Rodrigo Tenorio, Alexandre Toubiana, Tristan Bruel, Davide Gerosa, Jonathan R. Gair
https://arxiv.org/abs/2509.19466

Where did heavy binaries go? Gravitational-wave populations using Delaunay triangulation with optimized complexity
We investigate the joint mass-redshift evolution of the binary black hole merger rate in the latest gravitational-wave detection catalog, GWTC-4.0. We present and apply a novel non-parametric framework for modeling multi-dimensional, correlated distributions based on Delaunay triangulation. Crucially, the complexity of the model -- namely, the number, positions, and weights of triangulation nodes -- is inferred directly from the data, resulting in a highly efficient approach that requires about…

Randomized HyperSteiner: A Stochastic Delaunay Triangulation Heuristic for the Hyperbolic Steiner Minimal Tree
Aniss Aiman Medbouhi, Alejandro Garc\'ia-Castellanos, Giovanni Luca Marchetti, Daniel Pelt, Erik J Bekkers, Danica Kragic
https://arxiv.org/abs/2510.09328