4 papers
Two-Stage Learned Decomposition for Scalable Routing on Multigraphs
Filip Rydin, Morteza Haghir Chehreghani, Balázs Kulcsár
Most neural methods for Vehicle Routing Problems (VRPs) are limited to Euclidean settings or simple graphs. In this work, we instead consider multigraphs, where parallel edges repr…
High-dimensional Bayesian filtering through deep density approximation
Kasper BÃ¥gmark, Filip Rydin
In this work, we systematically benchmark two recently developed deep density methods for nonlinear filtering. We model the filtering density of a discretely observed stochastic di…
A convergent scheme for the Bayesian filtering problem based on the Fokker--Planck equation and deep splitting
Kasper BÃ¥gmark, Adam Andersson, Stig Larsson +1
A numerical scheme for approximating the nonlinear filtering density is introduced and its convergence rate is established, theoretically under a parabolic Hörmander condition, an…
Beyond Simple Graphs: Neural Multi-Objective Routing on Multigraphs
Filip Rydin, Attila Lischka, Jiaming Wu +2
Learning-based methods for routing have gained significant attention in recent years, both in single-objective and multi-objective contexts. Yet, existing methods are unsuitable fo…