3 papers
math.NA2019
On the convergence of Krylov methods with low-rank truncations
Davide Palitta, Patrick Kürschner
Low-rank Krylov methods are one of the few options available in the literature to address the numerical solution of large-scale general linear matrix equations. These routines amou…
math.NA2019
Low-rank updates and divide-and-conquer methods for quadratic matrix equations
Daniel Kressner, Patrick Kürschner, Stefano Massei
In this work, we consider two types of large-scale quadratic matrix equations: Continuous-time algebraic Riccati equations, which play a central role in optimal and robust control,…
math.NA2018
Greedy low-rank algorithm for spatial connectome regression
Patrick Kürschner, Sergey Dolgov, Kameron Decker Harris +1
Recovering brain connectivity from tract tracing data is an important computational problem in the neurosciences. Mesoscopic connectome reconstruction was previously formulated as…