In this work, we estimate the regions of attraction for belief propagation. This extends existing stability analysis and provides initial message values for which belief propagation is guaranteed to converge. Our approach utilizes the theory of Lyapunov functions that, however, does not readily yield useful regions of attraction. Therefore, we utilize polynomial sum-of-squares relaxations and provide an algorithm that computes valid Lyapunov functions. This admits a novel way of studying the solution space of belief propagation. Finally, we apply our approach to small-scale models and discuss the effect of the potentials on the regions of attraction.
|Titel||Uncertainty in Artificial Intelligence|
|Publikationsstatus||Angenommen/In Druck - 2021|
|Veranstaltung||37th Conference on Uncertainty in Artificial Intelligence: UAI 2021 - Virtuell|
Dauer: 27 Jul 2021 → 29 Jul 2021
|Konferenz||37th Conference on Uncertainty in Artificial Intelligence|
|Zeitraum||27/07/21 → 29/07/21|