Probabilistic Linear Solvers: A Unifying View
For researchers in numerical linear algebra and probabilistic numerics, this paper provides a theoretical unification that clarifies relationships between existing methods and bridges probabilistic and classical iterative solver literature.
This work unifies disparate probabilistic linear solvers by establishing general conditions for their equivalence, and connects them to projection methods like GMRES, providing a probabilistic interpretation of a broad class of iterative solvers and introducing a probabilistic view of preconditioning.
Several recent works have developed a new, probabilistic interpretation for numerical algorithms solving linear systems in which the solution is inferred in a Bayesian framework, either directly or by inferring the unknown action of the matrix inverse. These approaches have typically focused on replicating the behavior of the conjugate gradient method as a prototypical iterative method. In this work surprisingly general conditions for equivalence of these disparate methods are presented. We also describe connections between probabilistic linear solvers and projection methods for linear systems, providing a probabilistic interpretation of a far more general class of iterative methods. In particular, this provides such an interpretation of the generalised minimum residual method. A probabilistic view of preconditioning is also introduced. These developments unify the literature on probabilistic linear solvers, and provide foundational connections to the literature on iterative solvers for linear systems.