Abstract. This paper describes how reinforcement learning can be used to select from a wide variety of preconditioned solvers for sparse linear systems. This approach provides a simple way to consider complex metrics of goodness, and makes it easy to evaluate a wide range of preconditioned solvers. A basic implementation recommends solvers that, when they converge, generally do so with no more than a 17% overhead in time over the best solver possible within the test framework. Potential refinements of, and extensions to, the system are discussed.
Kuefler and Chen REF use reinforcement learning to select the best preconditioned solver for sparse linear systems from a sparse matrix collection.
On Using Reinforcement Learning to Solve Sparse Linear Systems
ICCS
Computer Science