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. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Kuefler, E., & Chen, T. Y. (2008). On using reinforcement learning to solve sparse linear systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5101 LNCS, pp. 955–964). https://doi.org/10.1007/978-3-540-69384-0_100
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