Multilevel Methods for Dynamic Programming
Deterministic and Stochastic Iterative Methods with Application to Recommendation Engines
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Beschreibung
The author studies multilevel methods for the solution of systems of linear equations arising from discrete probabilistic optimal control problems with a special emphasis of problems related to recommendation engines. This book features a sound and rigorous matrix-theoretic foundation, and novel convergence analyses of the proposed procedures. Moreover, the theoretical discussion is supported by numerical experiments. The research presented in this book has been carried out as a part of a joint project involving TU Hamburg-Harburg, TU Berlin, and the realtime analytics company prudsys AG.
Autor*in
Alexander Paprotny
Themen in »Multilevel Methods for Dynamic Programming«
Markov decision processes algebraic multigrid dynamic programming iterative solvers for linear systems multilevel methods recommender systems reinforcement learning stochastic iterative algorithms stochastic matrices stochastic optimal control temporal difference learning