Gusak, O.Dayar, T.Fourneau, J. M.2015-07-282015-07-282003-07-160377-22171872-6860http://hdl.handle.net/11693/11258The generator matrix of a continuous-time stochastic automata network (SAN) is a sum of tensor products of smaller matrices, which may have entries that are functions of the global state space. This paper specifies easy to check conditions for a class of ordinarily lumpable partitionings of the generator of a continuous-time SAN in which aggregation is performed automaton by automaton. When there exists a lumpable partitioning induced by the tensor representation of the generator, it is shown that an efficient aggregation-iterative disaggregation algorithm may be employed to compute the steady-state distribution. The results of experiments with two SAN models show that the proposed algorithm performs better than the highly competitive block Gauss-Seidel in terms of both the number of iterations and the time to converge to the solution. © 2002 Elsevier Science B.V. All rights reserved.EnglishMarkov ProcessesStochastic Automata NetworksOrdinary LumpabilityAggregation With Iterative DisaggregationArticle10.1016/S0377-2217(02)00431-9