Iterative solution of nonlinear equations in several variables
Iterative solution of nonlinear equations in several variables
Comparison of Partitioning Techniques for Two-Level Iterative Solvers on Large, Sparse Markov Chains
SIAM Journal on Scientific Computing
Iterative Aggregation/Disaggregation Methods for Computing Some Characteristics of Markov Chains
LSSC '01 Proceedings of the Third International Conference on Large-Scale Scientific Computing-Revised Papers
Application of Threshold Partitioning of Sparse Matrices to Markov Chains
IPDS '96 Proceedings of the 2nd International Computer Performance and Dependability Symposium (IPDS '96)
Models of Infection: Person to Person
Computing in Science and Engineering
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A class of iterative aggregation/disaggregation methods (IAD) for computation of some important characteristics of Markov chains such as stationary probability vectors and mean first passage times matrices is presented and convergence properties of the corresponding algorithms are analyzed. Particular attention is focused on the fast convergence. Some classes of problems are identified for which the IAD methods return exact solutions after one single iteration sweap.