Algorithms for determining the state-time probabilities and the limit matrix in Markov chains

New calculation procedures for finding the probabilities of state transitions of the system in Markov chains based on dynamic programming are developed and polynomial time algorithms for determining the limit state matrix in such processes are proposed. Computational complexity aspects and possible applications of the proposed algorithms for the stochastic optimization problems are characterized.