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CRC Press
314 pages
Presents a number of new and potentially useful self-learning (adaptive) control algorithms and theoretical as well as practical results for both unconstrained and constrained finite Markov chains-efficiently processing new information by adjusting the control strategies directly or indirectly.
Controlled Markov chains. Unconstrained Markov chains: Lagrange multipliers approach; penalty function approach; projection gradient method. Constrained Markov chains: Lagrange multipliers approach; penalty function approach; nonregular Markov chains; practical aspects.
