This book presents the latest findings on stochastic dynamic
programming models and on solving optimal control problems in
networks. It includes the authors' new findings on determining the
optimal solution of discrete optimal control problems in networks
and on solving game variants of Markov decision problems in the
context of computational networks. First, the book studies the
finite state space of Markov processes and reviews the existing
methods and algorithms for determining the main characteristics in
Markov chains, before proposing new approaches based on dynamic
programming and combinatorial methods. Chapter two is dedicated to
infinite horizon stochastic discrete optimal control models and
Markov decision problems with average and expected total discounted
optimization criteria, while Chapter three develops a special
game-theoretical approach to Markov decision processes and
stochastic discrete optimal control problems. In closing, the
book's final chapter is devoted to finite horizon stochastic
control problems and Markov decision processes. The algorithms
developed represent a valuable contribution to the important field
of computational network theory.
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