Optimization of Stochastic Discrete Systems and Control on Complex Networks

Computational Networks

Business & Finance, Management & Leadership, Operations Research, Nonfiction, Science & Nature, Mathematics, Applied
Cover of the book Optimization of Stochastic Discrete Systems and Control on Complex Networks by Dmitrii Lozovanu, Stefan Pickl, Springer International Publishing
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Author: Dmitrii Lozovanu, Stefan Pickl ISBN: 9783319118338
Publisher: Springer International Publishing Publication: November 27, 2014
Imprint: Springer Language: English
Author: Dmitrii Lozovanu, Stefan Pickl
ISBN: 9783319118338
Publisher: Springer International Publishing
Publication: November 27, 2014
Imprint: Springer
Language: English

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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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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