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Optimization of stochastic discrete ...

Lozovanu, Dmitrii.

Optimization of stochastic discrete systems and control on complex networkscomputational networks /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Optimization of stochastic discrete systems and control on complex networksby Dmitrii Lozovanu, Stefan Pickl.
Reminder of title:	computational networks /
Author:	Lozovanu, Dmitrii.
other author:	Pickl, Stefan.
Published:	Cham :Springer International Publishing :2015.
Description:	xix, 400 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Markov processes.
Online resource:	http://dx.doi.org/10.1007/978-3-319-11833-8
ISBN:	9783319118338 (electronic bk.)

Optimization of stochastic discrete systems and control on complex networkscomputational networks /
Lozovanu, Dmitrii.

Optimization of stochastic discrete systems and control on complex networkscomputational networks /[electronic resource] :by Dmitrii Lozovanu, Stefan Pickl. - Cham :Springer International Publishing :2015. - xix, 400 p. :ill., digital ;24 cm. - Advances in computational management science,v.121388-4301 ;. - Advances in computational management science ;11..

Discrete stochastic processes, numerical methods for Markov chains and polynomial time algorithms -- Stochastic optimal control problems and Markov decision processes with infinite time horizon -- A game-theoretical approach to Markov decision processes, stochastic positional games and multicriteria control models -- Dynamic programming algorithms for finite horizon control problems and Markov decision processes.

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.

ISBN: 9783319118338 (electronic bk.)

Standard No.: 10.1007/978-3-319-11833-8doiSubjects--Topical Terms:

181910
Markov processes.

LC Class. No.: QA274.7 / .L69 2015

Dewey Class. No.: 519.233

Optimization of stochastic discrete systems and control on complex networkscomputational networks /
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520 $a 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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