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Optimization of stochastic discrete ...

Lozovanu, Dmitrii.

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Optimization of stochastic discrete systems and control on complex networks = computational networks /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Optimization of stochastic discrete systems and control on complex networks/ by Dmitrii Lozovanu, Stefan Pickl.
其他題名:	computational networks /
作者:	Lozovanu, Dmitrii.
其他作者:	Pickl, Stefan.
出版者:	Cham :Springer International Publishing : : 2015.,
面頁冊數:	xix, 400 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Markov processes. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-11833-8
ISBN:	9783319118338 (electronic bk.)

Optimization of stochastic discrete systems and control on complex networks = computational 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:

532104
Markov processes.

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

Dewey Class. No.: 519.233

Optimization of stochastic discrete systems and control on complex networks = computational 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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