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| Título : |
Optimization of Stochastic Discrete Systems and Control on Complex Networks : Computational Networks |
| Tipo de documento: |
documento electrónico |
| Autores: |
Dmitrii Lozovanu ; SpringerLink (Online service) ; Stefan Wolfgang Pickl |
| Editorial: |
Cham : Springer International Publishing |
| Fecha de publicación: |
2015 |
| Otro editor: |
Imprint: Springer |
| Colección: |
Advances in Computational Management Science, ISSN 1388-4301 num. 12 |
| Número de páginas: |
XIX, 400 p. 54 illus |
| Il.: |
online resource |
| ISBN/ISSN/DL: |
978-3-319-11833-8 |
| Idioma : |
Inglés (eng) |
| Palabras clave: |
Business Operations research Decision making Algorithms Mathematical optimization Management science and Operation Research/Decision Theory Optimization Research, Science Discrete Algorithm Analysis Problem Complexity |
| Clasificación: |
658 Empresas. Organización de empresas |
| Resumen: |
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 |
| Nota de contenido: |
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 |
| En línea: |
http://dx.doi.org/10.1007/978-3-319-11833-8 |
| Link: |
https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35502 |
Optimization of Stochastic Discrete Systems and Control on Complex Networks : Computational Networks [documento electrónico] / Dmitrii Lozovanu ; SpringerLink (Online service) ; Stefan Wolfgang Pickl . - Cham : Springer International Publishing : Imprint: Springer, 2015 . - XIX, 400 p. 54 illus : online resource. - ( Advances in Computational Management Science, ISSN 1388-4301; 12) . ISBN : 978-3-319-11833-8 Idioma : Inglés ( eng)
| Palabras clave: |
Business Operations research Decision making Algorithms Mathematical optimization Management science and Operation Research/Decision Theory Optimization Research, Science Discrete Algorithm Analysis Problem Complexity |
| Clasificación: |
658 Empresas. Organización de empresas |
| Resumen: |
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 |
| Nota de contenido: |
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 |
| En línea: |
http://dx.doi.org/10.1007/978-3-319-11833-8 |
| Link: |
https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35502 |
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