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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.
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.
Richard Bellmann developed a theory of dynamic programming which is
for many reasons still in the center of great interest. The authors
present a new approach in the ?eld of the optimization and
multi-objective control of time-discrete systems which is closely
related to the work of Richard Bellmann. They develop their own
concept and their extension to the optimization and multi-objective
control of time-discrete systems as well as to dynamic networks and
multilayered structures are very stimulating for further research.
Di?erent perspectives of discrete control and optimal dynamic ?ow
problems on networks are treated and characterized. Together with
the algorithmic solutions a framework of multi-objective control
problems is - rived. The conclusion with a real world example
underlines the necessity and - portance of their theoretic
framework. As they come back to the classical Bellmann concept of
dynamic programming they stress and honor his basic concept without
debase their own work.
Multilayereddecisionprocessesaspartofthedesignandanalysisofcomplexsystems
and networks will be essential in many ways and ?elds in the
future.
Richard Bellmann developed a theory of dynamic programming which is
for many reasons still in the center of great interest. The authors
present a new approach in the ?eld of the optimization and
multi-objective control of time-discrete systems which is closely
related to the work of Richard Bellmann. They develop their own
concept and their extension to the optimization and multi-objective
control of time-discrete systems as well as to dynamic networks and
multilayered structures are very stimulating for further research.
Di?erent perspectives of discrete control and optimal dynamic ?ow
problems on networks are treated and characterized. Together with
the algorithmic solutions a framework of multi-objective control
problems is - rived. The conclusion with a real world example
underlines the necessity and - portance of their theoretic
framework. As they come back to the classical Bellmann concept of
dynamic programming they stress and honor his basic concept without
debase their own work.
Multilayereddecisionprocessesaspartofthedesignandanalysisofcomplexsystems
and networks will be essential in many ways and ?elds in the
future.
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