Titlebook: Dynamic Optimization; Deterministic and St Karl Hinderer,Ulrich Rieder,Michael Stieglitz Textbook 2016 Springer International Publishing AG

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Markovian Decision Processes with Discrete Transition LawWe consider MDPs with countable state spaces and variable discount factors. The discount factor may depend on the state and the action. Under minimal assumptions we prove the reward iteration and formulate a structure theorem for MDPs. Also the useful notion of a bounding function is introduced.

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Examples with Discrete Disturbances and with Discrete Transition LawIn this chapter we apply the general theorems from Chap.?. to special examples. In particular, we consider a production-inventory problem with backlogging and delivery lag and a queueing model with arrival control.

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Dynamic Optimization978-3-319-48814-1Series ISSN 0172-5939 Series E-ISSN 2191-6675

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https://doi.org/10.1007/978-3-642-61538-2scription of the general problem, and derive the basic solution technique: value iteration and optimality criterion. This allows us to derive structural properties of the solution of the allocation problem.

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Die Neuronenmaschinerie des Gehirns,of considerable importance. Here we study the monotonicity of ..(.) in . and, with arbitrary relations on ., also in .. Examples of structural properties considered in later sections are concavity and Lipschitz continuity of the value function. As a first benefit of each structural result it can ser

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,?,al states .) can be established by ad hoc methods. The existence of a maximizer at each stage is also obvious if the set .(.) of admissible actions is finite for all .. In Proposition?. we gave a result which covers many applications where . and . are intervals. The existence problem for maximizers