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- W1570666476 abstract "Abstract The presentation in this article is in the formal manner of classical applied mathematics and probability in order to focus on the methods and their implementation. In the section titled “Dynamic Programming in Continuous‐Time,” a fairly general model of stochastic dynamic programming in continuous‐time is outlined. In the section titled “Computational Reducing Canonical Forms,” canonical forms, such as a linear dynamics and quadratic cost model in control, that lead to a large reduction in computational effort are given or discussed. In the section titled “Finite Difference PDE Computational Methods,” finite difference partial differential equation (PDE) methods are given that are suitable for solving nonlinear Bellman dynamic programming equations, approximately. Alternatively, Markov chain approximation probabilistic methods which systematically justify the stability and weak convergence of the approximating Markov chain are summarized in the section titled “Probabilistic Markov Chain Approximation.” In the last section, there is a brief summary and directions to some other approaches." @default.
- W1570666476 created "2016-06-24" @default.
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- W1570666476 date "2011-01-01" @default.
- W1570666476 modified "2023-10-16" @default.
- W1570666476 title "Dynamic Programming, Control, and Computation" @default.
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- W1570666476 doi "https://doi.org/10.1002/9780470400531.eorms0171" @default.
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