Titlebook: Numerical Methods for Stochastic Control Problems in Continuous Time; Harold J. Kushner,Paul G. Dupuis Book 19921st edition Springer-Verla

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Proponent

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Controlled Markov Chains,n controlled finite state Markov chains. In this chapter, we will define some of the canonical control problems for the Markov chain models which will be used in the sequel as “approximating processes.” The cost functions will be defined. The functional equations which are satisfied by these cost fu

Champion

做作

The Markov Chain Approximation Method: Introduction,f functionals of the stochastic processes of interest, of the type described in Chapters 3, 7, 8, 9, 12, and 13. It was shown in Chapter 3 that the cost or optimal cost functionals can be the (at least formal) solutions to certain nonlinear partial differential equations. It is tempting to try to so

DIS

Juvenile

Computational Methods for Controlled Markov Chains,d value function for the approximating Markov chain models. We concentrate on methods for problems which are of interest over a potentially unbounded time interval. Numerical methods for the ergodic problem will be discussed in Chapter 7, and are simple modifications of the ideas of this chapter. So

吞下

The Ergodic Cost Problem: Formulation and Algorithms, useful to discuss the appropriate dynamic programming equations and some additional background material. The natural state spaces for control problems that are of interest over a long time interval are often unbounded, and they must be truncated for numerical purposes. One standard way of doing thi

guzzle

Medley

Convergence Proofs, discounted problems with absorbing boundaries; diffusion and jump diffusion models; optimal stopping problems, and problems where we stop on hitting a target set and where there is no discounting. The convergence results for the case of reflecting boundaries and the singular and ergodic control pro