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Numerical methods for stochastic control problems in continuous time. (English) Zbl 0754.65068
Applications of Mathematics. 24. New York etc.: Springer-Verlag. ix, 439 p. (1992).
The book contains 14 chapters: 1. Review of continuous time models. 2. Controlled Markov chains. 3. Dynamic programming equations. 4. The Markov chain approximation method: Introduction. 5. Construction of the approximating Markov chain. 6. Computational methods for controlled Markov chains. 7. The ergodic cost problems: Formulations and algorithms. 8. Heavy traffic and singular control problems: Examples and Markov chain approximations. 9. Weak convergence and the characterization of processes. 10. Convergence proofs. 11. Convergence for reflecting boundaries, singular control and ergodic cost problems. 12. Finite time problems and nonlinear filtering. 13. Problems from the calculus of variations. 14. The viscosity solution approach to proving convergence of numerical schemes.
The essential idea consists in the approximation of time continuous control problems — driven by Ito equations — by sequences of controlled Markov chains. The controlled Markov chains are constructed by a discretization of controlled Ito equations. The convergence is proved. The book also contains many examples and numerical methods (especially chapter 6).

MSC:
65K10 Numerical optimization and variational techniques
65-02 Research exposition (monographs, survey articles) pertaining to numerical analysis
93E20 Optimal stochastic control
65C99 Probabilistic methods, stochastic differential equations
60F17 Functional limit theorems; invariance principles
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