astic control (and uncontrolled) problems of current interest, with di ffusion, jump-diffusion, or reflected diffusion models. There is a com plete coverage of the standard models as well as of ergodic and singul ar control, the types of reflected diffusion models that appear as mod els of controlled queuing networks, and the approximation of optimal n onlinear filters. There are two new chapters concerning problems with jump or variance control. The methods are powerful tools for determini stic problems as well, and there is a greatly expanded development of such problems, with particular emphasis on complex problems arising in the calculus of variations. Convergence is proved via the efficient p robabilistic methods of weak convergence theory. A weak local consiste ncy is the essential condition. The required background is surveyed, a nd there is an extensive development of methods of approximation compu tational algorithms. The book is written on two levels: algorithms and applications, and mathematical proofs. Thus, the ideas should be very accessible to a broad audience.
|Publisher:||Springer New York|
|Series:||Stochastic Modelling and Applied Probability Series , #24|
|Edition description:||2nd ed. 2001|
|Product dimensions:||6.10(w) x 9.25(h) x 0.04(d)|
Table of Contents