Stochastic Modeling and Optimization

The objective of this volume is to highlight through a collection of chap­ ters some of the recent research works in applied prob ability, specifically stochastic modeling and optimization. The volume is organized loosely into four parts. The first part is a col­ lection of several basic methodologies: singularly perturbed Markov chains (Chapter 1), and related applications in stochastic optimal control (Chapter 2); stochastic approximation, emphasizing convergence properties (Chapter 3); a performance-potential based approach to Markov decision program­ ming (Chapter 4); and interior-point techniques (homogeneous self-dual embedding and central path following) applied to stochastic programming (Chapter 5). The three chapters in the second part are concerned with queueing the­ ory. Chapters 6 and 7 both study processing networks - a general dass of queueing networks - focusing, respectively, on limit theorems in the form of strong approximation, and the issue of stability via connections to re­ lated fluid models. The subject of Chapter 8 is performance asymptotics via large deviations theory, when the input process to a queueing system exhibits long-range dependence, modeled as fractional Brownian motion.

From the reviews:

"The Workshop Stochastic Models and Optimization ... in May 2001, forms the basis of the present volume. 14 papers from about 60 presentations at the workshop were selected and thoroughly revised making self-contained chapters of a book for a broad audience. It highlighted some recent advances in applied probability achieved mainly by scientists with Chinese background. ... The book seems to be very suitable for seminar studies at the graduate level." (Hans-Joachim Girlich, OR News, 25, November 2005)