Table of contents
About this book
Stochastic learning and optimization is a multidisciplinary subject that has wide applications in modern engineering, social, and financial problems, including those in Internet and wireless communications, manufacturing, robotics, logistics, biomedical systems, and investment science. This book is unique in the following aspects.
- (Four areas in one book) This book covers various disciplines in learning and optimization, including perturbation analysis (PA) of discrete-event dynamic systems, Markov decision processes (MDP)s), reinforcement learning (RL), and adaptive control, within a unified framework.
- (A simple approach to MDPs) This book introduces MDP theory through a simple approach based on performance difference formulas. This approach leads to results for the n-bias optimality with long-run average-cost criteria and Blackwell's optimality without discounting.
- (Event-based optimization) This book introduces the recently developed event-based optimization approach, which opens up a research direction in overcoming or alleviating the difficulties due to the curse of dimensionality issue by utilizing the system's special features.
- (Sample-path construction) This book emphasizes physical interpretations based on the sample-path construction.
- Book Title Stochastic Learning and Optimization
- Book Subtitle A Sensitivity-Based Approach
- DOI https://doi.org/10.1007/978-0-387-69082-7
- Copyright Information Springer-Verlag US 2007
- Publisher Name Springer, Boston, MA
- eBook Packages Computer Science Computer Science (R0)
- Hardcover ISBN 978-0-387-36787-3
- Softcover ISBN 978-1-4419-4222-7
- eBook ISBN 978-0-387-69082-7
- Edition Number 1
- Number of Pages XX, 566
- Number of Illustrations 119 b/w illustrations, 0 illustrations in colour
Discrete Mathematics in Computer Science
Control and Systems Theory
Calculus of Variations and Optimal Control; Optimization
Probability Theory and Stochastic Processes
- Buy this book on publisher's site
- Industry Sectors
- IT & Software
From the reviews:
"The book is written by known contributor to the theory of Markov decision problems and the theory of queueing systems and it is chiefly based on recent results obtained by the author. … The book provide good introductory materials for graduate students and engineers who wish to have an overview of learning and optimization theory, the related methodologies in different disciplines and their relations. Moreover, the book is useful in finding new research topics and in practical applications." (Vladimir Sobolev, Zentralblatt MATH, Vol. 1130, 2008)
"The systems studied in this book are stochastic dynamic systems … . The book is very well written, and … they are often presented in an intuitive way so that the study is really enjoyable. … the subject of the book is very important and very interesting. … It is intended for teachers, researchers, and graduate students who can recognize the practical and theoretical value of the methods described … . strongly recommended for scholars in engineering, mathematics, computer science, artificial intelligence, and machine learning." (Lefteris Angelis, ACM Computing Reviews, Vol. 49 (12), December, 2008)
"The key point of this monograph is perturbation analysis … . The book has appendices on Markov processes, stochastic matrices and queueing theory. Every chapter contains a number of problems for self-study. Along with known/proved statements, the reader can find many open problems for future research. Finally, the book can become the basis for several undergraduate lecture courses." (Aleksey B. Piunovskiy, Mathematical Reviews, Issue 2009 f)