Stochastic Learning and Optimization
A Sensitivity-Based Approach
(Sprache: Englisch)
Performance optimization is vital in the design and operation of modern engineering systems. This book provides a unified framework based on a sensitivity point of view. It introduces new approaches and proposes new research topics.
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Produktinformationen zu „Stochastic Learning and Optimization “
Performance optimization is vital in the design and operation of modern engineering systems. This book provides a unified framework based on a sensitivity point of view. It introduces new approaches and proposes new research topics.
Klappentext zu „Stochastic Learning and Optimization “
Performance optimization is vital in the design and operation of modern engineering systems, including communications, manufacturing, robotics, and logistics. Most engineering systems are too complicated to model, or the system parameters cannot be easily identified, so learning techniques have to be applied. This book provides a unified framework based on a sensitivity point of view. It also introduces new approaches and proposes new research topics within this sensitivity-based framework. This new perspective on a popular topic is presented by a well respected expert in the field.
Performance optimization is very important in designing and operating modern engineering systems in many areas, including communications (Internet and wireless), manufacturing, robotics, and logistics. Most engineering systems are too complicated to model, or the systems parameters cannot be easily identified. Therefore, learning techniques have to be applied. Learning and optimization of stochastic systems is a multi-disciplinary area which has been attracting wide attention from researchers in many disciplines including control systems, operations research, and computer science. Areas such as perturbation analysis (PA) in discrete event dynamic systems (DEDSs), Markov decision processes (MDPs) in operations research, reinforcement learning (RL) or neuro-dynamic programming (NDP) in computer science, identification and adaptive control (I&AC) in control systems, share the common goal: to make the "best decision" to optimize the system performance.
Different areas take different perspectives and have different formulations for the same goal. This book provides an overview for the above areas with a unified framework based on a sensitivity point of view. It also introduces new approaches and proposes new research topics within this sensitivity-based framework.
Different areas take different perspectives and have different formulations for the same goal. This book provides an overview for the above areas with a unified framework based on a sensitivity point of view. It also introduces new approaches and proposes new research topics within this sensitivity-based framework.
Inhaltsverzeichnis zu „Stochastic Learning and Optimization “
- Perturbation Analysis of Queueing Systems- Perturbation Analysis of Markov Chains
- Performance Sensitivities of Markov Processes
- Learning and Optimization with Perturbation Analysis
- Markov Decision Processes
- Ergodic Systems
- Sample-Path-Based Policy Iteration
- Reinforcement Learning
- Stochastic Approximation
- Temporal Difference Methods
- Identification and Adaptive Control
- Constructing Sensitivity Formulas
- Event-Based Optimization of Markov Systems
- Estimating Aggregated Potentials
Bibliographische Angaben
- Autor: Xi-Ren Cao
- 2007, 566 Seiten, Maße: 16 x 24,1 cm, Gebunden, Englisch
- Verlag: Springer
- ISBN-10: 038736787X
- ISBN-13: 9780387367873
- Erscheinungsdatum: 12.10.2007
Sprache:
Englisch
Rezension zu „Stochastic Learning and Optimization “
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)
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