Stochastic Learning and Optimization: A Sensitivity-Based Approach

Cao, Xi-Ren

ISBN 10: 038736787X / ISBN 13: 9780387367873

Editore: Springer-Verlag New York Inc, 2007

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1st edition. 566 pages. 8.75x6.50x1.75 inches. In Stock.

Codice articolo x-038736787X

Dati bibliografici

• Titolo: Stochastic Learning and Optimization: A Sensitivity-Based Approach
• Editore: Springer-Verlag New York Inc
• Data di pubblicazione: 2007
• Lingua: Inglese
• ISBN 10: 038736787X
• ISBN 13: 9780387367873
• Rilegatura: Hardcover
• Condizione: Brand New
• Peso: 1,11 chilogrammi

Descrizione articolo

Riassunto

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.

Dalla quarta di copertina

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.

1. (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.
2. (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.
3. (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.
4. (Sample-path construction) This book emphasizes physical interpretations based on the sample-path construction.