Stochastic Learning and Optimization: A Sensitivity-Based Approach

Paperback;588;155;235;31... Ausführliche Beschreibung

1. Person: Cao, Xi-Ren
Format: Buch
Sprache: English
Veröffentlicht: Springer US 2007
Beschreibung: 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.
Schlagworte: Computer
Markov Chains
Markov decision processes
Operations Research
ergodic systems
event based optimization
identification and adaptive control
perturbation analysis
queueing systems
reinforcement learning
stochastic approximation
Online Zugang: Volltext
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