Stochastic Learning and Optimization : A Sensitivity-Based Approach /
By: Cao, Xi-Ren [author.].
Contributor(s): SpringerLink (Online service).
Material type:
BookPublisher: Boston, MA : Springer US, 2007.Description: XX, 566 p. 119 illus. With 212 Problems. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9780387690827.Subject(s): Computer science | Computer science -- Mathematics | Artificial intelligence | Calculus of variations | Probabilities | Engineering design | Control engineering | Computer Science | Discrete Mathematics in Computer Science | Engineering Design | Control | Artificial Intelligence (incl. Robotics) | Calculus of Variations and Optimal Control; Optimization | Probability Theory and Stochastic ProcessesDDC classification: 004.0151 Online resources: Click here to access online | Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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E books
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PK Kelkar Library, IIT Kanpur | Available | EBK1426 |
Four Disciplines in Learning and Optimization -- Perturbation Analysis -- Learning and Optimization with Perturbation Analysis -- Markov Decision Processes -- Sample-Path-Based Policy Iteration -- Reinforcement Learning -- Adaptive Control Problems as MDPs -- The Event-Based Optimization - A New Approach -- Event-Based Optimization of Markov Systems -- Constructing Sensitivity Formulas.
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.
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