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Stochastic Learning and Optimization A Sensitivity-Based Approach /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York, NY :
Springer US : Imprint: Springer,
2007.
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| Edición: | 1st ed. 2007. |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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|---|---|---|---|
| 001 | 978-0-387-69082-7 | ||
| 003 | DE-He213 | ||
| 005 | 20220112194546.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 100301s2007 xxu| s |||| 0|eng d | ||
| 020 | |a 9780387690827 |9 978-0-387-69082-7 | ||
| 024 | 7 | |a 10.1007/978-0-387-69082-7 |2 doi | |
| 050 | 4 | |a QA76.9.M35 | |
| 050 | 4 | |a QA297.4 | |
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| 082 | 0 | 4 | |a 004.0151 |2 23 |
| 100 | 1 | |a Cao, Xi-Ren. |e author. |0 (orcid)0000-0001-5165-8804 |1 https://orcid.org/0000-0001-5165-8804 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Stochastic Learning and Optimization |h [electronic resource] : |b A Sensitivity-Based Approach / |c by Xi-Ren Cao. |
| 250 | |a 1st ed. 2007. | ||
| 264 | 1 | |a New York, NY : |b Springer US : |b Imprint: Springer, |c 2007. | |
| 300 | |a XX, 566 p. 119 illus. With 212 Problems. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 505 | 0 | |a 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. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Computer science-Mathematics. | |
| 650 | 0 | |a Discrete mathematics. | |
| 650 | 0 | |a Engineering design. | |
| 650 | 0 | |a Control engineering. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Mathematical optimization. | |
| 650 | 0 | |a Calculus of variations. | |
| 650 | 0 | |a Probabilities. | |
| 650 | 1 | 4 | |a Discrete Mathematics in Computer Science. |
| 650 | 2 | 4 | |a Engineering Design. |
| 650 | 2 | 4 | |a Control and Systems Theory. |
| 650 | 2 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Calculus of Variations and Optimization. |
| 650 | 2 | 4 | |a Probability Theory. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9780387515298 |
| 776 | 0 | 8 | |i Printed edition: |z 9781441942227 |
| 776 | 0 | 8 | |i Printed edition: |z 9780387367873 |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-0-387-69082-7 |z Texto Completo |
| 912 | |a ZDB-2-SCS | ||
| 912 | |a ZDB-2-SXCS | ||
| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||