Learning for Decision and Control in Stochastic Networks (Record no. 85720)
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000 -LEADER | |
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fixed length control field | 03138nam a22006015i 4500 |
001 - CONTROL NUMBER | |
control field | 978-3-031-31597-8 |
005 - DATE AND TIME OF LATEST TRANSACTION | |
control field | 20240730164505.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 230619s2023 sz | s |||| 0|eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
ISBN | 9783031315978 |
-- | 978-3-031-31597-8 |
082 04 - CLASSIFICATION NUMBER | |
Call Number | 621.3821 |
082 04 - CLASSIFICATION NUMBER | |
Call Number | 004.6 |
100 1# - AUTHOR NAME | |
Author | Huang, Longbo. |
245 10 - TITLE STATEMENT | |
Title | Learning for Decision and Control in Stochastic Networks |
250 ## - EDITION STATEMENT | |
Edition statement | 1st ed. 2023. |
300 ## - PHYSICAL DESCRIPTION | |
Number of Pages | XI, 71 p. 8 illus., 7 illus. in color. |
490 1# - SERIES STATEMENT | |
Series statement | Synthesis Lectures on Learning, Networks, and Algorithms, |
505 0# - FORMATTED CONTENTS NOTE | |
Remark 2 | Introduction -- The Stochastic Network Model -- Network Optimization Techniques -- Learning Network Decisions -- Summary and Discussions. |
520 ## - SUMMARY, ETC. | |
Summary, etc | This book introduces the Learning-Augmented Network Optimization (LANO) paradigm, which interconnects network optimization with the emerging AI theory and algorithms and has been receiving a growing attention in network research. The authors present the topic based on a general stochastic network optimization model, and review several important theoretical tools that are widely adopted in network research, including convex optimization, the drift method, and mean-field analysis. The book then covers several popular learning-based methods, i.e., learning-augmented drift, multi-armed bandit and reinforcement learning, along with applications in networks where the techniques have been successfully applied. The authors also provide a discussion on potential future directions and challenges. |
856 40 - ELECTRONIC LOCATION AND ACCESS | |
Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-31597-8 |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Koha item type | eBooks |
264 #1 - | |
-- | Cham : |
-- | Springer International Publishing : |
-- | Imprint: Springer, |
-- | 2023. |
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-- | text |
-- | txt |
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337 ## - | |
-- | computer |
-- | c |
-- | rdamedia |
338 ## - | |
-- | online resource |
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347 ## - | |
-- | text file |
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-- | rda |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computer Networks. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Stochastic processes. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Machine learning. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Application software. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computer science. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Mathematical optimization. |
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computer Networks. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Stochastic Networks. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Machine Learning. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computer and Information Systems Applications. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computer Science. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Optimization. |
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
-- | 2690-4314 |
912 ## - | |
-- | ZDB-2-SXSC |
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