Online stochastic combinatorial optimization / (Record no. 73007)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03730nam a2200553 i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 6267352 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220712204638.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 151223s2009 maua ob 001 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9780262257152 |
| -- | ebook |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | electronic |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 003 |
| 100 1# - AUTHOR NAME | |
| Author | Van Hentenryck, Pascal, |
| 245 10 - TITLE STATEMENT | |
| Title | Online stochastic combinatorial optimization / |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 1 PDF (xiii, 232 pages) : |
| 500 ## - GENERAL NOTE | |
| Remark 1 | "Multi-User" |
| 500 ## - GENERAL NOTE | |
| Remark 1 | Academic Complete Subscription 2011-2012 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Online decision making under uncertainty and time constraints represents one of the most challenging problems for robust intelligent agents. In an increasingly dynamic, interconnected, and real-time world, intelligent systems must adapt dynamically to uncertainties, update existing plans to accommodate new requests and events, and produce high-quality decisions under severe time constraints. Such online decision-making applications are becoming increasingly common: ambulance dispatching and emergency city-evacuation routing, for example, are inherently online decision-making problems; other applications include packet scheduling for Internet communications and reservation systems. This book presents a novel framework, online stochastic optimization, to address this challenge.This framework assumes that the distribution of future requests, or an approximation thereof, is available for sampling, as is the case in many applications that make either historical data or predictive models available. It assumes additionally that the distribution of future requests is independent of current decisions, which is also the case in a variety of applications and holds significant computational advantages. The book presents several online stochastic algorithms implementing the framework, provides performance guarantees, and demonstrates a variety of applications. It discusses how to relax some of the assumptions in using historical sampling and machine learning and analyzes different underlying algorithmic problems. And finally, the book discusses the framework's possible limitations and suggests directions for future research. |
| 650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| General subdivision | System Theory. |
| 650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| General subdivision | Operations Research. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Bent, Russell. |
| 856 42 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267352 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cambridge, Massachusetts : |
| -- | MIT Press, |
| -- | c2006. |
| 264 #2 - | |
| -- | [Piscataqay, New Jersey] : |
| -- | IEEE Xplore, |
| -- | [2009] |
| 336 ## - | |
| -- | text |
| -- | rdacontent |
| 337 ## - | |
| -- | electronic |
| -- | isbdmedia |
| 338 ## - | |
| -- | online resource |
| -- | rdacarrier |
| 588 ## - | |
| -- | Description based on PDF viewed 12/23/2015. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Stochastic processes. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Combinatorial optimization. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Online algorithms. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Operations research. |
| 650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | SCIENCE |
| 650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | TECHNOLOGY & ENGINEERING |
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