Stochastic Decomposition: A Statistical Method for Large Scale Stochastic Linear Programming
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
1996
|
| Schriftenreihe: | Nonconvex Optimization and Its Applications
8 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | Motivation Stochastic Linear Programming with recourse represents one of the more widely applicable models for incorporating uncertainty within in which the SLP optimization models. There are several arenas model is appropriate, and such models have found applications in air line yield management, capacity planning, electric power generation planning, financial planning, logistics, telecommunications network planning, and many more. In some of these applications, modelers represent uncertainty in terms of only a few seenarios and formulate a large scale linear program which is then solved using LP software. However, there are many applications, such as the telecommunications planning problem discussed in this book, where a handful of seenarios do not capture variability well enough to provide a reasonable model of the actual decision-making problem. Problems of this type easily exceed the capabilities of LP software by several orders of magnitude. Their solution requires the use of algorithmic methods that exploit the structure of the SLP model in a manner that will accommodate large scale applications |
| Beschreibung: | 1 Online-Ressource (XXIV, 222 p) |
| ISBN: | 9781461541158 9781461368458 |
| ISSN: | 1571-568X |
| DOI: | 10.1007/978-1-4615-4115-8 |
Internformat
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| 500 | |a Motivation Stochastic Linear Programming with recourse represents one of the more widely applicable models for incorporating uncertainty within in which the SLP optimization models. There are several arenas model is appropriate, and such models have found applications in air line yield management, capacity planning, electric power generation planning, financial planning, logistics, telecommunications network planning, and many more. In some of these applications, modelers represent uncertainty in terms of only a few seenarios and formulate a large scale linear program which is then solved using LP software. However, there are many applications, such as the telecommunications planning problem discussed in this book, where a handful of seenarios do not capture variability well enough to provide a reasonable model of the actual decision-making problem. Problems of this type easily exceed the capabilities of LP software by several orders of magnitude. Their solution requires the use of algorithmic methods that exploit the structure of the SLP model in a manner that will accommodate large scale applications | ||
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Datensatz im Suchindex
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| author | Higle, Julia L. |
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| author_variant | j l h jl jlh |
| building | Verbundindex |
| bvnumber | BV042420879 |
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| dewey-full | 519.6 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.6 |
| dewey-search | 519.6 |
| dewey-sort | 3519.6 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| doi_str_mv | 10.1007/978-1-4615-4115-8 |
| format | Electronic eBook |
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| institution | BVB |
| isbn | 9781461541158 9781461368458 |
| issn | 1571-568X |
| language | English |
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| psigel | ZDB-2-SMA ZDB-2-BAE ZDB-2-SMA_Archive |
| publishDate | 1996 |
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| publisher | Springer US |
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| series2 | Nonconvex Optimization and Its Applications |
| spelling | Higle, Julia L. Verfasser aut Stochastic Decomposition A Statistical Method for Large Scale Stochastic Linear Programming by Julia L. Higle, Suvrajeet Sen Boston, MA Springer US 1996 1 Online-Ressource (XXIV, 222 p) txt rdacontent c rdamedia cr rdacarrier Nonconvex Optimization and Its Applications 8 1571-568X Motivation Stochastic Linear Programming with recourse represents one of the more widely applicable models for incorporating uncertainty within in which the SLP optimization models. There are several arenas model is appropriate, and such models have found applications in air line yield management, capacity planning, electric power generation planning, financial planning, logistics, telecommunications network planning, and many more. In some of these applications, modelers represent uncertainty in terms of only a few seenarios and formulate a large scale linear program which is then solved using LP software. However, there are many applications, such as the telecommunications planning problem discussed in this book, where a handful of seenarios do not capture variability well enough to provide a reasonable model of the actual decision-making problem. Problems of this type easily exceed the capabilities of LP software by several orders of magnitude. Their solution requires the use of algorithmic methods that exploit the structure of the SLP model in a manner that will accommodate large scale applications Mathematics Systems theory Mathematical optimization Operations research Optimization Operation Research/Decision Theory Systems Theory, Control Mathematik Algorithmus (DE-588)4001183-5 gnd rswk-swf Dekomposition (DE-588)4149030-7 gnd rswk-swf Stochastische Optimierung (DE-588)4057625-5 gnd rswk-swf Stochastische lineare Optimierung (DE-588)4183378-8 gnd rswk-swf Stochastische Optimierung (DE-588)4057625-5 s Algorithmus (DE-588)4001183-5 s 1\p DE-604 Stochastische lineare Optimierung (DE-588)4183378-8 s Dekomposition (DE-588)4149030-7 s 2\p DE-604 Sen, Suvrajeet Sonstige oth https://doi.org/10.1007/978-1-4615-4115-8 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Higle, Julia L. Stochastic Decomposition A Statistical Method for Large Scale Stochastic Linear Programming Mathematics Systems theory Mathematical optimization Operations research Optimization Operation Research/Decision Theory Systems Theory, Control Mathematik Algorithmus (DE-588)4001183-5 gnd Dekomposition (DE-588)4149030-7 gnd Stochastische Optimierung (DE-588)4057625-5 gnd Stochastische lineare Optimierung (DE-588)4183378-8 gnd |
| subject_GND | (DE-588)4001183-5 (DE-588)4149030-7 (DE-588)4057625-5 (DE-588)4183378-8 |
| title | Stochastic Decomposition A Statistical Method for Large Scale Stochastic Linear Programming |
| title_auth | Stochastic Decomposition A Statistical Method for Large Scale Stochastic Linear Programming |
| title_exact_search | Stochastic Decomposition A Statistical Method for Large Scale Stochastic Linear Programming |
| title_full | Stochastic Decomposition A Statistical Method for Large Scale Stochastic Linear Programming by Julia L. Higle, Suvrajeet Sen |
| title_fullStr | Stochastic Decomposition A Statistical Method for Large Scale Stochastic Linear Programming by Julia L. Higle, Suvrajeet Sen |
| title_full_unstemmed | Stochastic Decomposition A Statistical Method for Large Scale Stochastic Linear Programming by Julia L. Higle, Suvrajeet Sen |
| title_short | Stochastic Decomposition |
| title_sort | stochastic decomposition a statistical method for large scale stochastic linear programming |
| title_sub | A Statistical Method for Large Scale Stochastic Linear Programming |
| topic | Mathematics Systems theory Mathematical optimization Operations research Optimization Operation Research/Decision Theory Systems Theory, Control Mathematik Algorithmus (DE-588)4001183-5 gnd Dekomposition (DE-588)4149030-7 gnd Stochastische Optimierung (DE-588)4057625-5 gnd Stochastische lineare Optimierung (DE-588)4183378-8 gnd |
| topic_facet | Mathematics Systems theory Mathematical optimization Operations research Optimization Operation Research/Decision Theory Systems Theory, Control Mathematik Algorithmus Dekomposition Stochastische Optimierung Stochastische lineare Optimierung |
| url | https://doi.org/10.1007/978-1-4615-4115-8 |
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