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Titlebook: Stochastic Decomposition; A Statistical Method Julia L. Higle,Suvrajeet Sen Book 1996 Springer Science+Business Media Dordrecht 1996 Mathem

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書目名稱Stochastic Decomposition
副標題A Statistical Method
編輯Julia L. Higle,Suvrajeet Sen
視頻videohttp://file.papertrans.cn/878/877894/877894.mp4
叢書名稱Nonconvex Optimization and Its Applications
圖書封面Titlebook: Stochastic Decomposition; A Statistical Method Julia L. Higle,Suvrajeet Sen Book 1996 Springer Science+Business Media Dordrecht 1996 Mathem
描述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.
出版日期Book 1996
關(guān)鍵詞Mathematica; Optimization algorithm; Optimization algorithms; STATISTICA; Simulation; algorithms; communic
版次1
doihttps://doi.org/10.1007/978-1-4615-4115-8
isbn_softcover978-1-4613-6845-8
isbn_ebook978-1-4615-4115-8Series ISSN 1571-568X
issn_series 1571-568X
copyrightSpringer Science+Business Media Dordrecht 1996
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Stabilizing Stochastic Decomposition,tes the impact of the procedure for updating the cutting planes. Specifically, while this mechanism ensures that the objective function approximation is asymptotically accurate near the iterates, it also ensures that any given cutting plane will eventually become redundant.
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Book 1996ch 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 t
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Stopping Rules for Stochastic Decomposition,any practical computer implementation requires effective stopping criteria. It is important to recognize that when using sampled data to solve a problem, standard deterministic stopping rules are inadequate. We will develop specialized optimality tests that take advantage of the information generated during the course of the SD algorithm.
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Stabilizing Stochastic Decomposition,proximations developed by an SD algorithm are considerably less accurate than the sample mean function, the SD approximations are sufficiently accurate to ensure asymptotic optimality for a subsequence of iterates. Moreover, the manner in which the objective function approximation is updated as addi
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