Titlebook: Computational Stochastic Programming; Models, Algorithms, Lewis Ntaimo Book 2024 Springer Nature Switzerland AG 2024 Mean-risk linear and

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書目名稱Computational Stochastic Programming影響因子(影響力)




書目名稱Computational Stochastic Programming影響因子(影響力)學科排名




書目名稱Computational Stochastic Programming網(wǎng)絡(luò)公開度




書目名稱Computational Stochastic Programming網(wǎng)絡(luò)公開度學科排名




書目名稱Computational Stochastic Programming被引頻次




書目名稱Computational Stochastic Programming被引頻次學科排名




書目名稱Computational Stochastic Programming年度引用




書目名稱Computational Stochastic Programming年度引用學科排名




書目名稱Computational Stochastic Programming讀者反饋




書目名稱Computational Stochastic Programming讀者反饋學科排名

Monocle

Book 2024tation. The purpose of this book is to provide a foundational and thorough treatment of the subject with a focus on models and algorithms and their computer implementation. The book’s most important features include a focus on both risk-neutral and risk-averse models, a variety of real-life example

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https://doi.org/10.1007/978-94-6300-902-7also use it in later chapters of the book. In this chapter, we begin with illustrations of deterministic models applied to the numerical example and then move on to risk-neutral stochastic models. We end the chapter with illustrations of risk-averse models introduced in the previous chapter.

OASIS

Junyi Zhang,Wonchul Kim,Akimasa Fujiwaratrices, we provide a review of sparse matrix formats in Sect. 10.3. We discuss program design for algorithm implementation and testing in Sect. 10.4 and end the chapter with a review of empirical analysis, methods of analysis, test problems, and reporting computational results in Sect. 10.5.

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Modeling and Illustrative Numerical Examplesalso use it in later chapters of the book. In this chapter, we begin with illustrations of deterministic models applied to the numerical example and then move on to risk-neutral stochastic models. We end the chapter with illustrations of risk-averse models introduced in the previous chapter.

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阻塞

Sampling-Based Stochastic Linear Programming Methodsin which sequential sampling is done to solve the approximation problem. We illustrate interior sampling with the basic stochastic decomposition (SD) method for MR-SLP. Since we place emphasis on algorithm computer implementation, we also discuss how to generate random samples from the instance data.

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Melodrama

Paul Emeka Okeke,Isunueo Benedicta Omeghien different classes of SP, i.e., stochastic linear programming (SLP), stochastic mixed-integer programming (SMIP), and probabilistically constrained stochastic programming (PC-SP). We provide simplified problem formulations with a focus on how to model the key elements of the problem.