sulcus 发表于 2025-3-26 21:04:28

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collateral 发表于 2025-3-27 04:43:00

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STYX 发表于 2025-3-27 05:16:49

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VEST 发表于 2025-3-27 12:30:41

Stochastic Programming Approximations Using Limited Moment Information, with Application to Asset Ad moment information of the underlying random vectors to approximate the expected recourse function. Bounds that use first-order moments, as well as higher-order moments are described in the context of saddle functions and specialized for convex recourse functions. Relation of these bounds to genera

Dictation 发表于 2025-3-27 13:53:05

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Nuance 发表于 2025-3-27 19:10:16

Risk Aversion in Two-Stage Stochastic Integer Programming,sulting mean–risk stochastic integer programs emphasis is placed on structural properties of these optimization problems and on algorithms for their solution. Bibliographical notes conclude the Chapter.

PAEAN 发表于 2025-3-27 23:19:08

Portfolio Optimization with Risk Control by Stochastic Dominance Constraints,pproach based on stochastic dominance. The portfolio return rate in this model is required to stochastically dominate a random benchmark, such as an index, or reference portfolio return. We formulate optimality conditions and duality relations for these models and construct equivalent optimization m

Surgeon 发表于 2025-3-28 05:43:51

,Mean–Absolute Deviation Model,large–scale portfolio optimization problems. MAD model has been used for solving huge portfolio optimization models including internationally diversified investment model, long-term ALM model, mortgage–backed security portfolio optimization model. Also, the MAD model enjoys several nice theoretical

游行 发表于 2025-3-28 08:30:06

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gruelling 发表于 2025-3-28 13:57:45

,Growth–Security Models and Stochastic Dominance,nother, then the ordering of random variables is required. In this paper the levels of stochastic dominance for random variables are used to define bi-criteria problems for determining an efficient investment strategy. The criteria are characterized as growth and security, respectively, and produce
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查看完整版本: Titlebook: Stochastic Programming; The State of the Art Gerd Infanger Book 2011 Springer Science+Business Media, LLC 2011