Titlebook: Risk-Averse Optimization and Control; Theory and Methods Darinka Dentcheva,Andrzej Ruszczyński Book 2024 The Editor(s) (if applicable) and

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Dynamic Risk Optimization,, the risk evaluation and decision-making should form a cohesive approach. This implies that at each stage ., we need to be able to evaluate the risk of the current and future outcomes ., …, .. The theory of dynamic risk is concerned with consistent ways of obtaining such evaluations and using them in optimization models.

山間窄路

Risk-Averse Control of Markov Systems,idea of our approach is to exploit the structure of the canonical probability space to represent conditional risk measures by simpler structures, called transition risk mappings, which evaluate the risk of a function of the system’s state.

浮雕

978-3-031-57990-5The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

FACT

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Optimization of Measures of Risk,Suppose a mapping ., where .?≥?1, describes the dependence of a random “cost” in a stochastic system on decision variables ., that is, [.(.)](.) is the cost associated with decision . and elementary event .?∈?..

Decrepit

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Numerical Methods for Problems with Stochastic Dominance Constraints,In this chapter, we discuss numerical solution of problems with second-order stochastic dominance constraints. Because of the involved nature of these constraints, specialized methods need to be designed, which are not simple adaptations of general optimization techniques.

MUT

Darinka Dentcheva,Andrzej RuszczyńskiComprehensive presentation of the rapidly growing fields of risk-averse optimization and control.Self-contained presentation of the theory of measures of risk.Many examples included

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Springer Series in Operations Research and Financial Engineeringhttp://image.papertrans.cn/r/image/830777.jpg

exigent

https://doi.org/10.1007/978-3-031-57988-2Risk-Averse Optimization; Risk Measures; Risk-Averse Dynamic Programming; Stochastic Dominance,; Risk-Av