Risk-Averse Optimization and Control Theory and Methods /
This book offers a comprehensive presentation of the theory and methods of risk-averse optimization and control. Problems of this type arise in finance, energy production and distribution, supply chain management, medicine, and many other areas, where not only the average performance of a stochastic...
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| Հիմնական հեղինակներ: | , |
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| Համատեղ հեղինակ: | |
| Ձևաչափ: | Էլեկտրոնային էլ․ գիրք |
| Լեզու: | անգլերեն |
| Հրապարակվել է: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2024.
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| Հրատարակություն: | 1st ed. 2024. |
| Շարք: | Springer Series in Operations Research and Financial Engineering,
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| Խորագրեր: | |
| Առցանց հասանելիություն: | https://doi.org/10.1007/978-3-031-57988-2 |
| Ցուցիչներ: |
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| 245 | 1 | 0 | |a Risk-Averse Optimization and Control |h [electronic resource] : |b Theory and Methods / |c by Darinka Dentcheva, Andrzej Ruszczyński. |
| 250 | |a 1st ed. 2024. | ||
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| 505 | 0 | |a Elements of the Utility Theory -- Measures of Risk -- Optimization of Measures of Risk -- Dynamic Risk Optimization -- Optimization with Stochastic Dominance Constraints -- Multivariate and Sequential Stochastic Orders -- Numerical Methods for Problems with Stochastic Dominance Constraints -- Risk-Averse Control of Markov Systems. | |
| 520 | |a This book offers a comprehensive presentation of the theory and methods of risk-averse optimization and control. Problems of this type arise in finance, energy production and distribution, supply chain management, medicine, and many other areas, where not only the average performance of a stochastic system is essential, but also high-impact and low-probability events must be taken into account. The book is a self-contained presentation of the utility theory, the theory of measures of risk, including systemic and dynamic measures of risk, and their use in optimization and control models. It also covers stochastic dominance relations and their application as constraints in optimization models. Optimality conditions for problems with nondifferentiable and nonconvex functions and operators involving risk measures and stochastic dominance relations are discussed. Much attention is paid to multi-stage risk-averse optimization problems and to risk-averse Markov decision problems. Specialized algorithms for solving risk-averse optimization and control problems are presented and analyzed: stochastic subgradient methods for risk optimization, decomposition methods for dynamic problems, event cut and dual methods for stochastic dominance constraints, and policy iteration methods for control problems. The target audience is researchers and graduate students in the areas of mathematics, business analytics, insurance and finance, engineering, and computer science. The theoretical considerations are illustrated with examples, which make the book useful material for advanced courses in the area. . | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
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| 700 | 1 | |a Ruszczyński, Andrzej. |e author. |0 (orcid)0000-0002-4571-1469 |1 https://orcid.org/0000-0002-4571-1469 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
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