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020 _a9783031579882
_9978-3-031-57988-2
024 7 _a10.1007/978-3-031-57988-2
_2doi
050 4 _aQA402.5-402.6
072 7 _aPBU
_2bicssc
072 7 _aMAT042000
_2bisacsh
072 7 _aPBU
_2thema
100 1 _aDentcheva, Darinka.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979672
245 1 0 _aRisk-Averse Optimization and Control
_h[electronic resource] :
_bTheory and Methods /
_cby Darinka Dentcheva, Andrzej Ruszczyński.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXV, 451 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Series in Operations Research and Financial Engineering,
_x2197-1773
505 0 _aElements 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 _aThis 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. .
650 0 _aMathematical optimization.
_965522
650 0 _aSocial sciences
_xMathematics.
_969736
650 0 _aMathematics.
_965523
650 1 4 _aOptimization.
_965528
650 2 4 _aMathematics in Business, Economics and Finance.
_969739
650 2 4 _aMathematics.
_965523
700 1 _aRuszczyński, Andrzej.
_eauthor.
_0(orcid)0000-0002-4571-1469
_1https://orcid.org/0000-0002-4571-1469
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979673
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031579875
776 0 8 _iPrinted edition:
_z9783031579899
776 0 8 _iPrinted edition:
_z9783031579905
830 0 _aSpringer Series in Operations Research and Financial Engineering,
_x2197-1773
_976963
856 4 0 _uhttps://doi.org/10.1007/978-3-031-57988-2
912 _aZDB-2-SMA
912 _aZDB-2-SXMS
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