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＜電子ブック＞
Stochastic Optimization : Algorithms and Applications / edited by Stanislav Uryasev, Panos M. Pardalos
(Applied Optimization ; 54)

版	1st ed. 2001.
出版者	(New York, NY : Springer US : Imprint: Springer)
出版年	2001
本文言語	英語
大きさ	XII, 435 p : online resource
著者標目	Uryasev, Stanislav editor Pardalos, Panos M editor SpringerLink (Online service)
件　名	LCSH:Industrial engineering LCSH:Production engineering LCSH:Control engineering LCSH:Mathematical optimization LCSH:Calculus of variations LCSH:Operations research LCSH:Finance LCSH:Mathematical models FREE:Industrial and Production Engineering FREE:Control and Systems Theory FREE:Calculus of Variations and Optimization FREE:Operations Research and Decision Theory FREE:Financial Economics FREE:Mathematical Modeling and Industrial Mathematics
一般注記	Output analysis for approximated stochastic programs -- Combinatorial Randomized Rounding: Boosting Randomized Rounding with Combinatorial Arguments -- Statutory Regulation of Casualty Insurance Companies: An Example from Norway with Stochastic Programming Analysis -- Option pricing in a world with arbitrage -- Monte Carlo Methods for Discrete Stochastic Optimization -- Discrete Approximation in Quantile Problem of Portfolio Selection -- Optimizing electricity distribution using two-stage integer recourse models -- A Finite-Dimensional Approach to Infinite-Dimensional Constraints in Stochastic Programming Duality -- Non—Linear Risk of Linear Instruments -- Multialgorithms for Parallel Computing: A New Paradigm for Optimization -- Convergence Rate of Incremental Subgradient Algorithms -- Transient Stochastic Models for Search Patterns -- Value-at-Risk Based Portfolio Optimization -- Combinatorial Optimization, Cross-Entropy, Ants and Rare Events -- Consistency of Statistical Estimators: the Epigraphical View -- Hierarchical Sparsity in Multistage Convex Stochastic Programs -- Conditional Value-at-Risk: Optimization Approach Stochastic programming is the study of procedures for decision making under the presence of uncertainties and risks. Stochastic programming approaches have been successfully used in a number of areas such as energy and production planning, telecommunications, and transportation. Recently, the practical experience gained in stochastic programming has been expanded to a much larger spectrum of applications including financial modeling, risk management, and probabilistic risk analysis. Major topics in this volume include: (1) advances in theory and implementation of stochastic programming algorithms; (2) sensitivity analysis of stochastic systems; (3) stochastic programming applications and other related topics. Audience: Researchers and academies working in optimization, computer modeling, operations research and financial engineering. The book is appropriate as supplementary reading in courses on optimization and financial engineering HTTP:URL=https://doi.org/10.1007/978-1-4757-6594-6

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