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. The practical experience gained in stochastic programming has been expanded to a much larger spectrum of applications including financial modelling, 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.
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Preface. Output analysis for approximated stochastic programs; J. Dupacová. Combinatorial Randomized Rounding: Boosting Randomized Rounding with Combinatorial Arguments; P. Efraimidis, P.G. Spirakis. Statutory Regulation of Casualty Insurance Companies: An Example from Norway with Stochastic Programming Analysis; A. Gaivoronski, et al. Option pricing in a world with arbitrage; X. Guo, L. Shepp. Monte Carlo Methods for Discrete Stochastic Optimization; T. Homem-de-Mello. Discrete Approximation in Quantile Problem of Portfolio Selection; A. Kibzun, R. Lepp. Optimizing electricity distribution using two-stage integer recourse models; W.K. Klein Haneveld, M.H. van der Vlerk. A Finite-Dimensional Approach to Infinite-Dimensional Constraints in Stochastic Programming Duality; L. Korf. Non-Linear Risk of Linear Instruments; A. Kreinin. Multialgorithms for Parallel Computing: A New Paradigm for Optimization; J. Nazareth. Convergence Rate of Incremental Subgradient Algorithms; A. Nedic, D. Bertsekas. Transient Stochastic Models for Search Patterns; E. Pasiliao. Value-at-Risk Based Portfolio Optimization; A. Puelz. Combinatorial Optimization, Cross-Entropy, Ants and Rare Events; R.Y. Rubinstein. Consistency of Statistical Estimators: the Epigraphical View; G. Salinetti. Hierarchical Sparsity in Multistage Convex Stochastic Programs; M. Steinbach. Conditional Value-at-Risk: Optimization Approach; S. Uryasev, R.T. Rockafellar.
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Da: Books Puddle, New York, NY, U.S.A.
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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, telecommunicat. Codice articolo 5969807
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 452 pp. Englisch. Codice articolo 9780792369516
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Condizione: New. Stochastic programming is the study of procedures for decision making under the presence of uncertainties and risks. Stochastic programming approaches have been used in a number of areas such as energy and production planning, telecommunications, and transportation. This book serves as supplementary reading in related courses. Editor(s): Uryasev, Stanislav; Pardalos, Panos M. Series: Applied Optimization. Num Pages: 447 pages, biography. BIC Classification: TGP; TJFM. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 234 x 156 x 25. Weight in Grams: 807. . 2001. 2001st Edition. hardcover. . . . . Codice articolo V9780792369516
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Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 452 pp. Englisch. Codice articolo 9780792369516
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