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" Stochastic Optimization: Algorithms and Applications "
edited by Stanislav Uryasev, Panos M. Pardalos.
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BL
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| Record Number
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573124
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b402343
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| Main Entry
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Uryasev, Stanislav.
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| Title & Author
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Stochastic Optimization: Algorithms and Applications\ edited by Stanislav Uryasev, Panos M. Pardalos.
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| Publication Statement
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Boston, MA :: Springer US :: Imprint: Springer,, 2001.
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| Series Statement
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Applied Optimization,; 54
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| ISBN
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9781475765946
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: 9781441948557
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| Contents
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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.
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| Abstract
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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.
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Mathematics.
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Electronic data processing.
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Mathematical optimization.
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Operations research.
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Pardalos, Panos M.
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SpringerLink (Online service)
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