Stochastic Optimization

Algorithms and Applications

By: Uryasev, Stanislav P. Uryasev, S. Uryasev (Editor)

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Hardcover | 31 May 2001

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452 Pages

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23.5 x 16.51 x 2.54

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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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Published: 1st December 2010

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Preface
Output analysis for approximated stochastic programs	p. 1
Combinatorial Randomized Rounding: Boosting Randomized Rounding with Combinatorial Arguments	p. 31
Statutory Regulation of Casualty Insurance Companies: An Example from Norway with Stochastic Programming Analysis	p. 55
Option pricing in a world with arbitrage	p. 87
Monte Carlo Methods for Discrete Stochastic Optimization	p. 97
Discrete Approximation in Quantile Problem of Portfolio Selection	p. 121
Optimizing electricity distribution using two-stage integer recourse models	p. 137
A Finite-Dimensional Approach to Infinite-Dimensional Constraints in Stochastic Programming Duality	p. 155
Non-Linear Risk of Linear-Instruments	p. 169
Multialgorithms for Parallel Computing: A New Paradigm for Optimization	p. 183
Convergence Rate of Incremental Subgradient Algorithms	p. 223
Transient Stochastic Models for Search Patterns	p. 265
Value-at-Risk Based Portfolio Optimization	p. 279
Combinatorial Optimization, Cross-Entropy, Ants and Rare Events	p. 303
Consistency of Statistical Estimators: the Epigraphical View	p. 365
Hierarchical Sparsity in Multistage Convex Stochastic Programs	p. 385
Conditional Value-at-Risk: Optimization Approach	p. 411
Table of Contents provided by Blackwell. All Rights Reserved.

Stochastic Optimization

Algorithms and Applications

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Hardcover

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