Ebook: Stochastic Optimization: Algorithms and Applications

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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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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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.
Content:
Front Matter....Pages i-xii
Output analysis for approximated stochastic programs....Pages 1-29
Combinatorial Randomized Rounding: Boosting Randomized Rounding with Combinatorial Arguments....Pages 31-53
Statutory Regulation of Casualty Insurance Companies: An Example from Norway with Stochastic Programming Analysis....Pages 55-85
Option pricing in a world with arbitrage....Pages 87-96
Monte Carlo Methods for Discrete Stochastic Optimization....Pages 97-119
Discrete Approximation in Quantile Problem of Portfolio Selection....Pages 121-135
Optimizing electricity distribution using two-stage integer recourse models....Pages 137-154
A Finite-Dimensional Approach to Infinite-Dimensional Constraints in Stochastic Programming Duality....Pages 155-167
Non-Linear Risk of Linear Instruments....Pages 169-182
Multialgorithms for Parallel Computing: A New Paradigm for Optimization....Pages 183-222
Convergence Rate of Incremental Subgradient Algorithms....Pages 223-264
Transient Stochastic Models for Search Patterns....Pages 265-277
Value-at-Risk Based Portfolio Optimization....Pages 279-302
Combinatorial Optimization, Cross-Entropy, Ants and Rare Events....Pages 303-363
Consistency of Statistical Estimators: the Epigraphical View....Pages 365-383
Hierarchical Sparsity in Multistage Stochastic Programs....Pages 385-410
Conditional Value-at-Risk: Optimization Approach....Pages 411-435

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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.
Content:
Front Matter....Pages i-xii
Output analysis for approximated stochastic programs....Pages 1-29
Combinatorial Randomized Rounding: Boosting Randomized Rounding with Combinatorial Arguments....Pages 31-53
Statutory Regulation of Casualty Insurance Companies: An Example from Norway with Stochastic Programming Analysis....Pages 55-85
Option pricing in a world with arbitrage....Pages 87-96
Monte Carlo Methods for Discrete Stochastic Optimization....Pages 97-119
Discrete Approximation in Quantile Problem of Portfolio Selection....Pages 121-135
Optimizing electricity distribution using two-stage integer recourse models....Pages 137-154
A Finite-Dimensional Approach to Infinite-Dimensional Constraints in Stochastic Programming Duality....Pages 155-167
Non-Linear Risk of Linear Instruments....Pages 169-182
Multialgorithms for Parallel Computing: A New Paradigm for Optimization....Pages 183-222
Convergence Rate of Incremental Subgradient Algorithms....Pages 223-264
Transient Stochastic Models for Search Patterns....Pages 265-277
Value-at-Risk Based Portfolio Optimization....Pages 279-302
Combinatorial Optimization, Cross-Entropy, Ants and Rare Events....Pages 303-363
Consistency of Statistical Estimators: the Epigraphical View....Pages 365-383
Hierarchical Sparsity in Multistage Stochastic Programs....Pages 385-410
Conditional Value-at-Risk: Optimization Approach....Pages 411-435
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