Stochastic Optimization: Algorithms and Applications
[Book]
edited by Stanislav Uryasev, Panos M. Pardalos.
Boston, MA :
Imprint: Springer,
2001.
Applied Optimization,
54
1384-6485 ;
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.
0
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.