Uryasev / Pardalos Stochastic Optimization

Algorithms and Applications
Erscheinungsjahr 2013
ISBN: 978-1-4757-6594-6
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 54, 435 Seiten, eBook

Reihe: Applied Optimization

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.

Zielgruppe

Research

Autoren/Hrsg.

Uryasev, Stanislav

Pardalos, Panos M.

Inhaltsverzeichnis

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.