Stochastic Optimization

Algorithms and Applications
Herausgegeben:Uryasev, Stanislav; Pardalos, Panos M.

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Applied Optimization 54
• Verlag: Springer / Springer US / Springer, Berlin
• Artikelnr. des Verlages: 978-1-4419-4855-7
• Softcover reprint of hardcover 1st ed. 2001
• Seitenzahl: 452
• Erscheinungstermin: 1. Dezember 2010
• Englisch
• Abmessung: 235mm x 155mm x 25mm
• Gewicht: 670g
• ISBN-13: 9781441948557
• ISBN-10: 1441948554
• Artikelnr.: 32109940

Inhaltsangabe

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.