Stochastic Optimization: Algorithms and Applications

by Stanislav Uryasev (Editor), Panos M. Pardalos (Editor)

Overview

Shastic programming is the study of procedures for decision making under the presence of uncertainties and risks. Shastic 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 shastic 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 shastic programming algorithms; (2) sensitivity analysis of shastic systems; (3) shastic 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.

Product Details

ISBN-13:	9780792369516
Publisher:	Springer US
Publication date:	05/31/2001
Series:	Applied Optimization , #54
Edition description:	2001
Pages:	435
Product dimensions:	6.14(w) x 9.21(h) x 0.36(d)

Table of Contents

Output analysis for approximated shastic programs.- Combinatorial Randomized Rounding: Boosting Randomized Rounding with Combinatorial Arguments.- Statutory Regulation of Casualty Insurance Companies: An Example from Norway with Shastic Programming Analysis.- Option pricing in a world with arbitrage.- Monte Carlo Methods for Discrete Shastic 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 Shastic Programming Duality.- Non—Linear Risk of Linear Instruments.- Multialgorithms for Parallel Computing: A New Paradigm for Optimization.- Convergence Rate of Incremental Subgradient Algorithms.- Transient Shastic 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 Shastic Programs.- Conditional Value-at-Risk: Optimization Approach.