Stochastic optimization : Algorithms and Applications

著者

書誌事項

Stochastic optimization : Algorithms and Applications

Edited by Stanislav Uryasev and Panos M. Pardalos

（Applied optimization, vol. 54）

Kluwer Academic Publishers, c2001

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内容説明・目次

内容説明

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.

目次

  • Preface. Output analysis for approximated stochastic programs
  • J. Dupacova. Combinatorial Randomized Rounding: Boosting Randomized Rounding with Combinatorial Arguments
  • P. Efraimidis, P.G. Spirakis. Statutory Regulation of Casualty Insurance Companies: An Example from Norway with Stochastic Programming Analysis
  • A. Gaivoronski, et al. Option pricing in a world with arbitrage
  • X. Guo, L. Shepp. Monte Carlo Methods for Discrete Stochastic Optimization
  • T. Homem-de-Mello. Discrete Approximation in Quantile Problem of Portfolio Selection
  • A. Kibzun, R. Lepp. Optimizing electricity distribution using two-stage integer recourse models
  • W.K. Klein Haneveld, M.H. van der Vlerk. A Finite-Dimensional Approach to Infinite-Dimensional Constraints in Stochastic Programming Duality
  • L. Korf. Non-Linear Risk of Linear Instruments
  • A. Kreinin. Multialgorithms for Parallel Computing: A New Paradigm for Optimization
  • J. Nazareth. Convergence Rate of Incremental Subgradient Algorithms
  • A. Nedic, D. Bertsekas. Transient Stochastic Models for Search Patterns
  • E. Pasiliao. Value-at-Risk Based Portfolio Optimization
  • A. Puelz. Combinatorial Optimization, Cross-Entropy, Ants and Rare Events
  • R.Y. Rubinstein. Consistency of Statistical Estimators: the Epigraphical View
  • G. Salinetti. Hierarchical Sparsity in Multistage Convex Stochastic Programs
  • M. Steinbach. Conditional Value-at-Risk: Optimization Approach
  • S. Uryasev, R.T. Rockafellar.

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