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Stochastic Optimization

Algorithms and Applications

  • Book
  • © 2001

Overview

Editors:
  1. Stanislav Uryasev

    1. Center for Applied Optimization, Department of Industrial and Systems Engineering, University of Florida, USA

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  2. Panos M. Pardalos

    1. Center for Applied Optimization, Department of Industrial and Systems Engineering, University of Florida, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Applied Optimization (APOP, volume 54)

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Table of contents (17 chapters)

  1. Front Matter

    Pages i-xii
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  2. Output analysis for approximated stochastic programs

    • Jitka Dupačová
    Pages 1-29

  3. Combinatorial Randomized Rounding: Boosting Randomized Rounding with Combinatorial Arguments

    • Pavlos S. Efraimidis, Paul G. Spirakis
    Pages 31-53

  4. Statutory Regulation of Casualty Insurance Companies: An Example from Norway with Stochastic Programming Analysis

    • Alexei A. Gaivoronski, Kjetil Høyland, Petter E. de Lange
    Pages 55-85

  5. Option pricing in a world with arbitrage

    • Xin Guo, Larry Shepp
    Pages 87-96

  6. Monte Carlo Methods for Discrete Stochastic Optimization

    • Tito Homem-de-Mello
    Pages 97-119

  7. Discrete Approximation in Quantile Problem of Portfolio Selection

    • Andrey Kibzun, Riho Lepp
    Pages 121-135

  8. Optimizing electricity distribution using two-stage integer recourse models

    • Willem K. Klein Haneveld, Maarten H. van der Vlerk
    Pages 137-154

  9. A Finite-Dimensional Approach to Infinite-Dimensional Constraints in Stochastic Programming Duality

    • Lisa A. Korf
    Pages 155-167

  10. Non-Linear Risk of Linear Instruments

    • Alexander Kreinin
    Pages 169-182

  11. Multialgorithms for Parallel Computing: A New Paradigm for Optimization

    • J. L. Nazareth
    Pages 183-222

  12. Convergence Rate of Incremental Subgradient Algorithms

    • Angelia Nedić, Dimitri Bertsekas
    Pages 223-264

  13. Transient Stochastic Models for Search Patterns

    • E. L. Pasiliao
    Pages 265-277

  14. Value-at-Risk Based Portfolio Optimization

    • Amy v. Puelz
    Pages 279-302

  15. Combinatorial Optimization, Cross-Entropy, Ants and Rare Events

    • Reuven Y. Rubinstein
    Pages 303-363

  16. Consistency of Statistical Estimators: the Epigraphical View

    • Gabriella Salinetti
    Pages 365-383

  17. Hierarchical Sparsity in Multistage Stochastic Programs

    • Marc C. Steinbach
    Pages 385-410

  18. Conditional Value-at-Risk: Optimization Approach

    • Stanislav Uryasev, R. Tyrrell Rockafellar
    Pages 411-435
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Keywords

About this book

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.

Editors and Affiliations

  • Center for Applied Optimization, Department of Industrial and Systems Engineering, University of Florida, USA

    Stanislav Uryasev, Panos M. Pardalos

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