© 2001

Stochastic Optimization: Algorithms and Applications

  • Stanislav Uryasev
  • Panos M. Pardalos

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

Table of contents

  1. Front Matter
    Pages i-xii
  2. Xin Guo, Larry Shepp
    Pages 87-96
  3. Willem K. Klein Haneveld, Maarten H. van der Vlerk
    Pages 137-154
  4. Alexander Kreinin
    Pages 169-182
  5. Angelia Nedić, Dimitri Bertsekas
    Pages 223-264
  6. Amy v. Puelz
    Pages 279-302
  7. Stanislav Uryasev, R. Tyrrell Rockafellar
    Pages 411-435

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.


Analysis Arbitrage Financial Modeling Monte Carlo Method Stochastic Optimization Stochastic Programming Stochastic model Stochastic models algorithms combinatorial optimization communication electricity modeling operations research optimization

Editors and affiliations

  • Stanislav Uryasev
    • 1
  • Panos M. Pardalos
    • 1
  1. 1.Center for Applied Optimization, Department of Industrial and Systems EngineeringUniversity of FloridaUSA

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