Stochastic Optimization

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

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

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

  Stanislav Uryasev, Panos M. Pardalos

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