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Role of Dependence in Chance-constrained and Robust Programming
[Role závislosti v úlohách s pravděpodobnostními omezeními a v úlohách robustního programování]

Author

Listed:
  • Michal Houda

Abstract

The paper deals with two methods of solving optimization programs where uncertainties occur: stochastic (in particular chance-constrained) programming and robust programming. We review briefly how these two methods deal with uncertainty and what approximations are commonly used. Furthermore, we are concentrated on approximations based on sample sets where some type of weak dependence occurs. We demonstrate that such kind of dependence does not imply any important malfunction of optimization methods used there. Numerical illustration on simple optimization program is given.

Suggested Citation

  • Michal Houda, 2007. "Role of Dependence in Chance-constrained and Robust Programming [Role závislosti v úlohách s pravděpodobnostními omezeními a v úlohách robustního programování]," Acta Oeconomica Pragensia, Prague University of Economics and Business, vol. 2007(4), pages 111-120.
    • Handle: RePEc:prg:jnlaop:v:2007:y:2007:i:4:id:80:p:111-120
    DOI: 10.18267/j.aop.80
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    References listed on IDEAS

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    1. Daniela Pucci de Farias & Benjamin Van Roy, 2004. "On Constraint Sampling in the Linear Programming Approach to Approximate Dynamic Programming," Mathematics of Operations Research, INFORMS, vol. 29(3), pages 462-478, August.
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    More about this item

    Keywords

    stochastic programming; robust programming; weak dependence;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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