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Nonlinear stochastic programming by Monte-Carlo estimators

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  • Sakalauskas, Leonidas L.

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  • Sakalauskas, Leonidas L., 2002. "Nonlinear stochastic programming by Monte-Carlo estimators," European Journal of Operational Research, Elsevier, vol. 137(3), pages 558-573, March.
    • Handle: RePEc:eee:ejores:v:137:y:2002:i:3:p:558-573
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    References listed on IDEAS

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    1. R. Y. Rubinstein, 1983. "Smoothed Functionals in Stochastic Optimization," Mathematics of Operations Research, INFORMS, vol. 8(1), pages 26-33, February.
    2. Jean-Michel Guldmann, 1983. "Supply, Storage, and Service Reliability Decisions by Gas Distribution Utilities: A Chance-Constrained Approach," Management Science, INFORMS, vol. 29(8), pages 884-906, August.
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    Cited by:

    1. Bartkute, Vaida & Sakalauskas, Leonidas, 2007. "Simultaneous perturbation stochastic approximation of nonsmooth functions," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1174-1188, September.
    2. Jangho Park & Rebecca Stockbridge & Güzin Bayraksan, 2021. "Variance reduction for sequential sampling in stochastic programming," Annals of Operations Research, Springer, vol. 300(1), pages 171-204, May.
    3. Mike G. Tsionas & Dionisis Philippas & Constantin Zopounidis, 2023. "Exploring Uncertainty, Sensitivity and Robust Solutions in Mathematical Programming Through Bayesian Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 205-227, June.
    4. J. O. Royset & E. Polak, 2007. "Extensions of Stochastic Optimization Results to Problems with System Failure Probability Functions," Journal of Optimization Theory and Applications, Springer, vol. 133(1), pages 1-18, April.

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