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Approximations for Probability Distributions and Stochastic Optimization Problems

In: Stochastic Optimization Methods in Finance and Energy

Author

Listed:
  • Georg Ch. Pflug

    (University of Vienna)

  • Alois Pichler

    (University of Vienna)

Abstract

In this chapter, an overview of the scenario generation problem is given. After an introduction, the basic problem of measuring the distance between two single-period probability models is described in Section 15.2. Section 15.3 deals with finding good single-period scenarios based on the results of the first section. The distance concepts are extended to the multi-period situation in Section 15.4. Finally, Section 15.5 deals with the construction and reduction of scenario trees.

Suggested Citation

  • Georg Ch. Pflug & Alois Pichler, 2011. "Approximations for Probability Distributions and Stochastic Optimization Problems," International Series in Operations Research & Management Science, in: Marida Bertocchi & Giorgio Consigli & Michael A. H. Dempster (ed.), Stochastic Optimization Methods in Finance and Energy, edition 1, chapter 0, pages 343-387, Springer.
    • Handle: RePEc:spr:isochp:978-1-4419-9586-5_15
    DOI: 10.1007/978-1-4419-9586-5_15
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    Citations

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    Cited by:

    1. Seljom, Pernille & Kvalbein, Lisa & Hellemo, Lars & Kaut, Michal & Ortiz, Miguel Muñoz, 2021. "Stochastic modelling of variable renewables in long-term energy models: Dataset, scenario generation & quality of results," Energy, Elsevier, vol. 236(C).
    2. Homem-de-Mello, Tito & Pagnoncelli, Bernardo K., 2016. "Risk aversion in multistage stochastic programming: A modeling and algorithmic perspective," European Journal of Operational Research, Elsevier, vol. 249(1), pages 188-199.
    3. Yannan Chen & Hailin Sun & Huifu Xu, 2021. "Decomposition and discrete approximation methods for solving two-stage distributionally robust optimization problems," Computational Optimization and Applications, Springer, vol. 78(1), pages 205-238, January.
    4. Walter Gutjahr & Alois Pichler, 2016. "Stochastic multi-objective optimization: a survey on non-scalarizing methods," Annals of Operations Research, Springer, vol. 236(2), pages 475-499, January.
    5. Bomze, Immanuel M. & Gabl, Markus & Maggioni, Francesca & Pflug, Georg Ch., 2022. "Two-stage stochastic standard quadratic optimization," European Journal of Operational Research, Elsevier, vol. 299(1), pages 21-34.
    6. Anna Timonina, 2015. "Multi-stage stochastic optimization: the distance between stochastic scenario processes," Computational Management Science, Springer, vol. 12(1), pages 171-195, January.
    7. Walter J. Gutjahr & Alois Pichler, 2016. "Stochastic multi-objective optimization: a survey on non-scalarizing methods," Annals of Operations Research, Springer, vol. 236(2), pages 475-499, January.
    8. Beltran-Royo, C., 2017. "Two-stage stochastic mixed-integer linear programming: The conditional scenario approach," Omega, Elsevier, vol. 70(C), pages 31-42.
    9. Nilay Noyan & Gábor Rudolf & Miguel Lejeune, 2022. "Distributionally Robust Optimization Under a Decision-Dependent Ambiguity Set with Applications to Machine Scheduling and Humanitarian Logistics," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 729-751, March.
    10. Michal Kaut, 2021. "Scenario generation by selection from historical data," Computational Management Science, Springer, vol. 18(3), pages 411-429, July.
    11. Lars Hellemo & Paul I. Barton & Asgeir Tomasgard, 2018. "Decision-dependent probabilities in stochastic programs with recourse," Computational Management Science, Springer, vol. 15(3), pages 369-395, October.
    12. Pichler, Alois & Tomasgard, Asgeir, 2016. "Nonlinear stochastic programming–With a case study in continuous switching," European Journal of Operational Research, Elsevier, vol. 252(2), pages 487-501.
    13. H. Heitsch & H. Leövey & W. Römisch, 2016. "Are Quasi-Monte Carlo algorithms efficient for two-stage stochastic programs?," Computational Optimization and Applications, Springer, vol. 65(3), pages 567-603, December.
    14. Arno Berger & Chuang Xu, 2020. "Asymptotics of One-Dimensional Lévy Approximations," Journal of Theoretical Probability, Springer, vol. 33(2), pages 1164-1195, June.
    15. Arno Berger & Chuang Xu, 2019. "Best Finite Approximations of Benford’s Law," Journal of Theoretical Probability, Springer, vol. 32(3), pages 1525-1553, September.
    16. Wei Wang & Huifu Xu & Tiejun Ma, 2020. "Quantitative Statistical Robustness for Tail-Dependent Law Invariant Risk Measures," Papers 2006.15491, arXiv.org.
    17. Hailin Sun & Huifu Xu, 2016. "Convergence Analysis for Distributionally Robust Optimization and Equilibrium Problems," Mathematics of Operations Research, INFORMS, vol. 41(2), pages 377-401, May.
    18. Yongchao Liu & Alois Pichler & Huifu Xu, 2019. "Discrete Approximation and Quantification in Distributionally Robust Optimization," Mathematics of Operations Research, INFORMS, vol. 44(1), pages 19-37, February.

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