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FAST---Fast Algorithm for the Scenario Technique

Author

Listed:
  • Algo Carè

    (Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC 3052, Australia)

  • Simone Garatti

    (Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italia)

  • Marco C. Campi

    (Dipartimento di Ingegneria dell'Informazione, Università di Brescia, 25123 Brescia, Italia)

Abstract

The scenario approach is a recently introduced method to obtain feasible solutions to chance-constrained optimization problems based on random sampling. It has been noted that the sample complexity of the scenario approach rapidly increases with the number of optimization variables and this may pose a hurdle to its applicability to medium- and large-scale problems. We here introduce the Fast Algorithm for the Scenario Technique, a variant of the scenario optimization algorithm with reduced sample complexity.

Suggested Citation

  • Algo Carè & Simone Garatti & Marco C. Campi, 2014. "FAST---Fast Algorithm for the Scenario Technique," Operations Research, INFORMS, vol. 62(3), pages 662-671, June.
    • Handle: RePEc:inm:oropre:v:62:y:2014:i:3:p:662-671
    DOI: 10.1287/opre.2014.1257
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    References listed on IDEAS

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    1. Wenqing Chen & Melvyn Sim & Jie Sun & Chung-Piaw Teo, 2010. "From CVaR to Uncertainty Set: Implications in Joint Chance-Constrained Optimization," Operations Research, INFORMS, vol. 58(2), pages 470-485, April.
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    Cited by:

    1. Algo Carè & Simone Garatti & Marco C. Campi, 2019. "The wait-and-judge scenario approach applied to antenna array design," Computational Management Science, Springer, vol. 16(3), pages 481-499, July.
    2. L. Jeff Hong & Zhiyuan Huang & Henry Lam, 2021. "Learning-Based Robust Optimization: Procedures and Statistical Guarantees," Management Science, INFORMS, vol. 67(6), pages 3447-3467, June.

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