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Sample Average Approximation in a Two-Stage Stochastic Linear Program with Quantile Criterion

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Abstract

A two-stage stochastic linear program with quantile criterion is considered. In this problem, the first stage strategy is deterministic and the second stage strategy is chosen when a realization of the random parameters is known. The properties of the problem are studied, a theorem on the existence of its solution is proved, and a sample average approximation of the problem is constructed. The sample average approximation is reduced to a mixed integer linear program, and a theorem on their equivalence is proved. A procedure for finding an optimal solution of the approximating problem is suggested. A theorem on the convergence of discrete approximations with respect to the value of the objective function and to the optimization strategy is given. We also consider some cases not covered in the theorem.

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References

  1. A. Shapiro, D. Dentcheva, and A. Ruszczynski, Lectures on Stochastic Programming: Modeling and Theory (SIAM, Philadelphia, 2009).

    Book  MATH  Google Scholar 

  2. A. I. Kibzun and A. V. Naumov, “A two-stage quantile linear programming problem,” Autom. Remote Control 56 (1), 68–76 (1995).

    MATH  Google Scholar 

  3. V. I. Norkin, A. I. Kibzun, and A. V. Naumov, “Reducing two-stage probabilistic optimization problems with discrete distribution of random data to mixed-integer programming problems,” Cybern. Syst. Anal. 50, 679–692 (2014).

    Article  MathSciNet  MATH  Google Scholar 

  4. Z. Artstein and R. J.-B. Wets, “Consistency of minimizers and the SLLN for stochastic programs,” J. Convex Anal. 2 (1–2), 1–17 (1996).

    MathSciNet  MATH  Google Scholar 

  5. B. K. Pagnoncelli, S. Ahmed, and A. Shapiro, “Sample average approximation method for chance constrained programming: Theory and applications,” J. Optim. Theory Appl. 142, 399–416 (2009). doi 10.1007/s10957-009-9523-6

    Article  MathSciNet  MATH  Google Scholar 

  6. A. I. Kibzun and S. V. Ivanov, “Convergence of discrete approximations of stochastic programming problems with probabilistic criteria,” in Discrete Optimization and Operations Research: Proceedings of the 9th International Conference, Vladivostok, Russia, 2016 (Springer, Cham, 2016), Ser. Lecture Notes in Computer Science 9869, pp. 525–537.

    Google Scholar 

  7. R. T. Rockafellar and R. J.-B. Wets, Variational Analysis, (Springer, Berlin, 2009).

    MATH  Google Scholar 

  8. I. I. Eremin, Linear Optimization and Systems of Linear Inequalities (Akademiya, Moscow, 2007) [in Russian].

    MATH  Google Scholar 

  9. A. I. Kibzun and Yu. S. Kan, Stochastic Programs with Probabilistic Criteria (Fizmatlit, Moscow, 2009) [in Russian].

    MATH  Google Scholar 

  10. R. Lepp, “Approximate solution of stochastic programming problems with recourse,” Kybernetika 23 (6), 476–482 (1987).

    MathSciNet  MATH  Google Scholar 

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Correspondence to S. V. Ivanov or A. I. Kibzun.

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Original Russian Text © S.V. Ivanov, A.I. Kibzun, 2017, published in Trudy Instituta Matematiki i Mekhaniki UrO RAN, 2017, Vol. 23, No. 3, pp. 134–143.

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Ivanov, S.V., Kibzun, A.I. Sample Average Approximation in a Two-Stage Stochastic Linear Program with Quantile Criterion. Proc. Steklov Inst. Math. 303 (Suppl 1), 115–123 (2018). https://doi.org/10.1134/S0081543818090122

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  • DOI: https://doi.org/10.1134/S0081543818090122

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