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Iterative estimation maximization for stochastic linear programs with conditional value-at-risk constraints

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  • Pu Huang
  • Dharmashankar Subramanian

Abstract

We present a new algorithm, iterative estimation maximization (IEM), for stochastic linear programs with conditional value-at-risk constraints. IEM iteratively constructs a sequence of linear optimization problems, and solves them sequentially to find the optimal solution. The size of the problem that IEM solves in each iteration is unaffected by the size of random sample points, which makes it extremely efficient for real-world, large-scale problems. We prove the convergence of IEM, and give a lower bound on the number of sample points required to probabilistically bound the solution error. We also present computational performance on large problem instances and a financial portfolio optimization example using an S&P 500 data set. Copyright Springer-Verlag 2012

Suggested Citation

  • Pu Huang & Dharmashankar Subramanian, 2012. "Iterative estimation maximization for stochastic linear programs with conditional value-at-risk constraints," Computational Management Science, Springer, vol. 9(4), pages 441-458, November.
    • Handle: RePEc:spr:comgts:v:9:y:2012:i:4:p:441-458
    DOI: 10.1007/s10287-011-0135-x
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    1. Gordon J. Alexander & Alexandre M. Baptista, 2004. "A Comparison of VaR and CVaR Constraints on Portfolio Selection with the Mean-Variance Model," Management Science, INFORMS, vol. 50(9), pages 1261-1273, September.
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    Cited by:

    1. Juan Ma & Foad Mahdavi Pajouh & Balabhaskar Balasundaram & Vladimir Boginski, 2016. "The Minimum Spanning k -Core Problem with Bounded CVaR Under Probabilistic Edge Failures," INFORMS Journal on Computing, INFORMS, vol. 28(2), pages 295-307, May.
    2. Foad Mahdavi Pajouh & Esmaeel Moradi & Balabhaskar Balasundaram, 2017. "Detecting large risk-averse 2-clubs in graphs with random edge failures," Annals of Operations Research, Springer, vol. 249(1), pages 55-73, February.

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