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Large Deviations of Empirical Estimates in Stochastic Programming Problems

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Cybernetics and Systems Analysis Aims and scope

Abstract

The authors analyze a stochastic programming problem where the random factor is a stationary ergodic sequence. The problem is approximated by minimizing an empirical function. It is proved that, under some conditions, the probability of large deviations of empirical estimates from the initial problem solution decreases exponentially.

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REFERENCES

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Authors and Affiliations

  1. V. M. Glushkov Institute of Cybernetics, National Academy of Sciences of Ukraine, Kiev, Ukraine

    P. S. Knopov & E. I. Kasitskaya

Authors
  1. P. S. Knopov
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  2. E. I. Kasitskaya
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Knopov, P.S., Kasitskaya, E.I. Large Deviations of Empirical Estimates in Stochastic Programming Problems. Cybernetics and Systems Analysis 40, 510–516 (2004). https://doi.org/10.1023/B:CASA.0000047872.23833.35

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  • DOI: https://doi.org/10.1023/B:CASA.0000047872.23833.35

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