LARGE DEVIATIONS OF EMPIRICAL ESTIMATES IN STOCHASTIC PROGRAMMING PROBLEMS

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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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