APPROXIMATING DATA-DRIVEN JOINT CHANCE-CONSTRAINED PROGRAMS VIA UNCERTAINTY SET CONSTRUCTION

@inproceedings{Roeder2016APPROXIMATINGDJ,
  title={APPROXIMATING DATA-DRIVEN JOINT CHANCE-CONSTRAINED PROGRAMS VIA UNCERTAINTY SET CONSTRUCTION},
  author={Theresa M. K. Roeder and Peter I. Frazier and Roberto Szechtman and Enlu Zhou},
  year={2016}
}
We study the use of robust optimization (RO) in approximating joint chance-constrained programs (CCP), in situations where only limited data, or Monte Carlo samples, are available in inferring the underlying probability distributions. We introduce a procedure to construct uncertainty set in the RO problem that translates into provable statistical guarantees for the joint CCP. This procedure relies on learning the high probability region of the data and controlling the region’s size via a… CONTINUE READING

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