Variance reduction in Monte Carlo sampling-based optimality gap estimators for two-stage stochastic linear programming. Stockbridge, R. & Bayraksan, G. Comput. Optim. Appl., 64(2):407-431, 2016.
Variance reduction in Monte Carlo sampling-based optimality gap estimators for two-stage stochastic linear programming. [link]Link  Variance reduction in Monte Carlo sampling-based optimality gap estimators for two-stage stochastic linear programming. [link]Paper  bibtex   
@article{journals/coap/StockbridgeB16,
  added-at = {2020-07-14T00:00:00.000+0200},
  author = {Stockbridge, Rebecca and Bayraksan, Güzin},
  biburl = {https://www.bibsonomy.org/bibtex/2a61910bdf2727c29b35967030a52e444/dblp},
  ee = {https://www.wikidata.org/entity/Q57500065},
  interhash = {a3878043ef672f6b284bcf4689f980f3},
  intrahash = {a61910bdf2727c29b35967030a52e444},
  journal = {Comput. Optim. Appl.},
  keywords = {dblp},
  number = 2,
  pages = {407-431},
  timestamp = {2020-07-24T00:06:36.000+0200},
  title = {Variance reduction in Monte Carlo sampling-based optimality gap estimators for two-stage stochastic linear programming.},
  url = {http://dblp.uni-trier.de/db/journals/coap/coap64.html#StockbridgeB16},
  volume = 64,
  year = 2016
}

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