A probabilistic lower bound for two-stage stochastic programs

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Description

In the framework of Benders decomposition for two-stage stochastic linear programs, the authors estimate the coefficients and right-hand sides of the cutting planes using Monte Carlo sampling. The authors present a new theory for estimating a lower bound for the optimal objective value and they compare (using various test problems whose true optimal value is known) the predicted versus the observed rate of coverage of the optimal objective by the lower bound confidence interval.

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23 p.

Creation Information

Dantzig, G.B. & Infanger, G. November 1, 1995.

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Description

In the framework of Benders decomposition for two-stage stochastic linear programs, the authors estimate the coefficients and right-hand sides of the cutting planes using Monte Carlo sampling. The authors present a new theory for estimating a lower bound for the optimal objective value and they compare (using various test problems whose true optimal value is known) the predicted versus the observed rate of coverage of the optimal objective by the lower bound confidence interval.

Physical Description

23 p.

Notes

OSTI as DE98006355

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  • Other Information: PBD: Nov 1995

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  • Other: DE98006355
  • Report No.: DOE/ER/25116--T3
  • Report No.: SOL--95-6
  • Grant Number: FG03-92ER25116
  • DOI: 10.2172/656786 | External Link
  • Office of Scientific & Technical Information Report Number: 656786
  • Archival Resource Key: ark:/67531/metadc704551

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  • November 1, 1995

Added to The UNT Digital Library

  • Sept. 12, 2015, 6:31 a.m.

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  • Nov. 5, 2015, 2:42 p.m.

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Dantzig, G.B. & Infanger, G. A probabilistic lower bound for two-stage stochastic programs, report, November 1, 1995; United States. (digital.library.unt.edu/ark:/67531/metadc704551/: accessed August 16, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.