A probabilistic lower bound for two-stage stochastic programs
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.
- Research Organization:
- Stanford Univ., Dept. of Operations Research, CA (United States)
- Sponsoring Organization:
- USDOE Office of Energy Research, Washington, DC (United States)
- DOE Contract Number:
- FG03-92ER25116
- OSTI ID:
- 656786
- Report Number(s):
- DOE/ER/25116--T3; SOL--95-6; ON: DE98006355
- Country of Publication:
- United States
- Language:
- English
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