Many practical decision problems involve both nonlinear relationships and uncertainties. The resulting stochastic nonlinear programs become quite difficult to solve as the number of possible scenarios increases. In this paper, we provide a decomposition method for problems in which nonlinear constraints appear within periods. We also show how the method extends to lower bounding refinements of the set of scenarios when the random data are independent from period to period. We then apply the method to a stochastic model of the U.S. economy based on the Global 2100 method developed by Manne and Richels.
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This material is based upon work supported by the National Science Foundation under Award Numbers SES-9211937 and DDM-9215921.
The research was performed under appointment to the U.S. Department of Energy, Graduate Fellowships for Global Change Program, administered by Oak Ridge Institute for Science and Education.
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Birge, J.R., Rosa, C.H. Parallel decomposition of large-scale stochastic nonlinear programs. Ann Oper Res 64, 39–65 (1996). https://doi.org/10.1007/BF02187640
- parallel computation
- stochastic programming