DECIS is a system for solving large-scale stochastic programs, i.e. programs that include parameters (coefficients and right-hand sides) that are not known with certainty, but are assumed to be known by their probability distribution. It employs Benders decomposition and advanced Monte Carlo sampling techniques. DECIS includes a variety of solution strategies, such as solving the universe problem, the expected value problem, Monte Carlo sampling within the Benders decomposition algorithm, and Monte Carlo pre-sampling. When using Monte Carlo sampling the user has the option of employing crude Monte Carlo without variance reduction techniques, or using as variance reduction techniques importance sampling or control variates, based on either an additive or a multiplicative approximation function. Pre-sampling is limited to using crude Monte Carlo only.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Glynn, Peter W.; Infanger, Gerd: Simulation-based confidence bounds for two-stage stochastic programs (2013)
- Infanger, Gerd: Stochastic programming for funding mortgage pools (2009)
- Gassmann, H. I.; Infanger, G.: Modelling history-dependent parameters in the SMPS format for stochastic programming (2008)
- Infanger, Gerd: Stochastic programming for funding mortgage pools (2007)