Algorithmic Developments in Monte Carlo Sampling-Based Methods for Stochastic Programming
Monte Carlo sampling-based methods are frequently used in stochastic programming when exact solution is not possible. In this dissertation, we develop two sets of Monte Carlo sampling-based algorithms to solve classes of two-stage stochastic programs. These algorithms follow a sequential framework s...
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| Language: | en |
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The University of Arizona.
2012
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| Online Access: | http://hdl.handle.net/10150/228433 |