Hybrid Simplicial-Randomized Approximate Stochastic Dynamic Programming for Multireservoir Optimization

We revisit the simplicial approximation scheme of [43, 44, 45] with a new randomized approach for selecting the grid points of the Bellman function’s piecewise linear approximation in a stochastic dynamic programming model for multireservoir optimization. Unlike the previous approach that requires a complete list of error bounds for all simplices, we propose to choose the grid points at each iteration from a small number of randomly sampled simplices from which an error bound is estimated. We present a series of numerical experiments to compare the performance of three such sampling strategies