An Adaptive Sequential Sample Average Approximation Framework for Solving Two-stage Stochastic Programs

We present adaptive sequential SAA (sample average approximation) algorithms to solve large-scale two-stage stochastic linear programs. The iterative algorithm framework we propose is organized into \emph{outer} and \emph{inner} iterations as follows: during each outer iteration, a sample-path problem is implicitly generated using a sample of observations or “scenarios,” and solved only \emph{imprecisely}, to within a … Read more