An algorithm is proposed, analyzed, and tested for solving nonlinearly constrained continuous optimization problem instances wherein the objective function is defined by an expectation over a random variable argument and the constraints include inequalities (and perhaps equalities) involving functions that may be nonconvex. The algorithm combines a retrospective-approximation strategy for handling the objective and an … Read more
In this paper, we propose a framework based on the Retrospective Approximation (RA) paradigm to solve optimization problems with a stochastic objective function and general nonlinear deterministic constraints. This framework sequentially constructs increasingly accurate approximations of the true problems which are solved to a specified accuracy via a deterministic solver, thereby decoupling the uncertainty from … Read more
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