This paper surveys the use of Monte Carlo sampling-based methods for stochastic
optimization problems. Such methods are required when—as it often happens in practice …

We consider unconstrained optimization problems where only “stochastic” estimates of the
objective function are observable as replicates from a Monte Carlo oracle. The Monte Carlo …

Stochastic programming models are large-scale optimization problems that are used to
facilitate decision making under uncertainty. Optimization algorithms for such problems need …

Several emerging applications call for a fusion of statistical learning and stochastic
programming (SP). We introduce a new class of models which we refer to as Predictive …

Scenario optimization is a broad scheme for data-driven decision-making in which
experimental observations act as constraints on the feasible domain for the optimization …

We present adaptive sequential SAA (sample average approximation) algorithms to solve
large-scale two-stage stochastic linear programs. The iterative algorithm framework we …

Several emerging applications call for a fusion of statistical learning (SL) and stochastic
programming (SP). The Learning Enabled Optimization paradigm fuses concepts from these …

In the context of optimization under uncertainty, we consider various combinations of
distribution estimation and resampling (bootstrap and bagging) for obtaining samples used …

We describe software for stochastic programming that uses only sampled data to obtain both
a consistent sample-average solution and a consistent estimate of confidence intervals for …

The scenario approach is a broad methodology for data-driven optimization that has found
numerous applications in systems and control design. It consists in making a decision that is …