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 …

Uncertainties from deepening penetration of renewable energy resources have posed
critical challenges to the secure and reliable operations of future electric grids. Among …

The SCIP Optimization Suite provides a collection of software packages for mathematical
optimization centered around the constraint integer programming framework SCIP. This …

We provide an exact deterministic reformulation for data-driven, chance-constrained
programs over Wasserstein balls. For individual chance constraints as well as joint chance …

In this paper, we study data-driven chance constrained stochastic programs, or more
specifically, stochastic programs with distributionally robust chance constraints (DCCs) in a …

In this paper, we present a unit commitment problem with uncertain wind power output. The
problem is formulated as a chance-constrained two-stage (CCTS) stochastic program. Our …

A wide range of problems can be modeled as Mixed Integer Linear Programming (MIP)
problems using standard formulation techniques. However, in some cases the resulting MIP …

In this paper, we study the link between a Chance-Constrained optimization Problem (CCP)
and its sample counterpart (SP). SP has a finite number, say N, of sampled constraints …

The beginning of stochastic programming, and in particular stochastic linear programming
(SLP), dates back to the 50's and early 60's of the last century. Pioneers wheat that time …

We consider convex optimization problems with uncertain, probabilistically described,
constraints. In this context, scenario optimization is a well recognized methodology where a …