The concepts of risk-aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. Statistical learning community has also …

The concepts of risk aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. The statistical learning community has …

We consider stochastic programs where the distribution of the uncertain parameters is only
observable through a finite training dataset. Using the Wasserstein metric, we construct a …

In this paper, we survey the primary research on the theory and applications of
distributionally robust optimization (DRO). We start with reviewing the modeling power and …

In this paper we focus on robust linear optimization problems with uncertainty regions
defined by φ-divergences (for example, chi-squared, Hellinger, Kullback–Leibler). We show …

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

The topic of this book is multistage stochastic optimization. Multistage reflects the fact that an
optimal decision is an entire strategy or policy, which is executed during subsequent instants …

Most of classical stochastic programming assumes that the distribution of uncertain
parameters are known, and this distribution is an input to the model. In many applications …

In this paper we study distributionally robust optimization (DRO) problems where the
ambiguity set of the probability distribution is defined by the Kullback-Leibler (KL) …

We study stochastic programs where the decision maker cannot observe the distribution of
the exogenous uncertainties but has access to a finite set of independent samples from this …