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 …

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 …

We study distributionally robust chance-constrained programming (DRCCP) optimization
problems with data-driven Wasserstein ambiguity sets. The proposed algorithmic and …

The entropic value-at-risk (EVaR) is a new coherent risk measure, which is an upper bound
for both the value-at-risk (VaR) and conditional value-at-risk (CVaR). One of the important …

The everlasting consumption of fossil fuels with limited reserves amid climate change and
environmental pollution arises public awareness of sustainable development, which calls for …

Robust optimization (RO) has attracted much attention from the optimization community over
the past decade. RO is dedicated to solving optimization problems subject to uncertainty …

We consider a distributionally robust formulation of stochastic optimization problems arising
in statistical learning, where robustness is with respect to ambiguity in the underlying data …