Abstract
Moment-based ambiguity sets are mostly used in distributionally robust chance constraints (DRCCs). Their conservatism can be reduced by imposing unimodality, but the known reformulations do not scale well. We propose a new ambiguity set tailored to unimodal and seemingly symmetric distributions by encoding unimodality-skewness information, which leads to conic reformulations of DRCCs that are more tractable than known ones based on semi-definite programs. Besides, the conic reformulation yields a closed-form expression of the inverse of unimodal Cantelli's bound.
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