Globalized distributionally robust optimization problems under the moment-based framework

This paper is devoted to reduce the conservativeness of distributionally robust optimization with moments information. Since optimal solution required be feasible for all uncertain distributions in a given ambiguity distribution set and so inevitable. To address this issue, we introduce globalized counterpart (GDRC) which allows constraint violations controlled by functional distance true inner set. We obtain deterministic equivalent forms several GDRCs under moment-based framework. specific, show systems inequalities second order moment information separable convex function special jointly function, respectively. also inequality GDRC first support The computationally tractable examples are presented these GDRCs. Numerical tests portfolio problem effectiveness our method results demonstrate that non-conservative flexible compared solution.