Given X⊂Rn\documentclass[12pt]{minimal}
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\begin{document}$${\mathbf {X}}\subset {\mathbb {R}}^n$$\end{document}, ε∈(0,1)\documentclass[12pt]{minimal}
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\begin{document}$$\varepsilon \in (0,1)$$\end{document}, a parametrized family of probability distributions (μa)a∈A\documentclass[12pt]{minimal}
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\begin{document}$$(\mu _{{\mathbf {a}}})_{{\mathbf {a}}\in {\mathbf {A}}}$$\end{document} on Ω⊂Rp\documentclass[12pt]{minimal}
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\begin{document}$${\varvec{\Omega }}\subset {\mathbb {R}}^p$$\end{document}, we consider the feasible set Xε∗⊂X\documentclass[12pt]{minimal}
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\begin{document}$${\mathbf {X}}^*_\varepsilon \subset {\mathbf {X}}$$\end{document} associated with the distributionally robust chance-constraint Xε∗={x∈X:Probμ[f(x,ω)>0]>1-ε,∀μ∈Ma},\documentclass[12pt]{minimal}
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\begin{document}$$\begin{aligned} {\mathbf {X}}^*_\varepsilon \,=\,\{{\mathbf {x}}\in {\mathbf {X}}:\,\mathrm{Prob}_\mu [f({\mathbf {x}},{\omega })\,>\,0]> 1-\varepsilon ,\,\forall \mu \in {\mathscr {M}}_{\mathbf {a}}\}, \end{aligned}$$\end{document}where Ma\documentclass[12pt]{minimal}
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\begin{document}$${\mathscr {M}}_{\mathbf {a}}$$\end{document} is the set of all possibles mixtures of distributions μa\documentclass[12pt]{minimal}
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\begin{document}$$\mu _{\mathbf {a}}$$\end{document}, a∈A\documentclass[12pt]{minimal}
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\begin{document}$${\mathbf {a}}\in {\mathbf {A}}$$\end{document}. For instance and typically, the family Ma\documentclass[12pt]{minimal}
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\begin{document}$${\mathscr {M}}_{\mathbf {a}}$$\end{document} is the set of all mixtures of Gaussian distributions on R\documentclass[12pt]{minimal}
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\begin{document}$${\mathbb {R}}$$\end{document} with mean and standard deviation a=(a,σ)\documentclass[12pt]{minimal}
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\begin{document}$${\mathbf {a}}=(a,\sigma )$$\end{document} in some compact set A⊂R2\documentclass[12pt]{minimal}
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\begin{document}$${\mathbf {A}}\subset {\mathbb {R}}^2$$\end{document}. We provide a sequence of inner approximations Xεd={x∈X:wd(x)<ε}\documentclass[12pt]{minimal}
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\begin{document}$${\mathbf {X}}^d_\varepsilon =\{{\mathbf {x}}\in {\mathbf {X}}:w_d({\mathbf {x}}) <\varepsilon \}$$\end{document}, d∈N\documentclass[12pt]{minimal}
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\begin{document}$$d\in {\mathbb {N}}$$\end{document}, where wd\documentclass[12pt]{minimal}
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\begin{document}$$w_d$$\end{document} is a polynomial of degree d whose vector of coefficients is an optimal solution of a semidefinite program. The size of the latter increases with the degree d. We also obtain the strong and highly desirable asymptotic guarantee that λ(Xε∗\Xεd)→0\documentclass[12pt]{minimal}
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\begin{document}$$\lambda ({\mathbf {X}}^*_\varepsilon {\setminus } {\mathbf {X}}^d_\varepsilon )\rightarrow 0$$\end{document} as d increases, where λ\documentclass[12pt]{minimal}
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\begin{document}$$\lambda $$\end{document} is the Lebesgue measure on X\documentclass[12pt]{minimal}
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\begin{document}$${\mathbf {X}}$$\end{document}. Same results are also obtained for the more intricated case of distributionally robust “joint” chance-constraints. There is a price to pay for this strong asymptotic guarantee which is the scalability of such a numerical scheme, and so far this important drawback makes it limited to problems of modest dimension.
