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Optimally Coupled Pairs of Square-Integrable Random Vectors

Two square-integrable random vectors are optimally coupled if their joint law is an optimal coupling of their laws, that is, if their mean-square distance equals the Wasserstein distance of their laws.

Statement

In the setting of Wasserstein Spaces, Random Vectors, Vector Fields and Symmetric Matrices in Every Dimension: Standing Notation, let X,Y∈L2(Ω;Rd)X,Y\in L^{2}(\Omega;\mathbb{R}^{d}), the space of classes of square-integrable random vectors with its norm ∥⋅∥L2\lVert\cdot\rVert_{L^{2}} and the law L(X)\mathcal{L}(X) of a class, and let W2W_{2} be the quadratic Wasserstein distance on P2(Rd)\mathcal{P}_{2}(\mathbb{R}^{d}). The joint law L(X,Y)\mathcal{L}(X,Y) is a coupling of L(X)\mathcal{L}(X) and L(Y)\mathcal{L}(Y) with quadratic cost ∥X−Y∥L22\lVert X-Y\rVert_{L^{2}}^{2}, by that clause.

(Optimally coupled pair) The pair (X,Y)(X,Y) is optimally coupled if L(X,Y)\mathcal{L}(X,Y) is an optimal coupling of L(X)\mathcal{L}(X) and L(Y)\mathcal{L}(Y), that is, by Optimal Coupling of Two Probability Measures with Finite Second Moment (i) and The Joint Law of Two Classes of Square-Integrable Random Vectors: Marginals, Cost, and Invariance Under Shifts by a Vector Field (ii), if

∥X−Y∥L22=W2(L(X),L(Y))2.\lVert X-Y\rVert_{L^{2}}^{2}=W_{2}\bigl(\mathcal{L}(X),\mathcal{L}(Y)\bigr)^{2}.

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