TheoremBase

The swap of an optimal coupling is optimal, so the convexity inequality holds along the coupling from each measure to the other; adding the two cancels the penalties and leaves the monotonicity. At uniquely mapped pairs the optimal coupling is induced by the optimal map, its swap by the reverse optimal map, and the displacement pairings become inner products with the optimal displacements.

Proof

Each result cited is universally quantified over the data in its own statement. Write W=W2(μ,ν)2W=W_{2}(\mu,\nu)^{2}.

Step 1 (the swap of an optimal coupling is optimal). Let π∈Π(μ,ν)\pi\in\Pi(\mu,\nu) be optimal, so I(π)=WI(\pi)=W by Optimal Coupling of Two Probability Measures with Finite Second Moment (i). By Couplings on Euclidean Space: Product Coupling, Swap, Finiteness of the Cost, Push-Forward Couplings, Modifying One Marginal, Quantisation, Gluing over a Finitely Supported Measure, and the Lipschitz Bound (ii), π⊤∈Π(ν,μ)\pi^{\top}\in\Pi(\nu,\mu) and I(π⊤)=I(π)=WI(\pi^{\top})=I(\pi)=W, and W=W2(ν,μ)2W=W_{2}(\nu,\mu)^{2} by The Quadratic Wasserstein Distance is a Metric on the Wasserstein Space (i). Hence π⊤\pi^{\top} is an optimal coupling of ν\nu and μ\mu, again by Optimal Coupling of Two Probability Measures with Finite Second Moment (i).

Step 2 (claim 1). Let π\pi be as in Step 1. Since μ,ν∈DΣ⊆D\mu,\nu\in\mathcal{D}_{\Sigma}\subseteq\mathcal{D} by Penalty Pairs on the Wasserstein Space: the Penalty, Its Score, and Their Domains (i), the λ\lambda-displacement convexity of λ\lambda-Displacement Convexity of a Penalty Pair on the Wasserstein Space (i) applies to the measure μ∈DΣ\mu\in\mathcal{D}_{\Sigma}, the measure ν∈D\nu\in\mathcal{D} and the optimal coupling π\pi, and also, by Step 1, to the measure ν∈DΣ\nu\in\mathcal{D}_{\Sigma}, the measure μ∈D\mu\in\mathcal{D} and the optimal coupling π⊤\pi^{\top}:

E(μ)+J(Σ(μ),π)+λ2W≤E(ν),E(ν)+J(Σ(ν),π⊤)+λ2W≤E(μ).\mathcal{E}(\mu)+\mathcal{J}(\Sigma(\mu),\pi)+\tfrac{\lambda}{2}W\le\mathcal{E}(\nu),\qquad\mathcal{E}(\nu)+\mathcal{J}(\Sigma(\nu),\pi^{\top})+\tfrac{\lambda}{2}W\le\mathcal{E}(\mu).

By claim 3 of Elementary Arithmetic in an Ordered Field the differences of the right and left sides are nonnegative, so their sum is nonnegative by claim 2 there. In that sum E(μ)\mathcal{E}(\mu) and E(ν)\mathcal{E}(\nu) cancel, and λ2W+λ2W=λW\tfrac{\lambda}{2}W+\tfrac{\lambda}{2}W=\lambda W because 22 times the multiplicative inverse of 22 is 11; so

0≤−J(Σ(μ),π)−J(Σ(ν),π⊤)−λW,0\le-\mathcal{J}(\Sigma(\mu),\pi)-\mathcal{J}(\Sigma(\nu),\pi^{\top})-\lambda W,

and claim 3 of Elementary Arithmetic in an Ordered Field turns this into claim 1.

Step 3 (claim 2). Let SS and S′S' be as in claim 2. The coupling πS=(id,S)#μ\pi_{S}=(\mathrm{id},S)_{\#}\mu belongs to Π(μ,ν)\Pi(\mu,\nu) and is optimal, and (id,S′)#ν(\mathrm{id},S')_{\#}\nu belongs to Π(ν,μ)\Pi(\nu,\mu) and is optimal, both by Optimal Transport Maps and Uniquely Mapped Pairs of Probability Measures (i). By Step 1, πS⊤\pi_{S}^{\top} is an optimal coupling of ν\nu and μ\mu. Since (ν,μ)(\nu,\mu) is uniquely mapped, there is an optimal map TT from ν\nu to μ\mu such that every optimal coupling of ν\nu and μ\mu equals (id,T)#ν(\mathrm{id},T)_{\#}\nu (Optimal Transport Maps and Uniquely Mapped Pairs of Probability Measures (ii)); applying this to both optimal couplings just named,

πS⊤=(id,T)#ν=(id,S′)#ν.\pi_{S}^{\top}=(\mathrm{id},T)_{\#}\nu=(\mathrm{id},S')_{\#}\nu .

Next, ∥id−S∥μ2=W<∞\lVert\mathrm{id}-S\rVert_{\mu}^{2}=W<\infty by The Optimal Map as a Square-Integrable Vector Field: Integrability, Transport Cost and Uniqueness of the Class (ii), and ∥S(x)−x∥=∥x−S(x)∥\lVert S(x)-x\rVert=\lVert x-S(x)\rVert for every xx by claim 5 of Properties of the Euclidean Norm on Rn\mathbb{R}^n: Square, Distance, Vanishing, Coordinate Bounds, Homogeneity, Triangle Inequalities and Expansion of a Square (with the scalar −1-1), so ∫Rd∥S−id∥2 dμ=∥id−S∥μ2<∞\int_{\mathbb{R}^{d}}\lVert S-\mathrm{id}\rVert^{2}\,d\mu=\lVert\mathrm{id}-S\rVert_{\mu}^{2}<\infty. Hence The Displacement Pairing of a Square-Integrable Vector Field Along a Coupling (iv) applies to SS and gives J(Σ(μ),πS)=⟨Σ(μ),S−id⟩μ\mathcal{J}(\Sigma(\mu),\pi_{S})=\langle\Sigma(\mu),S-\mathrm{id}\rangle_{\mu}. The class S−idS-\mathrm{id} is (−1)(id−S)(-1)(\mathrm{id}-S), the pointwise relation S(x)−x=(−1)(x−S(x))S(x)-x=(-1)(x-S(x)) passing to classes by Square-Integrable Random Vectors: Coordinates, Operations, Almost Sure Equality and the Mean-Square Form (v); by linearity of the inner product of L2(μ;Rd)L^{2}(\mu;\mathbb{R}^{d}) in its second argument,

−J(Σ(μ),πS)=⟨Σ(μ),id−S⟩μ.-\mathcal{J}(\Sigma(\mu),\pi_{S})=\langle\Sigma(\mu),\mathrm{id}-S\rangle_{\mu}.

The same argument with ν\nu, μ\mu and S′S' in place of μ\mu, ν\nu and SS gives −J(Σ(ν),(id,S′)#ν)=⟨Σ(ν),id−S′⟩ν-\mathcal{J}(\Sigma(\nu),(\mathrm{id},S')_{\#}\nu)=\langle\Sigma(\nu),\mathrm{id}-S'\rangle_{\nu}. Claim 1, applied to the optimal coupling πS\pi_{S} with πS⊤=(id,S′)#ν\pi_{S}^{\top}=(\mathrm{id},S')_{\#}\nu, now reads

λW≤⟨Σ(μ),id−S⟩μ+⟨Σ(ν),id−S′⟩ν,\lambda W\le\langle\Sigma(\mu),\mathrm{id}-S\rangle_{\mu}+\langle\Sigma(\nu),\mathrm{id}-S'\rangle_{\nu},

which is claim 2. ■\blacksquare

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