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Totally convex functions

4 Pith papers cite this work. Polarity classification is still indexing.

4 Pith papers citing it
abstract

We study the Optimal Transport problem for laws of random measures in the Kantorovich-Wasserstein space $\mathcal{P}_2(\mathcal{P}_2(\mathrm{H}))$, associated with a Hilbert space $\mathrm{H}$ (with finite or infinite dimension) and for the corresponding quadratic cost induced by the squared Wasserstein metric in $\\mathcal{P}_2(\mathrm{H}).$ Despite the lack of smoothness of the cost, the fact that the space $\mathcal{P}_2(\mathrm{H})$ is not Hilbertian, and the curvature distortion induced by the underlying Wasserstein metric, we will show how to recover at the level of random measures in $\mathcal{P}_2(\mathcal{P}_2(\mathrm{H}))$ the same deep and powerful results linking Euclidean Optimal Transport problems in $\mathcal{P}_2(\mathrm{H})$ and convex analysis. Our approach relies on the notion of totally convex functionals, on their total subdifferentials, and their Lagrangian liftings in the space square integrable $\mathrm{H}$-valued maps $L^2(\mathrm{Q},\mathbb{M};\mathrm{H}).$ With these tools, we identify a natural class of regular measures in $\mathcal{P}_2(\mathcal{P}_2(\mathrm{H}))$ for which the Monge formulation of the OT problem has a unique solution and we will show that this class includes relevant examples of measures with full support in $\mathcal{P}_2(\mathrm{H})$ arising from the push-forward transformation of nondegenerate Gaussian measures in $L^2(\mathrm{Q},\mathbb{M};\mathrm{H}).$

years

2026 4

representative citing papers

Bridging classical and martingale Schr\"odinger bridges

math.PR · 2026-04-01 · unverdicted · novelty 8.0

Martingale Schrödinger bridges extend to any dimension, minimize weighted quadratic energy from Brownian motion in continuous time, and coincide with Föllmer martingales in irreducible cases.

$L^2$ over Wasserstein: Statistical Analysis for Optimal Transport

math.ST · 2026-05-20 · unverdicted · novelty 7.0

Defines the L² over Wasserstein space to equip random probability measures with inherited Riemannian geometry, enabling statistical convergence results and Bayesian posterior consistency in the Wasserstein topology.

Adapted Optimal Transport between Filtered Gaussian Processes

math.PR · 2026-04-24 · unverdicted · novelty 7.0

Adapted optimal transport on filtered Gaussian processes reduces to a constrained Procrustes problem between Cholesky factors, yielding explicit martingale projections and asymptotic equivalence among bicausal couplings.

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