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The matching problem: connections to the Gaussian free field via large-scale linearization of the Monge-Ampere equation

By Felix Otto

Appears in collection : PDE/Probability Interactions: Particle Systems, Hyperbolic Conservation Laws / Interactions EDP/Probabilités : systèmes de particules, lois de conservation hyperboliques

The optimal transport between a random atomic measure described by the Poisson point process and the Lebesgue measure in d-dimensional space has received attention in diverse communities. Heuristics suggest that on large scales, the displacement potential, which is a solution of the highly nonlinear Monge-Ampere equation with a rough right hand side, behaves like the solution of its linearization, the Poisson equation driven by white noise. Most interesting is the case of dimension d=2, when the displacement inherits the logarithmic divergence of the Gaussian free field. For a large torus, this has been made rigorous on the macroscopic level (i.e. on the size of the torus) by recent work of Ambrosio.et.al. We show that this is also true on the microscopic level (i.e. on the scale of the point process). The argument relies on a new and purely variational approach to the (Schauder) regularity theory for the Monge-Ampere equation, which allows for a rough right hand side, and which amounts to a quantitative linearization on all (intermediate) scales. This deterministic approach allows to feed in the existing stochastic estimates. This is joint work with M.Goldman and M.Huesmann.

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Citation data

  • DOI 10.24350/CIRM.V.19570403
  • Cite this video OTTO, Felix (15/10/2019). The matching problem: connections to the Gaussian free field via large-scale linearization of the Monge-Ampere equation. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19570403
  • URL https://dx.doi.org/10.24350/CIRM.V.19570403

Bibliography

  • GOLDMAN, Michael, HUESMANN, Martin, et OTTO, Felix. A large-scale regularity theory for the Monge-Ampère equation with rough data and application to the optimal matching problem. arXiv preprint arXiv:1808.09250, 2018. - https://arxiv.org/abs/1808.09250

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