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Pré-Publication, Document De Travail Année : 2016

Convergence of a Newton algorithm for semi-discrete optimal transport

Résumé

Many problems in geometric optics or convex geometry can be recast as optimal transport problems and a popular way to solve these problems numerically is to assume that the source probability measure is absolutely continuous while the target measure is finitely supported. We introduce a damped Newton's algorithm for this type of problems, which is experimentally efficient, and we establish its global linear convergence for cost functions satisfying an assumption that appears in the regularity theory for optimal transport.
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Dates et versions

hal-01290496 , version 1 (18-03-2016)

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Jun Kitagawa, Quentin Mérigot, Boris Thibert. Convergence of a Newton algorithm for semi-discrete optimal transport. 2016. ⟨hal-01290496⟩
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