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Means in complete manifolds: uniqueness and approximation

Published online by Cambridge University Press:  27 March 2014

Marc Arnaudon and
Laurent Miclo
Marc Arnaudon
Affiliation:
Laboratoire de Mathématiques et Applications, CNRS: UMR 7348, Université de Poitiers, Téléport 2 – BP 30179, 86962 Futuroscope Chasseneuil Cedex, France. marc.arnaudon@math.univ-poitiers.fr
Laurent Miclo
Affiliation:
Institut de Mathématique de Toulouse, CNRS: UMR 5219, 118, route de Narbonne, 31062 Toulouse Cedex 9, France; laurent.miclo@math.univ-toulouse.fr

Abstract

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Let M be acomplete Riemannian manifold, M ∈ ℕ andp ≥ 1. Weprove that almost everywhere on x = (x1,...,xN) ∈ MNfor Lebesgue measure in MN, the measure \hbox{$\di \mu(x)=\f1N\sum_{k=1}^N\d_{x_k}$} has a unique p–mean ep(x).As a consequence, if X = (X1,...,XN)is a MN-valued randomvariable with absolutely continuous law, then almost surely μ(X(ω)) has aunique p–mean. In particular if (Xn)n ≥ 1is an independent sample of an absolutely continuous law in M, then the processep,n(ω) = ep(X1(ω),...,Xn(ω))is well-defined. Assume M is compact and consider a probability measureν inM. Usingpartial simulated annealing, we define a continuous semimartingale which converges inprobability to the set of minimizers of the integral of distance at power p with respect toν. When theset is a singleton, it converges to the p–mean.

Keywords

Stochastic algorithmsdiffusion processessimulated annealinghomogenizationprobability measures on compact Riemannian manifoldsintrinsic p-meansinstantaneous invariant measuresGibbs measuresspectral gap at small temperature

Information

Type
Research Article
Information
ESAIM: Probability and Statistics , Volume 18 , 2014, pp. 185 - 206
Copyright
© EDP Sciences, SMAI, 2014

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