Means in complete manifolds: uniqueness and approximation

Arnaudon, Marc; Miclo, Laurent

ESAIM: Probability and Statistics, Tome 18 (2014), pp. 185-206.

Let M be a complete Riemannian manifold, M ∈ ℕ and p ≥ 1. We prove that almost everywhere on x = (x₁,...,x_N) ∈ M^N for Lebesgue measure in M^N, the measure $d\mu \left(x\right)=\frac{1}{N}{\sum }_{k=1}^N{\delta }_{x_k}$ has a unique p-mean e_p(x). As a consequence, if X = (X₁,...,X_N) is a M^N-valued random variable with absolutely continuous law, then almost surely μ(X(ω)) has a unique p-mean. In particular if (X_n)_n ≥ 1 is an independent sample of an absolutely continuous law in M, then the process e_p,n(ω) = e_p(X₁(ω),...,X_n(ω)) is well-defined. Assume M is compact and consider a probability measure ν in M. Using partial simulated annealing, we define a continuous semimartingale which converges in probability to the set of minimizers of the integral of distance at power p with respect to ν. When the set is a singleton, it converges to the p-mean.

Classification : 60D05, 58C35, 37A30, 53C21, 60J65
Mots clés : stochastic algorithms, diffusion processes, simulated annealing, homogenization, probability measures on compact riemannian manifolds, intrinsic p-means, instantaneous invariant measures, Gibbs measures, spectral gap at small temperature

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