English

Uniformly ergodic probability measures

Functional Analysis 2023-12-14 v3

Abstract

Let GG be a locally compact group and μ\mu be a probability measure on GG. We consider the convolution operator λ1(μ) ⁣:L1(G)L1(G)\lambda_1(\mu)\colon L_1(G)\to L_1(G) given by λ1(μ)f=μf\lambda_1(\mu)f=\mu \ast f and its restriction λ10(μ)\lambda_1^0(\mu) to the augmentation ideal L10(G)L_1^0(G). Say that μ\mu is uniformly ergodic if the Ces\`aro means of the operator λ10(μ)\lambda_1^0(\mu) converge uniformly to 0, that is, if λ10(μ)\lambda_1^0(\mu) is a uniformly mean ergodic operator with limit 0 and that μ\mu is uniformly completely mixing if the powers of the operator λ10(μ)\lambda_1^0(\mu) converge uniformly to 0. We completely characterize the uniform mean ergodicity of the operator λ1(μ)\lambda_1(\mu) and the uniform convergence of its powers and see that there is no difference between λ1(μ)\lambda_1(\mu) and λ10(μ)\lambda_1^0(\mu) in this regard. We prove in particular that μ\mu is uniformly ergodic if and only if GG is compact, μ\mu is adapted (its support is not contained in a proper closed subgroup of GG) and 1 is an isolated point of the spectrum of μ\mu. The last of these three conditions is actually equivalent to μ\mu being spread-out (some convolution power of μ\mu is not singular). The measure μ\mu is uniformly completely mixing if and only if GG is compact, μ\mu is spread-out and the only unimodular value of the spectrum of μ\mu is 1.

Keywords

Cite

@article{arxiv.2301.10096,
  title  = {Uniformly ergodic probability measures},
  author = {Jorge Galindo and Enrique Jordá and Alberto Rodríguez-Arenas},
  journal= {arXiv preprint arXiv:2301.10096},
  year   = {2023}
}

Comments

Final Version. To appear in Publicacions Matem\`atiques

R2 v1 2026-06-28T08:18:46.865Z