Sparse Lerner operators in infinite dimensions

TL;DR

This paper proves boundedness of sparse Lerner operators on matrix- or operator-weighted spaces in infinite dimensions, extending classical results and providing sharper bounds with applications to multi-parameter analysis.

Contribution

It introduces a new proof technique for sparse Lerner operators in infinite dimensions and extends bounds to multi-parameter and operator-valued settings.

Findings

Boundedness characterized by Muckenhoupt A_2 condition

Dimension-independent bounds in terms of mixed A_2-A_infinity conditions

Sharper bounds for the maximal Bergman projection

Abstract

We use the principle of almost orthogonality to give a new and simple proof that a sparse Lerner operator is bounded on a matrix- or operator-weighted space $L_W^{2}(\mu)$, where $\mu$ is a doubling measure on $\R^d$ if and only if the weight $W$ satisfies the Muckenhoupt $A_2(\mu)$-condition, restricted to the sparse collection in question. Our method extends to the infinite-dimensional setting, thus allowing for applications to the multi-parameter setting. For the class of Muckenhoupt $A_2$-weights, we obtain bounds in terms of mixed $A_{2}(\mu)$-$A_{\infty}(\mu)$-conditions, which is independent of dimension and agrees with the best known bound in the finite-dimensional vectorial setting. As an application, we prove a matrix-weighted bound for the maximal Bergman projection, where we obtain a new sharper bound in terms of the B\'ekoll\'e-Bonami characteristic. Furthermore, we consider commutators of sparse Lerner operators on operator-valued weighted $L^{2}$-spaces and some applications to multi-parameters