Papers › Constructive subsampling of finite frames with applications in optimal function recovery

Constructive subsampling of finite frames with applications in optimal function recovery

25 Feb 2022arXiv:2202.12625links table onlyarchive 2025-07-28

Felix Bartel, Martin Schäfer, Tino Ullrich

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In this paper we present new constructive methods, random and deterministic, for the efficient subsampling of finite frames in ℂᵐ. Based on a suitable random subsampling strategy, we are able to extract from any given frame with bounds 0<A≤B<∞ (and condition B/A) a similarly conditioned reweighted subframe consisting of merely 𝒪(mlogm) elements. Further, utilizing a deterministic subsampling method based on principles developed by Batson, Spielman, and Srivastava to control the spectrum of sums of Hermitian rank-1 matrices, we are able to reduce the number of elements to 𝒪(m) (with a constant close to one). By controlling the weights via a preconditioning step, we can, in addition, preserve the lower frame bound in the unweighted case. This permits the derivation of new quasi-optimal unweighted (left) Marcinkiewicz-Zygmund inequalities for L₂(D,ν) with constructible node sets of size 𝒪(m) for m-dimensional subspaces of bounded functions. Those can be applied e.g. for (plain) least-squares sampling reconstruction of functions, where we obtain new quasi-optimal results avoiding the Kadison-Singer theorem. Numerical experiments indicate the applicability of our results.

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