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Published January 2021 | public
Journal Article Open

Function approximation via the subsampled Poincaré inequality


Function approximation and recovery via some sampled data have long been studied in a wide array of applied mathematics and statistics fields. Analytic tools, such as the Poincaré inequality, have been handy for estimating the approximation errors in different scales. The purpose of this paper is to study a generalized Poincaré inequality, where the measurement function is of subsampled type, with a small but non-zero lengthscale that will be made precise. Our analysis identifies this inequality as a basic tool for function recovery problems. We discuss and demonstrate the optimality of the inequality concerning the subsampled lengthscale, connecting it to existing results in the literature. In application to function approximation problems, the approximation accuracy using different basis functions and under different regularity assumptions is established by using the subsampled Poincaré inequality. We observe that the error bound blows up as the subsampled lengthscale approaches zero, due to the fact that the underlying function is not regular enough to have well-defined pointwise values. A weighted version of the Poincaré inequality is proposed to address this problem; its optimality is also discussed.

Additional Information

© 2020 American Institute of Mathematical Sciences. Received December 2019; revised June 2020. The research was in part supported by NSF Grants DMS-1912654 and DMS-1907977. Y. Chen is supported by the Kortschak Scholars Program. We want to thank Professor Henri Berestycki and Jinchao Xu for their interest in our work and for bringing to our attention some of the relevant references. Y. Chen would like to thank Yousuf Soliman for many insightful discussions on the subsampled Poincaré inequality. We thank the anonymous reviewer for the helpful comments that improve this work.

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August 20, 2023
August 20, 2023