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arXiv · 2512.19988

Quasi-interpolation with random sampling centers

Wenwu Gao·Le Hu·Xingping Sun·Xuan Zhou
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Abstract

We propose and study a general quasi-interpolation framework for stochastic function approximation, which stems and draws motivation from convolution-type solutions for certain practical weighted variational problems. We obtain our quasi-interpolants using Monte Carlo discretization of the pertinent integrals and establish a family of $L^p$-McDiarmid-type concentration inequalities for $1\leq p\leq \infty$, which resulted in verifiable expected error estimates for the stochastic quasi-interpolants. The $L^1$-version of these concentration inequalities is dynamically-independent of dimensions, which offers a partial stochastic mitigation of the so called ``curse of dimensionality". The $L^\infty$-version of these concentration inequalities strengthens the existing expected $L^\infty$-error estimates in the literature. Numerical simulation results are provided at the end of the paper to validate the underlying theoretical analysis.

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Wenwu Gao, Le Hu, Xingping Sun, Xuan Zhou. 2025-12-23. Quasi-interpolation with random sampling centers. https://arxiv.org/abs/2512.19988

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