We show that the notion of polynomial mesh (norming set), used to provide discretizations of a compact set nearly optimal for certain approximation theoretic purposes, can also be used to obtain finitely supported near G-optimal designs for polynomial regression. We approximate such designs by a standard multiplicative algorithm, followed by measure concentration via Caratheodory-Tchakaloff compression.

中文翻译：

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近G最优Tchakaloff设计

我们表明，多项式网格（范数集）的概念用于提供对于某些近似理论而言几乎最佳的紧集的离散化，也可以用于获得多项式回归的有限支持的近G最优设计。我们通过标准的乘法算法对此类设计进行近似，然后通过Caratheodory-Tchakaloff压缩来测量浓度。