论文标题
从点值完全恢复：chebyshev近似性的最佳算法之前
Full Recovery from Point Values: an Optimal Algorithm for Chebyshev Approximability Prior
论文作者
论文摘要
给定属于某个模型集的未知函数的示给定示给定样品，以最佳恢复以最小化恢复过程的最坏情况误差的方式来恢复此功能。虽然通常知道可以选择这种最佳恢复程序为线性，例如当模型集基于连续函数的子空间基于近似性时，该过程的构造很少可用。当近似空间是尺寸至少三个且包含常数函数时，本说明会发现实用算法以构建线性最佳恢复图的实用算法。
Given pointwise samples of an unknown function belonging to a certain model set, one seeks in Optimal Recovery to recover this function in a way that minimizes the worst-case error of the recovery procedure. While it is often known that such an optimal recovery procedure can be chosen to be linear, e.g. when the model set is based on approximability by a subspace of continuous functions, a construction of the procedure is rarely available. This note uncovers a practical algorithm to construct a linear optimal recovery map when the approximation space is a Chevyshev space of dimension at least three and containing the constant functions.
