论文标题
计算最佳恢复的实例：处理观察错误
Instances of Computational Optimal Recovery: Dealing with Observation Errors
论文作者
论文摘要
尝试从观察数据中恢复功能时，就某些建模假设而自然而然地试图以最佳的方式这样做。将重点放在最坏情况下，这是最佳恢复的标准目标。这里的独特曲折是通过一些界限模型考虑了不准确的数据以及对计算可靠性的强调。通过最佳恢复图的有效结构来揭示几种情况：线性或半标准可描述的模型下的局部最优性，在近似性模型下估算线性功能的全局最优性，以及在连续函数空间中的近似值下的全局近似性。
When attempting to recover functions from observational data, one naturally seeks to do so in an optimal manner with respect to some modeling assumption. With a focus put on the worst-case setting, this is the standard goal of Optimal Recovery. The distinctive twists here are the consideration of inaccurate data through some boundedness models and the emphasis on computational realizability. Several scenarios are unraveled through the efficient constructions of optimal recovery maps: local optimality under linearly or semidefinitely describable models, global optimality for the estimation of linear functionals under approximability models, and global near-optimality under approximability models in the space of continuous functions.
