The paper defines a distance between measures, gives an explicit recuperation operator, and proves that the resulting approximation error bounds are optimal for measures of finite total variation.
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Super-resolution meets machine learning: approximation of measures
The paper defines a distance between measures, gives an explicit recuperation operator, and proves that the resulting approximation error bounds are optimal for measures of finite total variation.