A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is
Discrete approximation of probability distributions is an important topic in stochastic programming. In this paper, we extend the research on this topic to distributionally robust optimization (DRO), where discretization is driven by either limited availability of empirical data (samples) or a computational need for improving numerical tractability. We start with a one-stage DRO where the ambiguity set is defined by generalized prior moment conditions and quantify the discrepancy between thedoi:10.1287/moor.2017.0911 fatcat:qyrepqf2yfgv3kzw4x2im7eqja