Abstract
Distribution-robust loss-averse optimization optimizes a nominal value with some protection against downside loss, under the assumption that only partial information on the underlying distribution is available. We herein present a general modeling framework for the distribution-robust loss-averse optimization problem. We provide an equivalent simpler formulation that usually permits a tractable solution procedure. We then explore the modeling framework’s relations with traditional robust optimization and mean-variance optimization. Additionally, we discuss extensions to stochastic linear programming.
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