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In this paper, we propose formulations and algorithms for robust portfolio optimization under both aleatory uncertainty (i.e., natural variability) and epistemic uncertainty (i.e., imprecise probabilistic information) arising from interval data. Epistemic uncertainty is represented using two...
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smooth concave objective functions, and develop a theory for data-driven calibration of the non-negative “robustness …
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