Scenario Reduction Revisited: Fundamental Limits and Guarantees

2017/01/15 by Napat Rujeerapaiboon, Rujeerapaiboon, Napat, Kilian Schindler +5 · 5 citations
Decision Sciences · Engineering · #Probabilistic and Robust Engineering Design #Risk and Portfolio Optimization #Sparse and Compressive Sensing Techniques

paper · pdf · doi:10.48550/arxiv.1701.04072

Abstract

The goal of scenario reduction is to approximate a given discrete distribution with another discrete distribution that has fewer atoms. We distinguish continuous scenario reduction, where the new atoms may be chosen freely, and discrete scenario reduction, where the new atoms must be chosen from among the existing ones. Using the Wasserstein distance as measure of proximity between distributions, we identify those n-point distributions on the unit ball that are least susceptible to scenario reduction, i.e., that have maximum Wasserstein distance to their closest m-point distributions for some prescribed m

Citations

• Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations

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• HGCN2SP: Hierarchical Graph Convolutional Network for Two-Stage Stochastic Programming
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• Distributional Robustness in Output Feedback Regret-Optimal Control

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