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In stochastic optimisation, some quantity is to be minimised subject to certain, random, constraints. Often, in order to quantify the randomness, scenarios are formed. However, optimizing with respect to each scenario is computationally costly. Moreover, it is often difficult to see how a change in assumptions changes the optimal solution. I will discuss how applying unsupervised clustering methods to the scenarios can lead to better understanding of the problem and lead to new heuristics, upper and lower bounds.

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