Data-Pooling in Stochastic Optimization

Understand

Managing large-scale systems often involves simultaneously solving thousands of unrelated stochastic optimization problems, each with limited data.

• Intuition suggests one can decouple these unrelated problems and solve them separately without loss of generality.
• We propose a novel data-pooling algorithm called Shrunken-SAA that disproves this intuition.
• In particular, we prove that combining data across problems can outperform decoupling, even when there is no a priori structure linking the problems and data are drawn independently.