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Online optimization with multiple budget constraints is challenging since theonline decisions over a short time horizon are coupled together by strictinventory constraints. The existing manually-designed algorithms cannot achievesatisfactory average performance for this setting because they often need alarge number of time steps for convergence and/or may violate the inventoryconstraints. In this paper, we propose a new machine learning (ML) assistedunrolling approach, called LAAU (Learning-Assisted Algorithm Unrolling), whichunrolls the online decision pipeline and leverages an ML model for updating theLagrangian multiplier online. For efficient training via backpropagation, wederive gradients of the decision pipeline over time. We also provide theaverage cost bounds for two cases when training data is available offline andcollected online, respectively. Finally, we present numerical results tohighlight that LAAU can outperform the existing baselines.

Learning-Assisted Algorithm Unrolling for Online Optimization with Budget Constraints