Predictive analytics, empowered by machine learning, is usually followed by decision-making problems in prescriptive analytics. We extend the above sequential prediction-optimization paradigm to a coupled scheme such that the prediction model can guide the decision problem to produce coordinated decisions yielding higher levels of performance. Speci fically, for stochastic programming (SP) models with latently decision-dependent uncertainty, we develop a coupled learning enabled optimization (CLEO) algorithm in which the learning step of predicting the latent dependency and the optimization step of computing a candidate decision are conducted interactively. The CLEO algorithm automatically balances the trade-off between the accuracy of learning models and the complexity of the composite decision-making problem. Under certain assumptions, we show that the sequence of solutions provided by CLEO converges to a first-order stationary point of the original SP problem with probability 1. In addition, we present preliminary computations which demonstrate the computational potential of this data-driven approach.
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Institution: University of Southern California
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