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. Specifically, 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.
Institution: University of Southern California
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