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The topic of learning to solve optimization problems has received interestfrom both the operations research and machine learning communities. In thiswork, we combine techniques from both fields to address the problem of learningto generate decisions to instances of continuous optimization problems wherethe feasible set varies with contextual features. We propose a novel frameworkfor training a generative model to estimate optimal decisions by combininginterior point methods and adversarial learning, which we further embed withinan data generation algorithm. Decisions generated by our model satisfyin-sample and out-of-sample optimality guarantees. Finally, we investigate casestudies in portfolio optimization and personalized treatment design,demonstrating that our approach yields advantages over predict-then-optimizeand supervised deep learning techniques, respectively.

Learning to Optimize Contextually Constrained Problems for Real-Time Decision-Generation