End-to-End Learning for Constrained Optimization

In this thesis, we study the integration of constrained optimization algorithms with the training
of deep neural networks. In particular, we we primarily interested in end-to-end trainable prediction
and decision models composed of differentiable components. The use of such techniques spans
several broad application areas, which we divide into two categories. When Learning to Optimize,
the goal is to train neural networks to solve or aid in the solution of constrained optimization
problems. In the Predict-Then-Optimize setting, the goal is to optimize decisions under uncertainty
by estimating unknown coefficients in optimization problems from correlated data. Both frameworks
stem from efforts to enhance optimization modeling technology for operations research and decision
making tasks. This thesis contributes to both areas, and seeks to combine techniques from each to
enhance the expressive and computational ability of models that learn to make decisions.