Solving optimization problems with unknown parameters often requires learning
a predictive model to predict the values of the unknown parameters and then
solving the problem using these values. Recent work has shown that including
the optimization problem as a layer in the model training pipeline results in
predictions of the unobserved parameters that lead to higher decision quality.
Unfortunately, this process comes at a large computational cost because the
optimization problem must be solved and differentiated through in each training
iteration; furthermore, it may also sometimes fail to improve solution quality
due to non-smoothness issues that arise when training through a complex
optimization layer. To address these shortcomings, we learn a low-dimensional
surrogate model of a large optimization problem by representing the feasible
space in terms of meta-variables, each of which is a linear combination of the
original variables. By training a low-dimensional surrogate model end-to-end,
and jointly with the predictive model, we achieve: i) a large reduction in
training and inference time; and ii) improved performance by focusing attention
on the more important variables in the optimization and learning in a smoother
space. Empirically, we demonstrate these improvements on a non-convex adversary
modeling task, a submodular recommendation task and a convex portfolio
optimization task.

通过将优化问题表示为元变量的线性组合，我们学习了大规模优化问题的低维代理模型。通过端到端地训练低维代理模型和预测模型，我们实现了训练和推断时间的大幅减少，同时通过关注优化中的重要变量和在更平滑的空间中学习来提高性能。