  The use of blackbox solvers inside neural networks is a relatively new area
which aims to improve neural network performance by including proven, efficient
solvers for complex problems. Existing work has created methods for learning
networks with these solvers as components while treating them as a blackbox.
This work attempts to improve upon existing techniques by optimizing not only
over the primary loss function, but also over the performance of the solver
itself by using Time-cost Regularization. Additionally, we propose a method to
learn blackbox parameters such as which blackbox solver to use or the heuristic
function for a particular solver. We do this by introducing the idea of a
hyper-blackbox which is a blackbox around one or more internal blackboxes.
