[frankling_]

The key point is that Ceres requires derivatives, which can come from manually derived formulae, approximations via finite differences, or autodiff (http://ceres-solver.org/derivatives.html). DiscoGrad doesn't do the optimization itself (for that, we use gradient descent, for example via Adam), but essentially represents a fourth option to obtain derivatives, and one which captures the branches in an optimization problem (which autodiff doesn't).

While I'm not super familiar with the typical use cases for Ceres, the gradient estimator from DiscoGrad could possibly be integrated to better handle branchy problems.