This is a small thing, but I'm really happy to see that their L-BFGS line search algorithm supports a maximum step size. Many numerical optimization libraries do not offer this simple feature, which is critical if your goal is to find a minimum that is close to the initial starting location. This is important in atomistic geometry optimization.
[1] The test cases can be seen as examples, obviously, but they are not written in an easily accessible form IMHO.
EDIT: Looking more closely, it seems you need to provide an explicit gradient, no autodiff included, which makes it a complete nonstarter for me.
Ceres also does work for dense problems and general convex optimization, but it doesn't provide as much of an advantage over other packages (that is, you could just call into any existing l-bfgs implementation and wouldn't benefit much).
For some other problems, Ceres doesn't scale or can get in the way. For example, if you have a single value to maximize and a large number of variables, then ceres' auto-diff system won't work (ceres::Jet is based on "forward-mode" automatic differentiation, storing partial derivatives on the stack. If you have a high-dimensional gradient, it can exhaust stack space). On the other hand, Ensmallen doesn't seem to come with any auto-diff functionality.
;)