A nice addition to the package could be checkpointing the state of the optimizer in order to restart the optimization e.g. when an optimization run has finished or crashed.

This could also be interesting for issue #30 in order to restart the optimization with a higher computational budget.

PR #6 has already touched this topic and it might make sense to get some inspiration form there.

We could then also adjust the pytorch example to feature this.