  Designing faster optimization algorithms is of ever-growing interest. In
recent years, learning to learn methods that learn how to optimize demonstrated
very encouraging results. Current approaches usually do not effectively include
the dynamics of the optimization process during training. They either omit it
entirely or only implicitly assume the dynamics of an isolated parameter. In
this paper, we show how to utilize the dynamic mode decomposition method for
extracting informative features about optimization dynamics. By employing those
features, we show that our learned optimizer generalizes much better to unseen
optimization problems in short. The improved generalization is illustrated on
multiple tasks where training the optimizer on one neural network generalizes
to different architectures and distinct datasets.
