Optimizing Neural Networks via Koopman Operator Theory (Supplemental Material)

As discussed in Sec. 3 of the main text, the computational complexity of Koopman training is We assume that both standard training and Koopman training use simple matrix computation methods. We note that none of these factors are relevant for Koopman training. The finite section method, Eq. 4, implies the run time complexity would be The authors contributed equally 34th Conference on Neural Information Processing Systems (NeurIPS 2020), V ancouver, Canada. Koopman operator(s) and evolve each partition separately from the others. In Sec. 3, we discussed when we think this "patching" approach should give small errors.