  Reduced modeling in high-dimensional reproducing kernel Hilbert spaces offers
the opportunity to approximate efficiently non-linear dynamics. In this work,
we devise an algorithm based on low rank constraint optimization and
kernel-based computation that generalizes a recent approach called
"kernel-based dynamic mode decomposition". This new algorithm is characterized
by a gain in approximation accuracy, as evidenced by numerical simulations, and
in computational complexity.
