Dynamic mode decomposition: an alternative algorithm for full-rank datasets

Dynamic mode decomposition (DMD) is a modal decomposition technique that describes high-dimensional dynamic data using coupled spatial-temporal modes. It combines the main features of performing principal component analysis (PCA) in space, and power spectral analysis in time. The method is equation-free in the sense that it does not require knowledge of the underlying governing equations and is entirely data-driven. The purpose of this paper is to introduce a new algorithm for computing the dynamic mode decomposition in the case of full rank data. The new approach is more economical from a computational point of view, which is an advantage when working with large datasets.