Randomized Dynamic Mode Decomposition

02/07/2017

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by   N. Benjamin Erichson, et al.

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berkeley college

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University of Washington

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This paper presents a randomized algorithm for computing the near-optimal low-rank dynamic mode decomposition (DMD). Randomized algorithms are emerging techniques to compute low-rank matrix approximations. They are able to ease the computational challenges arising in the area of big data. The idea is to derive from the high-dimensional input matrix a smaller matrix, which is then used to efficiently compute the dynamic modes and eigenvalues. The algorithm is presented in a modular probabilistic framework, and the approximation quality can be controlled via oversampling, and power iterations.