Koopman Theory and Linear Approximation Spaces

• Published in: arXiv.org
• Main Authors: Kurdila, Andrew J, Bobade, Parag
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 27.11.2018
• Subjects: Approximation; Convergence; Eigenvectors; Errors; Estimates; Learning theory; Mathematical analysis; Operators; Statistical analysis; Statistical methods; Wavelet
• ISSN: 2331-8422

Koopman theory studies dynamical systems in terms of operator theoretic properties of the Perron-Frobenius operator \(\mathcal{P}\) and Koopman operator \(\mathcal{U}\) respectively. In this paper, we derive the rates of convergence of approximations of \(\mathcal{P}\) or \(\mathcal{U}\) that are generated by finite dimensional bases like wavelets, multiwavelets, and eigenfunctions, as well as approaches that use samples of the input and output of the system in conjunction with these bases. We introduce a general class of priors that describe the information available for constructing such approximations and facilitate the error estimates in many applications of interest. These priors are defined in terms of the action of \(\mathcal{P}\) or \(\mathcal{U}\) on certain linear approximation spaces. The rates of convergence for the estimates of these operators are investigated under a variety of situations that are motivated from associated assumptions in practical applications. When the estimates of these operators are generated by samples, it is shown that the error in approximation of the Perron-Frobenius or Koopman operators can be decomposed into two parts, the approximation error and the sampling error. This result emphasizes that sample-based estimates of Perron-Frobenius and Koopman operators are subject to the well-known trade-off between the bias and variance that contribute to the error, a balance that also features in nonlinear regression and statistical learning theory.