Strong consistency of the projected total least squares dynamic mode decomposition for datasets with random noise

DOI10.1007/s13160-022-00547-6OpenAlexW4306249733MaRDI QIDQ2111579

Kensuke Aishima

Publication date: 17 January 2023

Published in: Japan Journal of Industrial and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13160-022-00547-6

zbMATH Keywords

strong convergencesingular value decompositioneigenvalue problemstotal least squaresdynamic mode decomposition

Mathematics Subject Classification ID

Numerical computation of eigenvalues and eigenvectors of matrices (65F15) Eigenvalues, singular values, and eigenvectors (15A18) Time series analysis of dynamical systems (37M10) Limit theorems for vector-valued random variables (infinite-dimensional case) (60B12)

Cites Work

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• Ergodic Theory, Dynamic Mode Decomposition, and Computation of Spectral Properties of the Koopman Operator
• Variable Projection Methods for an Optimized Dynamic Mode Decomposition
• Centering Data Improves the Dynamic Mode Decomposition
• Strong convergence for the dynamic mode decomposition based on the total least squares to noisy datasets
• Consistent Dynamic Mode Decomposition

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