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arXiv · 2512.24953

Resolvent-Based Singular-Value Diagnostics for Data-Driven Koopman Finite Sections

Yuanchao Xu·Itsushi Sakata·Isao Ishikawa
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Abstract

Finite-dimensional Koopman eigenvalues do not characterize resolvent growth, particularly for nonnormal compressions. We study the singular-value structure of empirical Koopman finite sections in the inner product induced by the data. Resolvent Dynamic Mode Decomposition (Resolvent DMD) removes unresolved Gram directions and then forms the Koopman or generator compression in a Gram-orthonormal basis. At a prescribed spectral parameter, the reciprocal of the smallest shifted singular value is the resolvent norm on the retained observable space. The associated right and left singular vectors give a minimum-residual pseudomode and the corresponding optimal forcing direction. For deterministic unregularized data, the squared ResDMD residual admits an orthogonal decomposition into a projected shifted residual and an invariance defect. The finite-section singular value is therefore a lower bound for the minimum ResDMD residual on the same space. We then identify stability assumptions that connect the finite sections with the underlying Koopman operator. Pointwise stability gives one-sided inclusion of inverse-resolvent sublevel sets. Stability on a punctured isolating neighborhood obtains local Hausdorff convergence near an isolated eigenvalue, while multiplicity stability preserves the algebraic count. Contractive, measure-preserving, and compact settings provide concrete sufficient conditions. The numerical examples examine coordinate dependence, finite-data residuals, contour calculations, and pseudomode separation for a noisy two-frequency signal.

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Yuanchao Xu, Itsushi Sakata, Isao Ishikawa. 2025-12-31. Resolvent-Based Singular-Value Diagnostics for Data-Driven Koopman Finite Sections. https://arxiv.org/abs/2512.24953

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