A Unified Algebraic Framework for Subspace Pruning in Koopman Operator Approximation via Principal Vectors
For researchers in dynamical systems and Koopman operator theory, this provides a more efficient method for identifying nearly invariant subspaces, though the improvement is incremental.
The paper proposes an algebraic framework for subspace pruning in Koopman operator approximation using principal vectors, achieving an order-of-magnitude reduction in computational complexity for tracking principal angles via rank-one updates.
Finite-dimensional approximations of the Koopman operator rely critically on identifying nearly invariant subspaces. This invariance proximity can be rigorously quantified via the principal angles between a candidate subspace and its image under the operator. To systematically minimize this error, we propose an algebraic framework for subspace pruning utilizing principal vectors. We establish the equivalence of this approach to existing consistency-based methods while providing a foundation for broader generalizations. To ensure scalability, we introduce an efficient numerical update scheme based on rank-one modifications, reducing the computational complexity of tracking principal angles by an order of magnitude. Finally, we demonstrate the effectiveness of our framework through numerical simulations.