The reachable subspace dynamics of LTI systems remain uniquely identifiable from any experiment, even when the full system is not.
SIAM, 2008
2 Pith papers cite this work. Polarity classification is still indexing.
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Nora is a matrix optimizer that stabilizes weight norms and angular velocities through row-wise momentum projection onto the orthogonal complement of the weights while approximating structured preconditioning with O(mn) complexity and proven scalability.
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Limits of Learning Linear Dynamics from Experiments
The reachable subspace dynamics of LTI systems remain uniquely identifiable from any experiment, even when the full system is not.
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Nora: Normalized Orthogonal Row Alignment for Scalable Matrix Optimizer
Nora is a matrix optimizer that stabilizes weight norms and angular velocities through row-wise momentum projection onto the orthogonal complement of the weights while approximating structured preconditioning with O(mn) complexity and proven scalability.