The paper introduces manifold-based algorithms and initializations for Hadamard decomposition, reformulating it as a low-rank factorization on manifolds and demonstrating efficiency on synthetic and real data.
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A Riemannian variable-projection method computes the nearest matrix with multiple eigenvalues (and structured variants) by minimizing a closed-form objective over the Stiefel manifold.
Exact conditions and bounds are derived for when robust asymptotic stability is lost in dissipative Hamiltonian DAEs under structure-preserving perturbations.
citing papers explorer
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Manifold-based Algorithms for the Hadamard Decomposition
The paper introduces manifold-based algorithms and initializations for Hadamard decomposition, reformulating it as a low-rank factorization on manifolds and demonstrating efficiency on synthetic and real data.
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Nearest matrix with multiple eigenvalues by Riemannian optimization
A Riemannian variable-projection method computes the nearest matrix with multiple eigenvalues (and structured variants) by minimizing a closed-form objective over the Stiefel manifold.
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Characterization of stability radii for robustly asymptotically stable dissipative Hamiltonian differential-algebraic systems
Exact conditions and bounds are derived for when robust asymptotic stability is lost in dissipative Hamiltonian DAEs under structure-preserving perturbations.