Matrix Spectral Norm and Optimal Low-Rank Approximation


 Matrix Spectral Norm and Optimal Low-Rank Approximation


This study examines the spectral norm as a fundamental measure in matrix approximation theory. Using singular value decomposition (SVD), operator norms, and perturbation analysis, we derive conditions for best low-rank approximations under the spectral norm.

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