A minimum-norm least-squares solution minimizes ∥Ax−y∥ and, among all such minimizers, minimizes ∥x∥. It lies in (kerA)⊥ and equals A†y when the Moore–Penrose inverse of an operator is defined at y.
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- Moore–Penrose inverse of an operator
- Normal equation for a linear inverse problem
- Inverse problem
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- Elastic net regularization in Hilbert sequence space
- Gaussian posterior in the zero-noise limit
- Landweber relaxation parameter
- Moore-Penrose inverse
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- Penrose equations
- Quartic-norm variational regularization
- Regularization of an inverse problem
- Strong convergence of relaxed Landweber iteration
- Vanishing-penalty ridge limit
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