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Regularization replaces sensitive inversion by a controlled approximation. For a bounded linear operator between Hilbert spaces, a linear regularization is a family of bounded linear operators $R_{\alpha }$ satisfying $R_{\alpha }y\to A^{†}y$ as $\alpha \downarrow 0$ for each datum in the domain of the Moore–Penrose inverse of an operator. Noisy-data convergence additionally requires a suitable regularization parameter choice.^[ref] Nonlinear variational regularization can instead use nonquadratic penalties.

The noise-bias decomposition for linear regularization separates two costs: amplifying measurement noise and changing the exact-data reconstruction. Truncated singular value decomposition discards weak modes; Tikhonov regularization attenuates them smoothly. A quadratic norm penalty expresses a preference for smaller reconstructions. Neither method manufactures a missing null space component: with complete averaging, the minimum-norm least-squares solution for $(0.5,0.5{)}^T$ is $(0.5,0.5{)}^T$, even if the original image was $(1,0{)}^T$. The preference selects a representative; extra observations or justified constraints are needed to identify the original.

For Tikhonov regularization, the Tikhonov stability bound limits transmitted measurement noise of norm at most $\delta$ to $\delta /(2\sqrt{\alpha })$. Taking $\alpha \to 0$ while $\delta /\sqrt{\alpha }\to 0$ gives a sufficient rule for convergent regularization of an inverse problem. Holding $\alpha$ fixed leaves approximation bias; letting it vanish too quickly can admit overwhelming measurement noise.

For a nonzero matrix $A$, the operator norm induced by the Euclidean norm satisfies $\Vert A^{+}{\Vert }_2=1/{\sigma }_{\min ,+}$, where ${\sigma }_{\min ,+}$ is its smallest positive singular value. The singular value decomposition makes the danger visible: a small nonzero stretch becomes a large amplification of errors when inverted.

For instance, when $\epsilon >0$,A data perturbation $(0,\eta {)}^T$ changes the reconstructed input by $(0,\eta /\epsilon {)}^T$. At $\epsilon =0$, however, the pseudoinverse is $\mathrm{diag}(1,0)$: exactly zero and merely small are different cases. Thus the pseudoinverse need not be continuous across a change of matrix rank. With uncertain data, truncated singular value decomposition or Tikhonov regularization can trade exact inversion of weak directions for stability; the cutoff or penalty adds a modelling choice.