Linear regularization

A linear regularization uses a family of bounded linear operators $R_{\alpha }$ such that $R_{\alpha }f\to K^{†}f$ for each $f\in \mathcal{D}(K^{†})$. A regularization parameter choice must balance approximation error with noise amplification to obtain a convergent regularization of an inverse problem.

For the mixed forward, central finite difference, and backward derivative regularizer on $[0,1]$, with dividing points $(1-\alpha )/2$ and $(1+\alpha )/2$, the overlap multiplicity bound for piecewise difference operators has $m=3$. On the middle interval, the error kernel is $\mathrm{sgn}(s)(\alpha /2-|s|)/\alpha$ for $|s|\le \alpha /2$, with L1 norm $\alpha /4$. Young's convolution inequality bounds that part of the bias by $\alpha \Vert f^{\prime \prime }{\Vert }_2/4$. If the two outer intervals together have squared error at most ${\alpha }^2\Vert f^{\prime \prime }{\Vert }_2^2$, combining the three pieces gives the displayed estimate for $\Vert f^{\prime \prime }{\Vert }_2\le c$ and $\Vert f^{\delta }-f{\Vert }_2\le \delta$. Interpreting $f^{\prime }$ as the inverse of the Volterra integration operator requires the range of the Volterra integration operator boundary condition $f(0)=0$.

Suppose a piecewise finite difference uses two translated evaluations divided by $\alpha$. If the pulled-back evaluation intervals cover almost every input point at most $m$ times, then $|a-b{|}^2\le 2(|a{|}^2+|b{|}^2)$ gives $\Vert R_{\alpha }f{\Vert }_2^2\le 2m\Vert f{\Vert }_2^2/{\alpha }^2$. This converts an interval-overlap count into an operator norm estimate without requiring pointwise values of an L2 space equivalence class outside the almost-everywhere evaluation rule.