 Solution

Use the inner product convention linear in its first argument. A singular value system consists of positive singular values ${\sigma }_n$ and orthonormal bases $u_n$ of $(\ker A{)}^{\perp }\subset X$ and $v_n$ of $\overline{\mathrm{ran}A}\subset Y$, withFor an infinite-rank compact operator, ${\sigma }_n\to 0$; a finite-rank operator has a finite system. Applying the spectral theorem for compact self-adjoint operators to $A^{\ast }A$ constructs $u_n$, with eigenvalues ${\sigma }_n^2$, and then $v_n=A{u}_n/{\sigma }_n$. This solution uses the input/output naming of this problem, which is the reverse of another common singular-vector convention.

The Moore–Penrose inverse of an operator isIts domain is $\mathrm{ran}A\oplus (\mathrm{ran}A{)}^{\perp }$, equivalently the data satisfying the Picard criterionIt ignores the component in $\ker A^{\ast }$ and lies in $(\ker A{)}^{\perp }$. The series then converges in $X$ by orthonormality and is the minimum-norm least-squares solution. If $y\in \mathrm{ran}A$, it is an exact solution; all other exact solutions are $x^{†}+z$ with $z\in \ker A$. Compactness does not make the inverse bounded when there are infinitely many positive singular values.