Now take one equation for two unknowns:
The linear system has an entire affine line of exact solutions. Since has full row rank, its Moore-Penrose inverse is
The kernel is , and all exact solutions are
Because is orthogonal to ,
The Euclidean norm is therefore smallest exactly at . The zero residual alone cannot select a solution; the minimum-norm rule supplies the additional criterion.
Figure 1.
The minimum-norm solution is the closest point of the solution line to the origin
. The affine line is perpendicular to . Moving along the line changes only a kernel component and increases by . Details.
This selection depends on the specified inner product. Rescaling one unknown changes what counts as a small Euclidean norm, so units and weighting matter in an application. A minimum-norm answer is mathematically canonical for the chosen geometry, rather than an automatic guarantee of physical plausibility.

Articles by others on the same topic (0)

There are currently no matching articles.