A positive matrix has a simple spectral-radius eigenvalue with positive left and right eigenvectors. Keeping the exact identity in view prevents the geometric or probabilistic intuition from drifting.
The mathematical object
Matrix factorizations expose different geometry: \(A=QR\) separates an orthonormal frame, while \(A=U\Sigma V^\ast\) separates rotations from axis scaling.
I keep the defining relation \(\mathsf D\) above the derived relation \(\mathsf C\). This exposes whether cancellation used \(x\ne0\) and whether the conclusion is canonical.
One explicit computation
The middle display is intentionally dense: it is where signs, bounds, multiplicities, or normalising factors are most likely to be lost.
Why the identity matters
The aligned summary deliberately puts the datum and conclusion on different rows. Mathematically, this is the distinction between specifying an object and proving a property of it.
Where it can fail
Conditioning matters numerically. An exact algebraic identity can be a poor computational method when it squares the condition number.
The notation is dense, but it is doing honest work: every delimiter records scope and every index records dependence. Removing one should require a mathematical reason.