Consider the original linear system
Completing the square proves that is a symmetric positive-definite matrix:
The Jacobi preconditioner has . Symmetric positive-definite preconditioning gives
Its eigenvectors and have eigenvalues and . Hence
The characteristic polynomial of is . Its eigenvalues and spectral condition number of a positive-definite matrix are
The ellipse axis ratio drops from about to . The transformation multiplies the second coordinate by ten, bringing the two energy scales together.
Figure 1.
Diagonal scaling changes thin energy ellipses into nearly circular energy ellipses
. Details.
Both panels show the same energy level sets, expressed in error coordinates and . Their coordinate axes have equal scales. The marked displacement becomes ; its energy remains . These are exact contours of the specified quadratic form.
Solving the original equations gives
Direct matrix multiplication checks both linear-system residuals:
By contrast, left preconditioning gives , which is visibly not a symmetric matrix, despite having the same positive eigenvalues as .
A bad scaling can reverse the benefit: the still valid symmetric positive-definite matrix gives . Its Rayleigh quotients on the coordinate unit vectors are and , so . Cheapness and positive definiteness do not guarantee improved conditioning.

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