By the spectral theorem for real symmetric matrices, choose the normalized eigenvectors to form an orthonormal basis. With , orthonormality gives and . For every vector , expand and apply :Since this equality holds for every , the spectral decomposition isEach is the rank-one orthogonal projection onto its eigenvector direction. In a degenerate eigenspace, normalized eigenvectors must be chosen mutually orthogonal; normalization alone does not guarantee this resolution of the identity.
If every eigenvalue is nonzero, define , where . Using the mutually orthogonal projection identity ,The same calculation gives . Thus the matrix inverse is
The inverse fails to exist exactly when at least one eigenvalue is zero. In that case a nonzero eigenvector lies in the kernel of a linear map, so is not one-to-one. Equivalently, its determinant is zero. Conversely, if every eigenvalue is nonzero, the preceding construction gives an inverse.
The third coordinate is decoupled, giving the unit eigenvector with eigenvalue . In the first two coordinates, the symmetric direction has eigenvalue , and the antisymmetric direction has eigenvalue . Their normalized forms areThey are an orthonormal basis of ; independent sign reversals are equally valid choices.
Insert the eigenvalues and eigenvectors into the inverse spectral decomposition. The first two projections contributeAdding the third projection givesMultiplication by gives the identity; in particular the upper block satisfies .
Summing the diagonal entries givesThe eigenvalues of the inverse are the reciprocals of those of , whose sum is also . This is precisely what the trace identity predicts.
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