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 is
Each 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.
The matrix trace is linear, and
Taking the trace of the spectral decomposition therefore proves
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 are
They 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 contribute
Adding the third projection gives
Multiplication by gives the identity; in particular the upper block satisfies .
Summing the diagonal entries gives
The 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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