The characteristic polynomials, in the convention , are
Solving the eigenvalue equations gives the complete eigenspaces
Every nonzero vector in a listed eigenspace is an eigenvector, and these are all the eigenvectors. For , the repeated eigenvalue has a two-dimensional eigenspace; for , all three eigenvalues are simple. The three displayed generating vectors form a basis because their column matrix
has determinant . Thus both matrices are diagonalizable, with diagonal entries for and for in this basis.
Suppose and for an invertible and diagonal matrices . Since diagonal matrices commute,
Therefore simultaneous diagonalization implies commutation. This argument works over either or and explains why simultaneous diagonalization produces commuting operators.
Let with . Commutation gives
Thus belongs to the eigenspace , with the zero vector allowed. Since has distinct eigenvalues, each eigenspace is one-dimensional. Hence for some scalar . This is the fact that commuting maps preserve eigenspaces.
The standard linear independence theorem for eigenvectors belonging to distinct eigenvalues supplies a basis of eigenvectors of . The preceding argument makes every one an eigenvector of as well. Their column matrix therefore simultaneously diagonalizes and . This is simple spectrum gives simultaneous diagonalization.
For real matrices, this conclusion always holds over ; a real common basis follows when the distinct eigenvalues of are real. Real matrix entries alone do not ensure a real eigenbasis: a quarter-turn of , with , has eigenvalues . The matrices in this question have real spectra, so their common basis is real.
Direct multiplication gives
Take and in the preceding result: has the three distinct eigenvalues and the matrices commute. The common eigenbasis from part (i) gives
Indeed and , and . These equalities directly verify the simultaneous diagonalization.

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