Eigenvectors of symmetric matrices

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The discussion centers on proving that the eigenvectors of symmetric matrices are orthogonal when their eigenvalues are distinct. It demonstrates that for eigenvectors v and u corresponding to different eigenvalues λ1 and λ2, the inner product <v, u> must equal zero if λ1 is not equal to λ2. This conclusion arises from the properties of symmetric matrices, which are self-adjoint, leading to the result that (λ1 - λ2)<v, u> = 0. However, if an eigenvalue is repeated, the associated eigenvector space may not be one-dimensional, meaning the eigenvectors are not guaranteed to be orthogonal. Thus, the orthogonality of eigenvectors is contingent on having distinct eigenvalues.
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Can anyone prove that the eigenvectors of symmetric matrices are orthogonal?
 
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Let v be a eigenvector with eigenvalue \lambda_1 and u an eigenvector with eigenvalue [math]\lambda_2[/math], both with length 1.

\lambda_1&lt;v, u&gt;= &lt;\lambda_1v, u&gt;
(<u, v> is the innerproduct)
= &lt; Av, u&gt;= \overline{&lt;v, Au&gt;}
(because A is symmetric)
("self adjoint" in general)
= \overline{&lt;v, \lambda_2u&gt;}= \lambda&lt;v, u&gt;
so that
\lambda_1&lt;v, u&gt;= \lambda_2&lt;v, u&gt;
(\lambda_1- \lambda_2)&lt;v, u&gt;= 0
Since \lambda_1 and \lambda_2 are not equal,
\lambda_1- \lambda_2 is not 0, <v, u> is.
 
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The important criterion is not symmetry, but whether the eigenvalues are all different (i.e. all the roots of the characteristic equation are different). If an eigenvalue is repeated, then the space associated with the eigenvector is not one dimensional, so that the vectors are not necessarily orthogonal.
 
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