  We treat the problem of the Frobenius distance evaluation from a given matrix
$ A \in \mathbb R^{n\times n} $ with distinct eigenvalues to the manifold of
matrices with multiple eigenvalues. On restricting considerations to the rank $
1 $ real perturbation matrices, we prove that the distance in question equals $
\sqrt{z_{\ast}} $ where $ z_{\ast} $ is a positive (generically, the least
positive) zero of the algebraic equation $$ \mathcal F(z) = 0, \ \mbox{where} \
\mathcal F(z):= \mathcal D_{\lambda} \left( \det \left[ (\lambda I - A)(\lambda
I - A^{\top})-z I_n \right] \right)/z^n $$ and $ \mathcal D_{\lambda} $ stands
for the discriminant of the polynomial treated with respect to $\lambda $. In
the framework of this approach we also provide the procedure for finding the
nearest to $ A $ matrix with multiple eigenvalue. Generalization of the problem
to the case of complex perturbations is also discussed. Several examples are
presented clarifying the computational aspects of the approach.
