Eigenvalues of a vector I'm trying to calculate (with Matlab) the minimal and maximal Eigenvalues $\lambda_{min}$ and $\lambda_{max}$ of the autocorrelationmatrix $R$ to adjust the stepsize of an adaptive filter.

My input signal $x$ is a vector of colored noise.
And my plan is to do something like


lmin = min(eig(xcorr(x)));
lmax = max(eig(xcorr(x)));


But it fails becaus 'For standard eigenproblem EIG(A), A must be square.'

I think this is a little bit strange, because in my opinion the output of the autocorrelation is always just a vector.

Please help me to find the point, whether I need to use something else than Matlabs 'eig' function or if it's mathematically not possible to calculate the eigenvalues of a single vector.
The autocorrelation matrix is a Toeplitz symmetric matrix that can be formed as:

Rxx = toeplitz(xcorr(x));


and that should give you a sensible (square) matrix that can be passed to eig.