how to set Equalizer's coefficient using generalized eigenvector. In that paper https://jwcn-eurasipjournals.springeropen.com/articles/10.1186/1687-1499-2012-72 .. Equation 3,  

$J(w) = w^HBw/w^HCw$ .. 

$B$ and $C$ are matrices, $w$ the filter coefficients vector we need to design. 

The author is supposed to maximize that cost function $J(w)$. So, he used the generalized eigenvalue decomposition of the two equations $(B,C)$ then he set the eigenvector correspondent to maximum eigenvalue as equalizer filter $w$.  

My question, Is my understanding for that idea correct? I mean we can use generalized eigenvalue decomposition for that purpose maximize/minimize, which means if set  setting the eigenvector correspondent to maximum eigenvalue as equalizer filter $w$ will maximize, and setting the eigenvector correspondent to minimum eigenvalue as equalizer filter $w$ will minimize. 

thank you.
The generalized eigenvalue problem is given by

$$Bw=\lambda Cw\tag{1}$$

where $\lambda$ is the generalized eigenvalue of the matrices $B$ and $C$. Multiplying $(1)$ from the left with $w^H$ (with $^H$ denoting the Hermitian conjugate) and dividing both sides by $w^HCw$ (assuming that this term is non-zero), we obtain

$$\frac{w^HBw}{w^HCw}=J(w)=\lambda\tag{2}$$

This shows that the value of the objective function $J(w)$ (which is a generalized Rayleigh quotient) equals the generalized eigenvalue $\lambda$. Consequently, $J(w)$ is maximized by the eigenvector corresponding to the maximum eigenvalue, and it is minimized by the eigenvector corresponding to the minimum eigenvalue.