maximize the eigenvalue of a symmetric matrix

**A Coarse Idea**

We meet with an integer optimizing problem, which is defined as follows:

$\displaystyle y^{*}= \textrm{argmax}_{\begin{subarray}{c}{y \in \mathcal{Y}}\end{subarray}}\quad \frac{y^TX^T\left(\sum_{k=1}^{N}{b_kw_k^{\phantom{ k}T}}\right)Xy}{\sqrt{y^TX^TXy}}$

Where $\displaystyle \mathcal{Y}$ denotes $\displaystyle \displaystyle n(n-1)/2$ dimensional column parameter vector, each entry taking value from $\displaystyle \left\{{-1,1}\right\}$. $\displaystyle y^{*}$ represents the optimal solution. $\displaystyle b_i, w_i$ denote $\displaystyle m$ dimensional column vector, whose values are known. The matrix $\displaystyle X \in \mathcal{R}^{m \times n(n-1)/2}$ is defined on $\displaystyle m$ dimensional column vectors $\displaystyle x_{1},x_{2} \ldots x_{n}$, where each column vector is definitively. The columns of $\displaystyle X$ are calculated by substraction of every two column vector $\displaystyle x_{i}, x_{j} \textrm{ when } i<j$. It is listed as follows:

$\displaystyle X =\left[\begin{array}{cccccccc}

|&\ldots&|&|&\dots&|&\ldots&|\\ x_{1}-x_{2}&\ldots&x_{1}-x_{n}&x_{2}-x_{3}&\dots&x_{2}-x_{n}&\ldots&x_{n-1}-x_{n}\\ |&\ldots&|&|&\dots&|&\ldots&|\\ \end{array}\right]$

**The Goal**

Our goal is to build an algorithm, which can solve the optimization problem in polynomial order time with $\displaystyle m$, $\displaystyle n$ and $\displaystyle N$. What's happiest, you can yield an analytical solution to the optimization problem.