I am working on a Quadratically Constrained Quadratic Program (QCQP) of the form:$$ \min_{x} \quad \frac{1}{2} x^T P x + q^T x + r$$ 
$$ subject\ to \qquad x^{T}x \leq 1   $$

where $P \in S^{++}_{n}$ is a symmetric positive definite matrix, and $q \in \mathbb{R}^{n}$ is a given vector. I aim to show that the optimal solution $x^{*}$ satisfies:$$ x^{*}=-(P+ \lambda I)^{-1}q$$
where $ \lambda$ =$max\{ 0,\bar{\lambda}\}$ and $\bar{\lambda}$ is the largest solution to the nonlinear equation:$$q^{T}(P+\lambda I)^{-2}q=1 $$
I tried solving this by using the necessary and sufficient conditions for the optimal solution:$$ x \ is \ optimal \ if \ and \ only \ if \ x \in X \ and
\\
\nabla f_0(x)^T (y - x) \geq 0 \quad \text{for all} \quad y \in X.
$$
But I wasn't able to make progress.Is there an alternative approach to solve this problem, or any hints on how to use the optimality conditions more effectively? Any suggestions would be greatly appreciated!