Given a symmetric positive semidefinite matrix $\bf \Sigma$ and a real-valued vector $\bf r$,
$$ \begin{array}{ll} \underset {{\bf w}} {\text{minimize}} & {\bf w}^\top {\bf \Sigma} \, {\bf w} \\ \text{subject to} & {\bf 1}^\top {\bf w} = 1 \\ & {\bf r}^\top {\bf w} \geq r_0 \\ & {\bf w} \geq {\bf 0} \end{array} $$
I know how to solve equality-constrained quadratic programs, but I am not sure how to solve inequality-constrained quadratic programs. Should I introduce a slack variable?