  Consider the problem of finding a point in a unit $n$-dimensional
$\ell_p$-ball ($p\ge 2$) such that the minimum of the weighted Euclidean
distance from given $m$ points is maximized. We show in this paper that the
recent SDP-relaxation-based approximation algorithm [SIAM J. Optim. 23(4),
2264-2294, 2013] will not only provide the first theoretical approximation
bound of $\frac{1-O\left(\sqrt{ \ln(m)/n}\right)}{2}$, but also perform much
better in practice, if the SDP relaxation is removed and the optimal solution
of the SDP relaxation is replaced by a simple scalar matrix.

Optimization and Control