I am considering the following optimization problem. Let $P$ be a set of $n$ points in $\mathbb{R}^d$
maximize $\sum_{p\in P}\vert\langle \Vert p\Vert p, \hat{x}\rangle\vert$ subject to $\Vert\hat{x}\Vert^2=1, \hat{x}\in \mathbb{R}^d$.
Without the absolute value inside the summand, the solution is easy, the optimal solution is achived by the unit vector along the vector $\sum_{p\in P}\Vert p\Vert p$. But with the absolute value in the picture, I failed to find a solution. Can this problem be solved by techniques of optimization literature?
Thanks
p.s.
This problem has some superficial similarities with Chebyshev approximation problem:
$minimize \max_i \vert a_i^Tx-b_i\vert$. The aforementioned problem involves $l_\infty$ norm, whereas the original problem deals with $l_1$ norm.