  We consider the problem of maximizing $\langle c,x \rangle$ subject to the
constraints $Ax \leq \mathbf{1}$, where $x\in{\mathbb R}^n$, $A$ is an $m\times
n$ matrix with mutually independent centered subgaussian entries of unit
variance, and $c$ is a cost vector of unit Euclidean length. In the asymptotic
regime $n\to\infty$, $\frac{m}{n}\to\infty$, and under some additional
assumptions on $c$, we prove that the optimal objective value $z^*$ of the
linear program satisfies $$ \lim\limits_{n\to\infty}\sqrt{2\log(m/n)}\,z^*=
1\quad \mbox{almost surely}. $$ In the context of high-dimensional convex
geometry, our findings imply sharp asymptotic bounds on the spherical mean
width of the random convex polyhedron $P=\{x\in{\mathbb R}^n:\; Ax\leq
\mathbf{1}\}$. We provide numerical experiments as supporting data for the
theoretical predictions. Further, we carry out numerical studies of the
limiting distribution and the standard deviation of $z^*$.
