  The phase space flow of a dynamical system leading to the solution of Linear
Programming (LP) problems is explored as an example of complexity analysis in
an analog computation framework. An ensemble of LP problems with $n$ variables
and $m$ constraints ($n>m$), where all elements of the vectors and matrices are
normally distributed is studied. The convergence time of a flow to the fixed
point representing the optimal solution is computed. The cumulative
distribution ${\cal F}^{(n,m)}(\Delta)$ of the convergence rate $\Delta_{min}$
to this point is calculated analytically, in the asymptotic limit of large
$(n,m)$, in the framework of Random Matrix Theory. In this limit ${\cal
F}^{(n,m)}(\Delta)$ is found to be a scaling function, namely it is a function
of one variable that is a combination of $n$, $m$ and $\Delta$ rather then a
function of these three variables separately. From numerical simulations also
the distribution of the computation times is calculated and found to be a
scaling function as well.
