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Random Sampling a linearly constrained region in n-dimensions...
nonlinear, discontinous, cost function $f(x_1,x_2,..,x_N)$ along with linear constraints $x_n \ge 0, \forall n$
$x_n \le c_n$
and $\sum_{n=1}^N x_n = 1$ find an optimal (local) solution by randomly sampling … Approximating the polytope with a n-dimensional cube and sampling from the cube, only about 1% of the samples fall within the feasible region...which is inefficient if I'm trying to generate many samples …