  We presented a separation based optimization algorithm which, rather than
optimization the entire variables altogether, This would allow us to employ: 1)
a class of nonlinear functions with three variables and 2) a convex quadratic
multivariable polynomial, for minimization of reprojection error. Neglecting
the inversion required to minimize the nonlinear functions, in this paper we
demonstrate how separation allows eradication of matrix inversion.
