  The Separability Problem is approached from the perspective of Ellipsoidal
Classification. A Density Operator of dimension N can be represented as a
vector in a real vector space of dimension $N^{2}- 1$, whose components are the
projections of the matrix onto some selected basis. We suggest a method to test
separability, based on successive optimization programs. First, we find the
Minimum Volume Covering Ellipsoid that encloses a particular set of properly
vectorized bipartite separable states, and then we compute the Euclidean
distance of an arbitrary vectorized bipartite Density Operator to this
ellipsoid. If the vectorized Density Operator falls inside the ellipsoid, it is
regarded as separable, otherwise it will be taken as entangled. Our method is
scalable and can be implemented straightforwardly in any desired dimension.
Moreover, we show that it allows for detection of Bound Entangled States
