  We present a new approach to compute low lying eigenvalues and corresponding
eigenvectors for strongly correlated many-body systems. The method was inspired
by the so-called Automated Multilevel Sub-structuring Method (AMLS).
Originally, it relies on subdividing the physical space into several regions.
In these sub-systems the eigenproblem is solved, and the regions are combined
in an adequate way. We developed a method to partition the state space of a
many-particle system in order to apply similar operations on the partitions.
The tensorial structure of the Hamiltonian of many-body systems make them even
more suitable for this approach. The method allows to break down the complexity
of large many-body systems to the complexity of two spatial sub-systems having
half the geometric size. Considering the exponential size of the Hilbert space
with respect to the geometric size this represents a huge advantage. In this
work, we present some benchmark computations for the method applied to the
one-band Hubbard model.
