Calculating vibrational spectra with sum of product basis functions   without storing full-dimensional vectors or matrices
  We propose an iterative method for computing vibrational spectra that significantly reduces the memory cost of calculations. It uses a direct product primitive basis, but does not require storing vectors with as many components as there are product basis functions. Wavefunctions are represented in a basis each of whose functions is a sum of products (SOP) and the factorizable structure of the Hamiltonian is exploited. If the factors of the SOP basis functions are properly chosen, wavefunctions are linear combinations of a small number of SOP basis functions. The SOP basis functions are generated using a shifted block power method. The factors are refined with a rank reduction algorithm to cap the number of terms in a SOP basis function. The ideas are tested on a 20-D model Hamiltonian and a realistic CH$_3$CN (12 dimensional) potential. For the 20-D problem, to use a standard direct product iterative approach one would need to store vectors with about $10^{20}$ components and would hence require about $8 \times 10^{11}$ GB. With the approach of this paper only 1 GB of memory is necessary. Results for CH$_3$CN agree well with those of a previous calculation on the same potential. 
Arnaud Leclerc and Tucker Carrington
physics.comp-ph quant-ph