  Vibrational spectra and wavefunctions of polyatomic molecules can be
calculated at low memory cost using low-rank sum-of-product (SOP)
decompositions to represent basis functions generated using an iterative
eigensolver. Using a SOP tensor format does not determine the iterative
eigensolver. The choice of the interative eigensolver is limited by the need to
restrict the rank of the SOP basis functions at every stage of the calculation.
We have adapted, implemented and compared different reduced-rank algorithms
based on standard iterative methods (block-Davidson algorithm, Chebyshev
iteration) to calculate vibrational energy levels and wavefunctions of the
12-dimensional acetonitrile molecule. The effect of using low-rank SOP basis
functions on the different methods is analyzed and the numerical results are
compared with those obtained with the reduced rank block power method
introduced in J. Chem. Phys. 140, 174111 (2014). Relative merits of the
different algorithms are presented, showing that the advantage of using a more
sophisticated method, although mitigated by the use of reduced-rank
sum-of-product functions, is noticeable in terms of CPU time.
