You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
We propose a new algorithm for calculation of vibrational spectra of molecules using tensor train decomposition. Under the assumption that eigenfunctions lie on a low-parametric manifold of low-rank tensors we suggest using well-known iterative methods that utilize matrix inversion (locally optimal block preconditioned conjugate gradient method, inverse iteration) and solve corresponding linear systems inexactly along this manifold. As an application, we accurately compute vibrational spectra (84 states) of acetonitrile molecule CH3CN on a laptop in one hour using only 100 MB of memory to represent all computed eigenfunctions.

Cited Abstract(s):
[1]: A multilayer (ML) formulation of the multiconfiguration time-dependent Hartree (MCTDH) theory is presented. In this new approach, the single-particle (SP) functions in the original MCTDH method are further expressed employing a time-dependent multiconfigurational expansion. The Dirac–Frenkel variational principle is then applied to optimally determine the equations of motion. Following this strategy, the SP groups are built in several layers, where each top layer SP can contain many more Cartesian degrees of freedom than in the previous formulation of the MCTDH method. As a result, the ML-MCTDH method has the capability of treating substantially more physical degrees of freedom than the original MCTDH method, and thus significantly enhances the ability of carrying out quantum dynamical simulations for complex molecular systems. The efficiency of the new formulation is demonstrated by converged quantum dynamical simulations for systems with a few hundred to a thousand degrees of freedom.

[2]: We define the hierarchical singular value decomposition (SVD) for tensors of order @math . This hierarchical SVD has properties like the matrix SVD (and collapses to the SVD in @math ), and we prove these. In particular, one can find low rank (almost) best approximations in a hierarchical format ( @math -Tucker) which requires only @math parameters, where @math is the order of the tensor, @math the size of the modes, and @math the (hierarchical) rank. The @math -Tucker format is a specialization of the Tucker format and it contains as a special case all (canonical) rank @math tensors. Based on this new concept of a hierarchical SVD we present algorithms for hierarchical tensor calculations allowing for a rigorous error analysis. The complexity of the truncation (finding lower rank approximations to hierarchical rank @math tensors) is in @math and the attainable accuracy is just 2-3 digits less than machine precision.

[3]:
The Multi Configuration Time Dependent Hartree (MCTDH) approach [3] also uses tensor format, namely the Tucker decomposition. This approach reduces complexity, but suffers from the curse of dimensionality. This problem was solved in the multilayer MCTDH [1] which is similar to the Hierarchical Tucker representation [2] .