Estimating a Few Extreme Singular Values and Vectors for Large-Scale Matrices in Tensor Train Format

New algorithms for singular value decomposition (SVD) of very large-scale matrices based on a low-rank tensor approximation technique called the tensor train (TT) format are proposed and demonstrate the effectiveness of the proposed methods compared with standard SVD algorithms and TT-based algorithms developed for symmetric eigenvalue decomposition.