Bart Vandereycken

Time integration of tensor trains

by , ,

Abstract:

A robust and efficient time integrator for dynamical tensor approximation in the tensor train or matrix product state format is presented. The method is based on splitting the projector onto the tangent space of the tensor manifold. The algorithm can be used for updating time-dependent tensors in the given data-sparse tensor train/matrix product state format and for computing an approximate solution to high-dimensional tensor differential equations within this data-sparse format. The formulation, implementation, and theoretical properties of the proposed integrator are studied, and numerical experiments with problems from quantum molecular dynamics and with iterative processes in the tensor train format are included.

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Reference:

Ch. Lubich, I. Oseledets, B. Vandereycken, "Time integration of tensor trains", In SIAM J. Numer. Anal., vol. 53, no. 2, pp. 917-941, 2015.

Bibtex Entry:

@article{Lubich_OV_2015,
    Abstract = {A robust and efficient time integrator for dynamical tensor approximation in the tensor train or matrix product state format is presented. The method is based on splitting the projector onto the tangent space of the tensor manifold. The algorithm can be used for updating time-dependent tensors in the given data-sparse tensor train/matrix product state format and for computing an approximate solution to high-dimensional tensor differential equations within this data-sparse format. The formulation, implementation, and theoretical properties of the proposed integrator are studied, and numerical experiments with problems from quantum molecular dynamics and with iterative processes in the tensor train format are included.},
    Author = {Lubich, Ch. and Oseledets, I. and Vandereycken, B.},
    Doi = {10.1137/140976546},
    Journal = {SIAM J. Numer. Anal.},
    Number = {2},
    Pages = {917--941},
    Pdf = {http://www.unige.ch/math/vandereycken/papers/published_Lubich_OV_2015.pdf},
    Title = {Time integration of tensor trains},
    Volume = {53},
    Year = {2015}}