A tensor decomposition algorithm for large ODEs with conservation laws

Bibliographic Details
Title: A tensor decomposition algorithm for large ODEs with conservation laws
Authors: Dolgov, Sergey V.
Publication Year: 2014
Collection: Mathematics
Condensed Matter
Physics (Other)
Subject Terms: Mathematics - Numerical Analysis, Condensed Matter - Strongly Correlated Electrons, Physics - Computational Physics, 15A69, 33F05, 65F10, 65L05, 65M70, 34C14
More Details: We propose an algorithm for solution of high-dimensional evolutionary equations (ODEs and discretized time-dependent PDEs) in the Tensor Train (TT) decomposition, assuming that the solution and the right-hand side of the ODE admit such a decomposition with a low storage. A linear ODE, discretized via one-step or Chebyshev differentiation schemes, turns into a large linear system. The tensor decomposition allows to solve this system for several time points simultaneously using an extension of the Alternating Least Squares algorithm. This method computes the TT approximation of the solution directly, without ever solving the original large problem, and encapsulates the Galerkin model reduction of the ODE. This allows an efficient estimation of the time discretization error, and hence provides a way to adapt the time steps. Besides, conservation laws can be preserved exactly in the reduced model by expanding the approximation subspace with the generating vectors of the linear invariants and correction of the euclidean norm. In numerical experiments with the transport and the chemical master equations, we demonstrate that the new method is faster than traditional time stepping and stochastic simulation algorithms, whereas the invariants are preserved up to the machine precision irrespectively of the TT approximation accuracy.
Document Type: Working Paper
Access URL: http://arxiv.org/abs/1403.8085
Accession Number: edsarx.1403.8085
Database: arXiv
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  Data: A tensor decomposition algorithm for large ODEs with conservation laws
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  Data: <searchLink fieldCode="AR" term="%22Dolgov%2C+Sergey+V%2E%22">Dolgov, Sergey V.</searchLink>
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  Data: 2014
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  Data: Mathematics<br />Condensed Matter<br />Physics (Other)
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– Name: Abstract
  Label: Description
  Group: Ab
  Data: We propose an algorithm for solution of high-dimensional evolutionary equations (ODEs and discretized time-dependent PDEs) in the Tensor Train (TT) decomposition, assuming that the solution and the right-hand side of the ODE admit such a decomposition with a low storage. A linear ODE, discretized via one-step or Chebyshev differentiation schemes, turns into a large linear system. The tensor decomposition allows to solve this system for several time points simultaneously using an extension of the Alternating Least Squares algorithm. This method computes the TT approximation of the solution directly, without ever solving the original large problem, and encapsulates the Galerkin model reduction of the ODE. This allows an efficient estimation of the time discretization error, and hence provides a way to adapt the time steps. Besides, conservation laws can be preserved exactly in the reduced model by expanding the approximation subspace with the generating vectors of the linear invariants and correction of the euclidean norm. In numerical experiments with the transport and the chemical master equations, we demonstrate that the new method is faster than traditional time stepping and stochastic simulation algorithms, whereas the invariants are preserved up to the machine precision irrespectively of the TT approximation accuracy.
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RecordInfo BibRecord:
  BibEntity:
    Subjects:
      – SubjectFull: Mathematics - Numerical Analysis
        Type: general
      – SubjectFull: Condensed Matter - Strongly Correlated Electrons
        Type: general
      – SubjectFull: Physics - Computational Physics
        Type: general
      – SubjectFull: 15A69, 33F05, 65F10, 65L05, 65M70, 34C14
        Type: general
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      – TitleFull: A tensor decomposition algorithm for large ODEs with conservation laws
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            NameFull: Dolgov, Sergey V.
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          Dates:
            – D: 31
              M: 03
              Type: published
              Y: 2014
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