Tensor-train decomposition
IV Oseledets - SIAM Journal on Scientific Computing, 2011 - SIAM
SIAM Journal on Scientific Computing, 2011•SIAM
A simple nonrecursive form of the tensor decomposition in d dimensions is presented. It
does not inherently suffer from the curse of dimensionality, it has asymptotically the same
number of parameters as the canonical decomposition, but it is stable and its computation is
based on low-rank approximation of auxiliary unfolding matrices. The new form gives a clear
and convenient way to implement all basic operations efficiently. A fast rounding procedure
is presented, as well as basic linear algebra operations. Examples showing the benefits of …
does not inherently suffer from the curse of dimensionality, it has asymptotically the same
number of parameters as the canonical decomposition, but it is stable and its computation is
based on low-rank approximation of auxiliary unfolding matrices. The new form gives a clear
and convenient way to implement all basic operations efficiently. A fast rounding procedure
is presented, as well as basic linear algebra operations. Examples showing the benefits of …
A simple nonrecursive form of the tensor decomposition in d dimensions is presented. It does not inherently suffer from the curse of dimensionality, it has asymptotically the same number of parameters as the canonical decomposition, but it is stable and its computation is based on low-rank approximation of auxiliary unfolding matrices. The new form gives a clear and convenient way to implement all basic operations efficiently. A fast rounding procedure is presented, as well as basic linear algebra operations. Examples showing the benefits of the decomposition are given, and the efficiency is demonstrated by the computation of the smallest eigenvalue of a 19-dimensional operator.
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