An Adaptive Algebraic Multigrid Algorithm for Low-Rank Canonical Tensor Decomposition

By Hans De Sterck, Killian Miller. 2011

23 citations recorded by the source corpus.

A new algorithm based on algebraic multigrid is presented for computing the rank-$R$ canonical decomposition of a tensor for small $R$. Standard alternating least squares (ALS) is used as the relaxation method. Transfer operators and coarse-level tensors are constructed in an adaptive setup phase that combines multiplicative correction and bootstrap algebraic multigrid. An accurate solution is computed by an additive solve phase based on the full approximation scheme. Numerical tests show that for certain test problems our multilevel method significantly outperforms standalone ALS when a high level of accuracy is required.

Subfields: Computational Mathematics, Computational Theory and Mathematics, Statistical and Nonlinear Physics

Citations in the corpus

• A literature survey of low‐rank tensor approximation techniques (2013, neutral): “…and robustness of gradient-based and Newton-like methods [2,72,74,140,223,224], modifying and improving ALS (alternating least squares) [41, 57, 58, 90, 225], studying the convergence of ALS [51, 194, 253] and reducing the cost of the unfolding operations required during the approximation…”