
A TRUSTREGIONBASED ALTERNATING LEASTSQUARES ALGORITHM FOR TENSOR DECOMPOSITIONS
Fan Jiang, Deren Han, Xiaofei Zhang
Journal of Computational Mathematics
2018, 36 (3):
351373.
DOI: 10.4208/jcm.1605m20160828
Tensor canonical decomposition (shorted as CANDECOMP/PARAFAC or CP) decomposes a tensor as a sum of rankone tensors, which finds numerous applications in signal processing, hypergraph analysis, data analysis, etc. Alternating leastsquares (ALS) is one of the most popular numerical algorithms for solving it. While there have been lots of efforts for enhancing its efficiency, in general its convergence can not been guaranteed.
In this paper, we cooperate the ALS and the trustregion technique from optimization field to generate a trustregionbased alternating leastsquares (TRALS) method for CP. Under mild assumptions, we prove that the whole iterative sequence generated by TRALS converges to a stationary point of CP. This thus provides a reasonable way to alleviate the swamps, the notorious phenomena of ALS that slow down the speed of the algorithm. Moreover, the trust region itself, in contrast to the regularization alternating leastsquares (RALS) method, provides a selfadaptive way in choosing the parameter, which is essential for the efficiency of the algorithm. Our theoretical result is thus stronger than that of RALS in[26], which only proved the cluster point of the iterative sequence generated by RALS is a stationary point. In order to accelerate the new algorithm, we adopt an extrapolation scheme. We apply our algorithm to the amino acid fluorescence data decomposition from chemometrics, BCM decomposition and rank(
L
_{r},
L
_{r}, 1) decomposition arising from signal processing, and compare it with ALS and RALS. The numerical results show that TRALS is superior to ALS and RALS, both from the number of iterations and CPU time perspectives.
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