Rank-r decomposition of symmetric tensors

Jie WEN, Qin NI, Wenhuan ZHU

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PDF(186 KB)
Front. Math. China ›› 2017, Vol. 12 ›› Issue (6) : 1339-1355. DOI: 10.1007/s11464-017-0632-5
RESEARCH ARTICLE
RESEARCH ARTICLE

Rank-r decomposition of symmetric tensors

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Abstract

An algorithm is presented for decomposing a symmetric tensor into a sum of rank-1 symmetric tensors. For a given tensor, by using apolarity, catalecticant matrices and the condition that the mapping matrices are commutative, the rank of the tensor can be obtained by iteration. Then we can find the generating polynomials under a selected basis set. The decomposition can be constructed by the solutions of generating polynomials under the condition that the solutions are all distinct which can be guaranteed by the commutative property of the matrices. Numerical examples demonstrate the efficiency and accuracy of the proposed method.

Keywords

Symmetric tensor / symmetric rank / decomposition / generating polynomial / catalectieant matrix

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Jie WEN, Qin NI, Wenhuan ZHU. Rank-r decomposition of symmetric tensors. Front. Math. China, 2017, 12(6): 1339‒1355 https://doi.org/10.1007/s11464-017-0632-5

References

[1]
AlexanderJ, HirschowitzA. Polynomial interpolation in several variables.J Algebraic Geom, 1995, 4: 201–222
[2]
BallicoE. On the typical rank of real bivariate polynomials.Linear Algebra Appl,2014, 452(1): 263–269
CrossRef Google scholar
[3]
BallicoE, BernardiA. Decomposition of homogeneous polynomials with low rank.Math Z, 2012, 271(3): 1141–1149
CrossRef Google scholar
[4]
BatselierK, WongN. Symmetric tensor decomposition by an iterative eigendecomposition algorithm.J Comput Appl Math, 2016, 308(15): 69–82
CrossRef Google scholar
[5]
BernardiA, GimiglianoA, Id`aM. Computing symmetric rank for symmetric tensors.J Symbolic Comput, 2011, 46(1): 34–53
CrossRef Google scholar
[6]
BrachatJ, ComonP, MourrainB, Tsigaridas E. Symmetric tensor decomposition.Linear Algebra Appl,2010, 433(11-12): 1851–1872
CrossRef Google scholar
[7]
BuczynskaW, BuczynskiJ. Secant varieties to high degree Veronese reembeddings, catalecticant matrices and smoothable Gorenstein schemes.J Algebraic Geom, 2014, 23(1): 63–90
CrossRef Google scholar
[8]
ComonP. Tensors: a brief introduction.IEEE Signal Processing Magazine, 2014, 31(3): 44–53
CrossRef Google scholar
[9]
ComonP, GolubG, LimL H, MourrainB. Symmetric tensors and symmetric tensor rank.SIAM J Matrix Anal Appl,2008, 30(3): 1254–1279
CrossRef Google scholar
[10]
FriedlandS. Remarks on the symmetric rank of symmetric tensors.SIAM J Matrix Anal Appl, 2016: 37(1): 320–337
CrossRef Google scholar
[11]
HillarC, LimL H. Most tensor problems are NP-hard.J ACM,2013, 60(6): 45
CrossRef Google scholar
[12]
JiangB, LiZ N. ZhangS Z. Characterizing real-valued multivariate complex polynomials and their symmetric tensor representations.SIAM J Matrix Anal Appl,2016, 37(1): 381–408
CrossRef Google scholar
[13]
LandsbergJ M. Tensors: Geometry and Applications.Grad Stud Math, Vol 128. Providence: Amer Math Soc, 2012
[14]
NieJ. Generating polynomials and symmetric tensor decompositions.Found Comput Math,
CrossRef Google scholar
[15]
OedingL, OttaavianiG. Eigenvectors of tensors and algorithms for Waring decomposition.J Symbolic Comput,2013, 54: 9–35
CrossRef Google scholar
[16]
RobevaE. Orthogonal decomposition of symmetric tensor.SIAM J Matrix Anal Appl,2016, 37(1): 86–102
CrossRef Google scholar
[17]
SturmfelsB. Solving Systems of Polynomial Equations.CBMS Regional Conference Series in Mathematics, No 97. Providence: Amer Math Soc, 2002
CrossRef Google scholar

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