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Mathematics of Computation

Published by the American Mathematical Society since 1960 (published as Mathematical Tables and other Aids to Computation 1943-1959), Mathematics of Computation is devoted to research articles of the highest quality in computational mathematics.

ISSN 1088-6842 (online) ISSN 0025-5718 (print)

The 2020 MCQ for Mathematics of Computation is 1.78.

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Nuclear norm of higher-order tensors
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by Shmuel Friedland and Lek-Heng Lim PDF
Math. Comp. 87 (2018), 1255-1281 Request permission

Abstract:

We establish several mathematical and computational properties of the nuclear norm for higher-order tensors. We show that like tensor rank, tensor nuclear norm is dependent on the choice of base field; the value of the nuclear norm of a real $3$-tensor depends on whether we regard it as a real $3$-tensor or a complex $3$-tensor with real entries. We show that every tensor has a nuclear norm attaining decomposition and every symmetric tensor has a symmetric nuclear norm attaining decomposition. There is a corresponding notion of nuclear rank that, unlike tensor rank, is lower semicontinuous. We establish an analogue of Banach’s theorem for tensor spectral norm and Comon’s conjecture for tensor rank; for a symmetric tensor, its symmetric nuclear norm always equals its nuclear norm. We show that computing tensor nuclear norm is NP-hard in several ways. Deciding weak membership in the nuclear norm unit ball of $3$-tensors is NP-hard, as is finding an $\varepsilon$-approximation of nuclear norm for $3$-tensors. In addition, the problem of computing spectral or nuclear norm of a $4$-tensor is NP-hard, even if we restrict the $4$-tensor to be bi-Hermitian, bisymmetric, positive semidefinite, nonnegative valued, or all of the above. We discuss some simple polynomial-time approximation bounds. As an aside, we show that computing the nuclear $(p,q)$-norm of a matrix is NP-hard in general but polynomial-time if $p=1$, $q = 1$, or $p=q=2$, with closed-form expressions for the nuclear $(1,q)$- and $(p,1)$-norms.
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Additional Information
  • Shmuel Friedland
  • Affiliation: Department of Mathematics, Statistics and Computer Science, University of Illinois at Chicago, 851 S. Morgan Street, Chicago, Illinois 60607
  • MR Author ID: 69405
  • Email: friedlan@uic.edu
  • Lek-Heng Lim
  • Affiliation: Computational and Applied Mathematics Initiative, Department of Statistics, University of Chicago, 5747 S. Ellis Avenue, Chicago, Illinois 60637
  • MR Author ID: 680138
  • Email: lekheng@galton.uchicago.edu
  • Received by editor(s): May 28, 2016
  • Received by editor(s) in revised form: November 2, 2016
  • Published electronically: September 19, 2017
  • Additional Notes: The first author was partially supported by NSF DMS-1216393
    The second author was partially supported by AFOSR FA9550-13-1-0133, DARPA D15AP00109, NSF IIS 1546413, DMS 1209136, DMS 1057064
  • © Copyright 2017 American Mathematical Society
  • Journal: Math. Comp. 87 (2018), 1255-1281
  • MSC (2010): Primary 15A69, 90C60; Secondary 47B10, 68Q25
  • DOI: https://doi.org/10.1090/mcom/3239
  • MathSciNet review: 3766387