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Tensor Rank and the Ill-Posedness of the Best Low-Rank Approximation Problem

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de Silva, Vin, Lim, Lek-Heng·2008·SIAM Journal on Matrix Analysis and Applications

Abstract

There has been continued interest in seeking a theorem describing optimal low-rank approximations to tensors of order 3 or higher that parallels the Eckart–Young theorem for matrices. In this paper, we argue that the naive approach to this problem is doomed to failure because, unlike matrices, tensors of order 3 or higher can fail to have best rank-r approximations. The phenomenon is much more widespread than one might suspect: examples of this failure can be constructed over a wide range of dimensions, orders, and ranks, regardless of the choice of norm (or even Brègman divergence). Moreover, we show that in many instances these counterexamples have positive volume: they cannot be regarded as isolated phenomena. In one extreme case, we exhibit a tensor space in which no rank-3 tensor has an optimal rank-2 approximation. The notable exceptions to this misbehavior are rank-1 tensors and order-2 tensors (i.e., matrices). In a more positive spirit, we propose a natural way of overcoming the ill-posedness of the low-rank approximation problem, by using weak solutions when true solutions do not exist. For this to work, it is necessary to characterize the set of weak solutions, and we do this in the case of rank 2, order 3 (in arbitrary dimensions). In our work we emphasize the importance of closely studying concrete low-dimensional examples as a first step toward more general results. To this end, we present a detailed analysis of equivalence classes of $2 \times 2 \times 2$ tensors, and we develop methods for extending results upward to higher orders and dimensions. Finally, we link our work to existing studies of tensors from an algebraic geometric point of view. The rank of a tensor can in theory be given a semialgebraic description; in other words, it can be determined by a system of polynomial inequalities. We study some of these polynomials in cases of interest to us; in particular, we make extensive use of the hyperdeterminant $\Delta$ on $\mathbb{R}^{2\times 2 \times 2}$.

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Metadata

Authors
de Silva, V., & Lim, L.-H.
Venue
SIAM Journal on Matrix Analysis and Applications
Published
2008-01
Volume
30
Issue
3
Pages
1084-1127
ISSN
  • 0895-4798 (ISSN-L)
  • 1095-7162
OpenAlex
W2132267493
Cited by
927
References
1
FWCI
16.28
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Tensor Rank and the Ill-Posedness of the Best Low-Rank Approximation Problem. https://app.cochat.ai/papers/10.1137/06066518x
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de Silva, V., & Lim, L.-H. (2008). Tensor Rank and the Ill-Posedness of the Best Low-Rank Approximation Problem. SIAM Journal on Matrix Analysis and Applications. https://doi.org/10.1137/06066518x

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