Computing linear sections of varieties: quantum entanglement, tensor decompositions and beyond
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Abstract (arXiv)
We study the problem of finding elements in the intersection of an arbitrary conic variety in mathbbFn with a given linear subspace (where mathbbF can be the real or complex field). This problem captures a rich family of algorithmic problems under different choices of the variety. The special case of the variety consisting of rank-1 matrices already has strong connections to central problems in different areas like quantum information theory and tensor decompositions. This problem is known to be NP-hard in the worst case, even for the variety of rank-1 matrices. Surprisingly, despite these hardness results we develop an algorithm that solves this problem efficiently for "typical" subspaces. Here, the subspace U subseteq mathbbFn is chosen generically of a certain dimension, potentially with some generic elements of the variety contained in it. Our main result is a guarantee that our algorithm recovers all the elements of U that lie in the variety, under some mild non-degeneracy assumptions on the variety. As corollaries, we obtain the following new results: bullet Polynomial time algorithms for several entangled subspaces problems in quantum entanglement, including determining r-entanglement, complete entanglement, and genuine entanglement of a subspace. While all of these problems are NP-hard in the worst case, our algorithm solves them in polynomial time for generic subspaces of dimension up to a constant multiple of the maximum possible. bullet Uniqueness results and polynomial time algorithmic guarantees for generic instances of a broad class of low-rank decomposition problems that go beyond tensor decompositions. Here, we recover a decomposition of the form sumi=1R vi otimes wi, where the vi are elements of the variety X. This implies new uniqueness results and genericity guarantees even in the special case of tensor decompositions.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 11 of its 80 references that are in the map. First posted on arXiv 7 Dec 2022.
Builds on
11 of 80 references.
References · 11
- 2016Tensor Decomposition for Signal Processing and Machine Learningcited by 224
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- 2013Fourier PCA and Robust Tensor Decompositioncited by 8
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- 2016On cap sets and the group-theoretic approach to matrix multiplicationcited by 2
- 2020Overcomplete order-3 tensor decomposition, blind deconvolution and Gaussian mixture modelscited by 2
- 2019Pseudospectral Shattering, the Sign Function, and Diagonalization in Nearly Matrix Multiplication Timecited by 1
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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.
- 2024Overcomplete Tensor Decomposition via Koszul-Young Flatteningscited by 0
- 2026Tensor-based second-order causal discoverycited by 0
- 2026Tensor-based second-order causal discovery16 Jun 2026
- 2024Overcomplete Tensor Decomposition via Koszul-Young Flattenings21 Nov 2024