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Border rank is not multiplicative under the tensor product

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abstract

It has recently been shown that the tensor rank can be strictly submultiplicative under the tensor product, where the tensor product of two tensors is a tensor whose order is the sum of the orders of the two factors. The necessary upper bounds were obtained with help of border rank. It was left open whether border rank itself can be strictly submultiplicative. We answer this question in the affirmative. In order to do so, we construct lines in projective space along which the border rank drops multiple times and use this result in conjunction with a previous construction for a tensor rank drop. Our results also imply strict submultiplicativity for cactus rank and border cactus rank.

fields

cs.CC 1

years

2024 1

verdicts

CONDITIONAL 1

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Asymptotic tensor rank is characterized by polynomials

cs.CC · 2024-11-24 · conditional · novelty 8.0

Sublevel sets of asymptotic tensor rank are Zariski-closed, making the parameter well-ordered in value, complete over the complex numbers, and computable from above.

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  • Asymptotic tensor rank is characterized by polynomials cs.CC · 2024-11-24 · conditional · none · ref 16 · internal anchor

    Sublevel sets of asymptotic tensor rank are Zariski-closed, making the parameter well-ordered in value, complete over the complex numbers, and computable from above.