We present a deterministic $2^{k^{\mathcal{O}(1)}} \text{poly}(n,d)$ time algorithm for decomposing $d$-dimensional, width-$n$ tensors of rank at most $k$ over $\mathbb{R}$ and $\mathbb{C}$. This improves upon the previous randomized algorithm of Peleg, Shpilka, and Volk (ITCS '24) that takes $2^{k^{k^{\mathcal{O}(k)}}} \text{poly}(n,d)$ time and the deterministic $n^{k^k}$ time algorithms of Bhargava, Saraf, ... more >>>

We give fast algorithms for low-rank tensor decomposition.
Over $\mathbb R$ and $\mathbb C$ we give a deterministic algorithm that takes as input an order-$d$, width-$n$, rank-$k$ tensor, gets measurement access to the tensor, runs in time
$k^{O(k^2)}\cdot \mathrm{poly}(n,d)$, and finds a minimal decomposition as a sum of rank-one ... more >>>