Subspace power method for symmetric tensor decomposition

We introduce the Subspace Power Method (SPM) for calculating the CP decomposition of low-rank real symmetric tensors. This algorithm calculates one new CP component at a time, alternating between applying the shifted symmetric higher-order power method (SS-HOPM) to a certain modified tensor, constructed from a matrix flattening of the original tensor; and using appropriate deflation steps. We obtain rigorous guarantees for SPM regarding convergence and global optima for input tensors of dimension $d$ and order $m$ of rank up to $O(d^{\lfloor m/2\rfloor})$, via results in classical algebraic geometry and optimization theory. As a by-product of our analysis we prove that SS-HOPM converges unconditionally, settling a conjecture of Kolda-Mayo. Numerical experiments demonstrate that SPM is roughly one order of magnitude faster than state-of-the-art CP decomposition algorithms at moderate ranks. Furthermore, prior knowledge of the CP rank is not required by SPM.