Complete Decomposition of Symmetric Tensors in Linear Time and Polylogarithmic Precision

TL;DR

This paper presents a groundbreaking randomized algorithm for symmetric tensor decomposition that operates in linear time and uses only polylogarithmic precision, significantly improving efficiency over previous methods.

Contribution

It introduces the first finite-precision, linear-time algorithm for symmetric tensor decomposition under specific assumptions, advancing computational efficiency in tensor analysis.

Findings

Algorithm runs in O(n^3) arithmetic operations.

Requires only poly-logarithmic bits of precision.

Works with high probability under given conditions.

Abstract

We study symmetric tensor decompositions, i.e. decompositions of the input symmetric tensor T of order 3 as sum of r 3rd-order tensor powers of u_i where u_i are vectors in \C^n. In order to obtain efficient decomposition algorithms, it is necessary to require additional properties from the u_i. In this paper we assume that the u_i are linearly independent. This implies that r is at most n, i.e., the decomposition of T is undercomplete. We will moreover assume that r=n (we plan to extend this work to the case where r is strictly less than n in a forthcoming paper). We give a randomized algorithm for the following problem: given T, an accuracy parameter epsilon, and an upper bound B on the condition number of the tensor, output vectors u'_i such that u_i and u'_i differ by at most epsilon (in the l_2 norm and up to permutation and multiplication by phases) with high probability. The main novel features of our algorithm are: (1) We provide the first algorithm for this problem that works in the computation model of finite arithmetic and requires only poly-logarithmic (in n, B and 1/epsilon) many bits of precision. (2) Moreover, this is also the first algorithm that runs in linear time in the size of the input tensor. It requires O(n^3) arithmetic operations for all accuracy parameters epsilon = 1/poly(n).