Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4637935 | Journal of Computational and Applied Mathematics | 2016 | 14 Pages |
Abstract
We present an iterative algorithm, called the symmetric tensor eigen-rank-one iterative decomposition (STEROID), for decomposing a symmetric tensor into a real linear combination of symmetric rank-1 unit-norm outer factors using only eigendecompositions and least-squares fitting. Originally designed for a symmetric tensor with an order being a power of two, STEROID is shown to be applicable to any order through an innovative tensor embedding technique. Numerical examples demonstrate the high efficiency and accuracy of the proposed scheme even for large scale problems. Furthermore, we show how STEROID readily solves a problem in nonlinear block-structured system identification and nonlinear state-space identification.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Kim Batselier, Ngai Wong,