Optimization via Separated Representations and the Canonical Tensor Decomposition
Numerical Analysis
2017-09-13 v1 Optimization and Control
Abstract
We introduce a new, quadratically convergent algorithm for finding maximum absolute value entries of tensors represented in the canonical format. The computational complexity of the algorithm is linear in the dimension of the tensor. We show how to use this algorithm to find global maxima of non-convex multivariate functions in separated form. We demonstrate the performance of the new algorithms on several examples.
Cite
@article{arxiv.1605.05789,
title = {Optimization via Separated Representations and the Canonical Tensor Decomposition},
author = {Matthew J Reynolds and Gregory Beylkin and Alireza Doostan},
journal= {arXiv preprint arXiv:1605.05789},
year = {2017}
}
Comments
13 pages, 4 figures