Inner Rank and Lower Bounds for Matrix Multiplication
Computational Complexity
2019-05-15 v2 Data Structures and Algorithms
Abstract
We develop a notion of {\em inner rank} as a tool for obtaining lower bounds on the rank of matrix multiplication tensors. We use it to give a short proof that the border rank (and therefore rank) of the tensor associated with matrix multiplication over an arbitrary field is at least . While inner rank does not provide improvements to currently known lower bounds, we argue that this notion merits further study.
Cite
@article{arxiv.1706.04225,
title = {Inner Rank and Lower Bounds for Matrix Multiplication},
author = {Joel Friedman},
journal= {arXiv preprint arXiv:1706.04225},
year = {2019}
}
Comments
Errors in many of the results (starting with those of Section 4) due to an "exchange of indices."