Lower Bounds for Linear Decision Lists
Computational Complexity
2019-01-18 v1
Abstract
We demonstrate a lower bound technique for linear decision lists, which are decision lists where the queries are arbitrary linear threshold functions. We use this technique to prove an explicit lower bound by showing that any linear decision list computing the function requires size . This completely answers an open question of Tur{\'a}n and Vatan [FoCM'97]. We also show that the spectral classes , and the polynomial threshold function classes , are incomparable to linear decision lists.
Cite
@article{arxiv.1901.05911,
title = {Lower Bounds for Linear Decision Lists},
author = {Arkadev Chattopadhyay and Meena Mahajan and Nikhil Mande and Nitin Saurabh},
journal= {arXiv preprint arXiv:1901.05911},
year = {2019}
}
Comments
18 pages