English

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 MAJXORMAJ \circ XOR requires size 20.18n2^{0.18 n}. This completely answers an open question of Tur{\'a}n and Vatan [FoCM'97]. We also show that the spectral classes PL1,PLPL_1, PL_\infty, and the polynomial threshold function classes PT^1,PT1\widehat{PT}_1, PT_1, 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

R2 v1 2026-06-23T07:14:52.723Z