Exploiting hidden structure in selecting dimensions that distinguish vectors. Froese, V., van Bevern, R., Niedermeier, R., & Sorge, M. Journal of Computer and System Sciences, 82(3):521–535, 2016.
Preprint doi abstract bibtex The NP-hard Distinct Vectors problem asks to delete as many columns as possible from a matrix such that all rows in the resulting matrix are still pairwise distinct. Our main result is that, for binary matrices, there is a complexity dichotomy for Distinct Vectors based on the maximum (H) and the minimum (h) pairwise Hamming distance between matrix rows: Distinct Vectors can be solved in polynomial time if H≤2⌈h/2⌉+1, and is NP-complete otherwise. Moreover, we explore connections of Distinct Vectors to hitting sets, thereby providing several fixed-parameter tractability and intractability results also for general matrices.
@article{FBNS16,
title = {Exploiting hidden structure in selecting dimensions
that distinguish vectors},
author = {Vincent Froese and René van Bevern and Rolf
Niedermeier and Manuel Sorge},
url_Preprint = {http://arxiv.org/abs/1512.01150},
doi = {10.1016/j.jcss.2015.11.011},
issn = {0022-0000},
year = 2016,
date = {2016-05-01},
journal = {Journal of Computer and System Sciences},
volume = 82,
number = 3,
pages = {521--535},
abstract = {The NP-hard Distinct Vectors problem asks to delete
as many columns as possible from a matrix such that
all rows in the resulting matrix are still pairwise
distinct. Our main result is that, for binary
matrices, there is a complexity dichotomy for
Distinct Vectors based on the maximum (H) and the
minimum (h) pairwise Hamming distance between matrix
rows: Distinct Vectors can be solved in polynomial
time if H≤2⌈h/2⌉+1, and is NP-complete
otherwise. Moreover, we explore connections of
Distinct Vectors to hitting sets, thereby providing
several fixed-parameter tractability and
intractability results also for general matrices.}
}
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