Abstract
Many problems in computer science and applied mathematics require rounding a vector 𝐰 of fractional values lying in the interval [0,1] to a binary vector 𝐱 so that, for a given matrix 𝐀, 𝐀𝐱 is as close to 𝐀𝐰 as possible. For example, this problem arises in LP rounding algorithms used to approximate NPhard optimization problems and in the design of uniformly distributed point sets for numerical integration. For a given matrix 𝐀, the worstcase error over all choices of 𝐰 incurred by the best possible rounding is measured by the linear discrepancy of 𝐀, a quantity studied in discrepancy theory, and introduced by Lovasz, Spencer, and Vesztergombi (EJC, 1986).
We initiate the study of the computational complexity of linear discrepancy. Our investigation proceeds in two directions: (1) proving hardness results and (2) finding both exact and approximate algorithms to evaluate the linear discrepancy of certain matrices. For (1), we show that linear discrepancy is NPhard. Thus we do not expect to find an efficient exact algorithm for the general case. Restricting our attention to matrices with a constant number of rows, we present a polytime exact algorithm for matrices consisting of a single row and matrices with a constant number of rows and entries of bounded magnitude. We also present an exponentialtime approximation algorithm for general matrices, and an algorithm that approximates linear discrepancy to within an exponential factor.
BibTeX  Entry
@InProceedings{li_et_al:LIPIcs:2020:12935,
author = {Lily Li and Aleksandar Nikolov},
title = {{On the Computational Complexity of Linear Discrepancy}},
booktitle = {28th Annual European Symposium on Algorithms (ESA 2020)},
pages = {69:169:16},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {9783959771627},
ISSN = {18688969},
year = {2020},
volume = {173},
editor = {Fabrizio Grandoni and Grzegorz Herman and Peter Sanders},
publisher = {Schloss DagstuhlLeibnizZentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2020/12935},
URN = {urn:nbn:de:0030drops129352},
doi = {10.4230/LIPIcs.ESA.2020.69},
annote = {Keywords: discrepancy theory, linear discrepancy, rounding, NPhardness}
}
Keywords: 

discrepancy theory, linear discrepancy, rounding, NPhardness 
Collection: 

28th Annual European Symposium on Algorithms (ESA 2020) 
Issue Date: 

2020 
Date of publication: 

26.08.2020 