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        <datestamp>2024-03-06T10:51:07Z</datestamp>
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          <dc:title>On the Computational Complexity of Linear Discrepancy</dc:title>
          <dc:creator>Li, Lily</dc:creator>
          <dc:creator>Nikolov, Aleksandar</dc:creator>
          <dc:subject>discrepancy theory</dc:subject>
          <dc:subject>linear discrepancy</dc:subject>
          <dc:subject>rounding</dc:subject>
          <dc:subject>NP-hardness</dc:subject>
          <dc:description>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 NP-hard optimization problems and in the design of uniformly distributed point sets for numerical integration. For a given matrix 𝐀, the worst-case 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).&#13;
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 NP-hard. 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 poly-time 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 exponential-time approximation algorithm for general matrices, and an algorithm that approximates linear discrepancy to within an exponential factor.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Lily Li and Aleksandar Nikolov</dc:contributor>
          <dc:date>2020</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 173, 28th Annual European Symposium on Algorithms (ESA 2020)</dc:relation>
          <dc:type>InProceedings</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.ESA.2020.69</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-129352</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2020.69</dc:identifier>
          <dc:language>eng</dc:language>
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