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        <identifier>oai:drops-oai.dagstuhl.de:4730</identifier>
        <datestamp>2024-03-06T10:35:20Z</datestamp>
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          <dc:title>Improved Lower Bounds for Testing Triangle-freeness in Boolean Functions via Fast Matrix Multiplication</dc:title>
          <dc:creator>Fu, Hu</dc:creator>
          <dc:creator>Kleinberg, Robert</dc:creator>
          <dc:subject>Property testing</dc:subject>
          <dc:subject>linear invariance</dc:subject>
          <dc:subject>fast matrix multiplication</dc:subject>
          <dc:subject>uniquely solvable puzzles</dc:subject>
          <dc:description>Understanding the query complexity for testing linear-invariant properties has been a central open problem in the study of algebraic property testing. Triangle-freeness in Boolean functions is a simple property whose testing complexity is unknown. Three Boolean functions f1, f2 and f3, mapping {0,1}^k to {0,1}, are said to be triangle free if there is no x, y in {0,1}^k such that f1(x) = f2(y) = f3(x + y) = 1.  This property is known to be strongly testable (Green 2005), but the number of queries needed is upper-bounded only by a tower of twos whose height is polynomial in 1 / epsislon, where epsislon is the distance between the tested function triple and triangle-freeness, i.e., the minimum fraction of function values that need to be modified to make the triple triangle free. A lower bound of (1 / epsilon)^2.423 for any one-sided tester was given by Bhattacharyya and Xie (2010). In this work we improve this bound to (1 / epsilon)^6.619. Interestingly, we prove this by way of a combinatorial construction called uniquely solvable puzzles that was at the heart of Coppersmith and Winograd's renowned matrix multiplication algorithm.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Hu Fu and Robert Kleinberg</dc:contributor>
          <dc:date>2014</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 28, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2014)</dc:relation>
          <dc:type>InProceedings</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.APPROX-RANDOM.2014.669</dc:identifier>
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          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX-RANDOM.2014.669</dc:identifier>
          <dc:language>eng</dc:language>
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