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        <identifier>oai:drops-oai.dagstuhl.de:16575</identifier>
        <datestamp>2024-03-06T10:57:50Z</datestamp>
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          <dc:title>A Better-Than-3log(n) Depth Lower Bound for De Morgan Formulas with Restrictions on Top Gates</dc:title>
          <dc:creator>Mihajlin, Ivan</dc:creator>
          <dc:creator>Sofronova, Anastasia</dc:creator>
          <dc:subject>formula complexity</dc:subject>
          <dc:subject>communication complexity</dc:subject>
          <dc:subject>Karchmer-Raz-Wigderson conjecture</dc:subject>
          <dc:subject>De Morgan formulas</dc:subject>
          <dc:description>We prove that a modification of Andreev’s function is not computable by (3 + α - ε) log(n) depth De Morgan formula with (2α - ε)log{n} layers of AND gates at the top for any 0 &lt; α &lt; 1/5 and any constant ε &gt; 0. In order to do this, we prove a weak variant of Karchmer-Raz-Wigderson conjecture. To be more precise, we prove the existence of two functions f : {0,1}ⁿ → {0,1} and g : {0,1}ⁿ → {0,1}ⁿ such that f(g(x) ⊕ y) is not computable by depth (1 + α - ε) n formulas with (2 α - ε) n layers of AND gates at the top. We do this by a top-down approach, which was only used before for depth-3 model. &#13;
Our technical contribution includes combinatorial insights into structure of composition with random boolean function, which led us to introducing a notion of well-mixed sets. A set of functions is well-mixed if, when composed with a random function, it does not have subsets that agree on large fractions of inputs. We use probabilistic method to prove the existence of well-mixed sets.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Ivan Mihajlin and Anastasia Sofronova</dc:contributor>
          <dc:date>2022</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 234, 37th Computational Complexity Conference (CCC 2022)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.CCC.2022.13</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-165755</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CCC.2022.13</dc:identifier>
          <dc:language>eng</dc:language>
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