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        <identifier>oai:drops-oai.dagstuhl.de:5826</identifier>
        <datestamp>2024-03-06T10:37:12Z</datestamp>
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          <dc:title>Functional Lower Bounds for Arithmetic Circuits and Connections to Boolean Circuit Complexity</dc:title>
          <dc:creator>Forbes, Michael A.</dc:creator>
          <dc:creator>Kumar, Mrinal</dc:creator>
          <dc:creator>Saptharishi, Ramprasad</dc:creator>
          <dc:subject>boolean circuits</dc:subject>
          <dc:subject>arithmetic circuits</dc:subject>
          <dc:subject>lower bounds</dc:subject>
          <dc:subject>functional computation</dc:subject>
          <dc:description>We say that a circuit C over a field F {functionally} computes a polynomial P in F[x_1, x_2, ..., x_n] if for every x in {0,1}^n we have that C(x) = P(x). This is in contrast to syntactically computing P, when C = P as formal polynomials. In this paper, we study the question of proving lower bounds for homogeneous depth-3 and depth-4 arithmetic circuits for functional computation. We prove the following results: &#13;
&#13;
1. Exponential lower bounds for homogeneous depth-3 arithmetic circuits for a polynomial in VNP. &#13;
&#13;
2. Exponential lower bounds for homogeneous depth-4 arithmetic circuits with bounded individual degree for a polynomial in VNP. &#13;
&#13;
Our main motivation for this line of research comes from our observation that strong enough functional lower bounds for even very special depth-4 arithmetic circuits for the Permanent imply a separation between #P and ACC0. Thus, improving the second result to get rid of the bounded individual degree condition could lead to substantial  progress in boolean circuit complexity. Besides, it is known from a recent result of  Kumar and Saptharishi [Kumar/Saptharishi, ECCC 2015] that over constant sized finite fields, strong enough {average case} functional lower bounds for homogeneous depth-4 circuits imply superpolynomial lower bounds for homogeneous depth-5 circuits. &#13;
&#13;
Our proofs are based on a family of new complexity measures called shifted evaluation dimension, and might be of independent interest.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Michael A. Forbes and Mrinal Kumar and Ramprasad Saptharishi</dc:contributor>
          <dc:date>2016</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 50, 31st Conference on Computational Complexity (CCC 2016)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
          <dc:type>doc-type:ResearchArticle</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.CCC.2016.33</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-58266</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CCC.2016.33</dc:identifier>
          <dc:language>eng</dc:language>
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