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        <identifier>oai:drops-oai.dagstuhl.de:18548</identifier>
        <datestamp>2024-03-06T11:02:05Z</datestamp>
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          <dc:title>Multivariate to Bivariate Reduction for Noncommutative Polynomial Factorization</dc:title>
          <dc:creator>Arvind, Vikraman</dc:creator>
          <dc:creator>Joglekar, Pushkar S.</dc:creator>
          <dc:subject>Arithmetic circuits</dc:subject>
          <dc:subject>algebraic branching programs</dc:subject>
          <dc:subject>polynomial factorization</dc:subject>
          <dc:subject>automata</dc:subject>
          <dc:subject>noncommutative polynomial ring</dc:subject>
          <dc:description>Based on a theorem of Bergman [Cohn, 2006] we show that multivariate noncommutative polynomial factorization is deterministic polynomial-time reducible to the factorization of bivariate noncommutative polynomials. More precisely, we show the following:  &#13;
1) In the white-box setting, given an n-variate noncommutative polynomial f ∈ 𝔽⟨X⟩ over a field 𝔽 (either a finite field or the rationals) as an arithmetic circuit (or algebraic branching program), computing a complete factorization of f into irreducible factors is deterministic polynomial-time reducible to white-box factorization of a noncommutative bivariate polynomial g ∈ 𝔽⟨x,y⟩; the reduction transforms f into a circuit for g (resp. ABP for g), and given a complete factorization of g (namely, arithmetic circuits (resp. ABPs) for irreducible factors of g) the reduction recovers a complete factorization of f in polynomial time.&#13;
We also obtain a similar deterministic polynomial-time reduction in the black-box setting.&#13;
2) Additionally, we show over the field of rationals that bivariate linear matrix factorization of 4× 4 matrices is at least as hard as factoring square-free integers. This indicates that reducing noncommutative polynomial factorization to linear matrix factorization (as done in [Vikraman Arvind and Pushkar S. Joglekar, 2022]) is unlikely to succeed over the field of rationals even in the bivariate case. In contrast, multivariate linear matrix factorization for 3×3 matrices over rationals is in polynomial time.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Vikraman Arvind and Pushkar S. Joglekar</dc:contributor>
          <dc:date>2023</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 272, 48th International Symposium on Mathematical Foundations of Computer Science (MFCS 2023)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.MFCS.2023.14</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-185480</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.MFCS.2023.14</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
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