2019
DOI: 10.1016/j.jsc.2018.07.004
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On the factorization of non-commutative polynomials (in free associative algebras)

Abstract: By viewing non-commutative polynomials, that is, elements in free associative algebras, in terms of linear representations, we generalize Horner's rule to the non-commutative (multivariate) setting. We introduce the concept of Horner systems (which has parallels to that of companion matrices), discuss their construction and show how they enable the efficient evaluation of non-commutative polynomials by matrices.

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Cited by 9 publications
(43 citation statements)
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“…In general however, it is necessary to minimize a given system. For a polynomial ALS this is discussed in [22,Section 2.2], for the general case we refer to [20].…”
Section: Rational Operationsmentioning
confidence: 99%
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“…In general however, it is necessary to minimize a given system. For a polynomial ALS this is discussed in [22,Section 2.2], for the general case we refer to [20].…”
Section: Rational Operationsmentioning
confidence: 99%
“…The following definition is slightly adapted to avoid confusion with other transformation matrices for the factorization, formulated independent of a given admissible linear system. Definition 2.14 (Polynomial ALS and Transformation [22,Definition 24]). An ALS A = (u, A, v) of dimension n with system matrix A = (a ij ) for a non-zero…”
Section: Introductionmentioning
confidence: 99%
“…For details we refer to [2,Chapter 7] or [10,Section 6.4]. Since this work is only one part in a series, further information and references can be found in [11] (linear word problem, minimal inverse), [12] (polynomial factorization) and [13] (general factorization theory).…”
Section: Introductionmentioning
confidence: 99%
“…The intention of this paper is to be independent of the other three papers in this series (about the free field) as far as possible and leave it to the reader, for example, to interpret a standard form of the inverse of a polynomial as its factorization (into irreducible elements). Although the idea for minimizing "polynomial" linear representations is similar to that in Section 4, [12,Algorithm 32] is only a very special case of Algorithm 4.14 and the "refinement" in the former case is trivial.…”
Section: Introductionmentioning
confidence: 99%
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