Multivariate to bivariate reduction for noncommutative polynomial factorization

IF 0.8 4区 计算机科学 Q3 COMPUTER SCIENCE, THEORY & METHODS
V. Arvind , Pushkar S. Joglekar
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引用次数: 0

Abstract

Based on Bergman's theorem, we show that multivariate noncommutative polynomial factorization is deterministic polynomial-time reducible to the factorization of bivariate noncommutative polynomials. More precisely,

  • 1.

    Given an n-variate noncommutative polynomial fFX over a field F as an arithmetic circuit, computing a complete factorization of f into irreducible factors is deterministic polynomial-time reducible to factorization of a noncommutative bivariate polynomial gFx,y; the reduction transforms f into a circuit for g, and given a complete factorization of g, the reduction recovers a complete factorization of f in polynomial time.

    The reduction works both in the white-box and the black-box setting.

  • 2.

    We show over the field of rationals that bivariate linear matrix factorization problem for 4×4 matrices is at least as hard as factoring square-free integers and for 3×3 matrices it is in polynomial time.

非交换多项式因式分解的多变量到双变量还原
基于伯格曼定理,我们证明了多元非交换多项式因式分解与二元非交换多项式因式分解是确定性多项式时间可简化的。更确切地说,1.给定一个 F 域上的 n 变量非交换多项式 f∈F〈X〉作为算术电路,计算 f 的完整因式分解为不可还原因式是确定性多项式时间可还原为非交换二元多项式 g∈F〈x,y〉的因式分解;还原法将 f 转化为 g 的电路,给定 g 的完整因式分解,还原法就能在多项式时间内恢复 f 的完整因式分解。还原法在白箱和黑箱环境中都有效2。.我们在有理数域上证明,4×4 矩阵的双变量线性矩阵因式分解问题至少与无平方整数因式分解一样难,而对于 3×3 矩阵,则只需多项式时间。
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来源期刊
Information and Computation
Information and Computation 工程技术-计算机:理论方法
CiteScore
2.30
自引率
0.00%
发文量
119
审稿时长
140 days
期刊介绍: Information and Computation welcomes original papers in all areas of theoretical computer science and computational applications of information theory. Survey articles of exceptional quality will also be considered. Particularly welcome are papers contributing new results in active theoretical areas such as -Biological computation and computational biology- Computational complexity- Computer theorem-proving- Concurrency and distributed process theory- Cryptographic theory- Data base theory- Decision problems in logic- Design and analysis of algorithms- Discrete optimization and mathematical programming- Inductive inference and learning theory- Logic & constraint programming- Program verification & model checking- Probabilistic & Quantum computation- Semantics of programming languages- Symbolic computation, lambda calculus, and rewriting systems- Types and typechecking
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