Zachary Hough | Searcharxiv

arXiv2018

Degree-optimal moving frames for rational curves

A $\mathit{\text{moving frame}}$ at a rational curve is a basis of vectors moving along the curve. When the rational curve is given parametrically by a row vector $\mathbf{a}$ of univariate polynomials, a moving frame with important algebraic properties can be defined by the columns of an invertible polynomial matrix $P$, such that $\mathbf{a} P=[\gcd(\mathbf{a}),0\ldots,0]$. A $\mathit{\text{degree-optimal moving frame}}$ has column-wise minimal degree, where the degree of a column is defined to be the maximum of the degrees of its components. Algebraic moving frames are closely related to the univariate versions of the celebrated Quillen-Suslin problem, effective Nullstellensatz problem, and syzygy module problem. However, this paper appears to be the first devoted to finding an efficient algorithm for constructing a degree-optimal moving frame, a property desirable in various applications. We compare our algorithm with other possible approaches, based on already available algorithms, and show that it is more efficient. We also establish several new theoretical results concerning the degrees of an optimal moving frame and its components. In addition, we show that any deterministic algorithm for computing a degree-optimal algebraic moving frame can be augmented so that it assigns a degree-optimal moving frame in a $GL_n(\mathbb{K})$-equivariant manner. This crucial property of classical geometric moving frames, in combination with the algebraic properties, can be exploited in various problems.

math.AG↗

arXiv2017

Algorithm for computing $μ$-bases of univariate polynomials

We present a new algorithm for computing a $μ$-basis of the syzygy module of $n$ polynomials in one variable over an arbitrary field $\mathbb{K}$. The algorithm is conceptually different from the previously-developed algorithms by Cox, Sederberg, Chen, Zheng, and Wang for $n=3$, and by Song and Goldman for an arbitrary $n$. It involves computing a "partial" reduced row-echelon form of a $ (2d+1)\times n(d+1)$ matrix over $\mathbb{K}$, where $d$ is the maximum degree of the input polynomials. The proof of the algorithm is based on standard linear algebra and is completely self-contained. It includes a proof of the existence of the $μ$-basis and as a consequence provides an alternative proof of the freeness of the syzygy module. The theoretical (worst case asymptotic) computational complexity of the algorithm is $O(d^2n+d^3+n^2)$. We have implemented this algorithm (HHK) and the one developed by Song and Goldman (SG). Experiments on random inputs indicate that SG gets faster than HHK when $d$ gets sufficiently large for a fixed $n$, and that HHK gets faster than SG when $n$ gets sufficiently large for a fixed $d$.

math.AG↗