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The distributional analysis of Euclidean algorithms was carried out by Baladi and Vall\'{e}e. They showed the asymptotic normality of the number of division steps and associated costs in the Euclidean algorithm as a random variable on the…

Dynamical Systems · Mathematics 2025-10-27 Dohyeong Kim , Jungwon Lee , Seonhee Lim

We present a $p$-adic algorithm to recover the lexicographic Gr\"obner basis $\mathcal G$ of an ideal in $\mathbb Q[x,y]$ with a generating set in $\mathbb Z[x,y]$, with a complexity that is less than cubic in terms of the dimension of…

Commutative Algebra · Mathematics 2023-12-22 Eric Schost , Catherine St-Pierre

In addition to rather complicated general methods it is interesting and valuable to develop fast efficient methods for calculating generators of power integral bases in special types of number fields. We consider sextic fields containing a…

Number Theory · Mathematics 2021-02-22 István Gaál

We present an efficient algorithm for computing the leading monomials of a minimal Groebner basis of a generic sequence of homogeneous polynomials. Our approach bypasses costly polynomial reductions by exploiting structural properties…

Symbolic Computation · Computer Science 2026-05-12 Kosuke Sakata , Tsuyoshi Takagi

Two correspondences have been provided that associate any linear code over a finite field with a binomial ideal. In this paper, algorithms for computing their Graver bases and universal Gr\"obner bases are given. To this end, a connection…

Commutative Algebra · Mathematics 2014-05-08 Natalia Dück , Karl-Heinz Zimmermann

Computing pseudospectra of non-normal matrices is essential for understanding the stability and transient behavior of dynamical systems. Such analysis is critical in applications including fluid dynamics, control systems, and differential…

Numerical Analysis · Mathematics 2026-05-07 Amit Punia , Rakesh Kumar , Madan Lal

We develop an algorithm for recursively constructing orthogonal multiplet bases for the color space of QCD, for any order of partons and any Nc. This recipe is then applied for explicitly constructing some of these bases. Using the bases, a…

High Energy Physics - Phenomenology · Physics 2018-12-26 Malin Sjodahl , Johan Thorén

Domain generalization (DG) aims to learn predictive models that can generalize to unseen domains. Most existing DG approaches focus on learning domain-invariant representations under the assumption of conditional distribution shift (i.e.,…

Machine Learning · Computer Science 2026-02-03 Jewon Yeom , Kyubyung Chae , Hyunggyu Lim , Yoonna Oh , Dongyoon Yang , Taesup Kim

Graph Neural Networks (GNNs) have made significant advances on several fundamental inference tasks. As a result, there is a surge of interest in using these models for making potentially important decisions in high-regret applications.…

Machine Learning · Computer Science 2020-02-26 Kaidi Xu , Sijia Liu , Pin-Yu Chen , Mengshu Sun , Caiwen Ding , Bhavya Kailkhura , Xue Lin

Motivated by the question of whether Chow polynomials of matroids have only real roots, this article revisits the known relationship between Eulerian polynomials and the Hilbert series of Chow rings of permutohedral varieties. This is done…

Combinatorics · Mathematics 2024-10-21 Basile Coron

Probabilistic models over strings have played a key role in developing methods allowing indels to be treated as phylogenetically informative events. There is an extensive literature on using automata and transducers on phylogenies to do…

Populations and Evolution · Quantitative Biology 2013-07-15 Alexandre Bouchard-Côté

We develop the tools to bound extreme roots of multivariate real zero polynomials globally. This is done through the use of a relaxation that approximates their rigidly convex sets. This relaxation can easily be constructed using the degree…

Combinatorics · Mathematics 2025-10-14 Alejandro González Nevado

Interest has been rising lately towards methods representing data in non-Euclidean spaces, e.g. hyperbolic or spherical, that provide specific inductive biases useful for certain real-world data properties, e.g. scale-free, hierarchical or…

Machine Learning · Computer Science 2020-05-20 Gregor Bachmann , Gary Bécigneul , Octavian-Eugen Ganea

We generalise Gabidulin codes to the case of infinite fields, eventually with characteristic zero. For this purpose, we consider an abstract field extension and any automorphism in the Galois group. We derive some conditions on the…

Information Theory · Computer Science 2017-03-28 Daniel Augot , Pierre Loidreau , Gwezheneg Robert

Kohn introduced in 1979 the algorithm of multipliers to study the subelliptc estimate of the $\bar\partial$-Neumann problem for a smooth weakly pseudoconvex domain in a complex Euclidean space which satisfies D'Angelo's finite type…

Complex Variables · Mathematics 2023-12-12 Yum-Tong Siu

Riemannian diffusion models draw inspiration from standard Euclidean space diffusion models to learn distributions on general manifolds. Unfortunately, the additional geometric complexity renders the diffusion transition term inexpressible…

Machine Learning · Computer Science 2023-11-01 Aaron Lou , Minkai Xu , Stefano Ermon

Multiplication of polynomials is among key operations in computer algebra which plays important roles in developing techniques for other commonly used polynomial operations such as division, evaluation/interpolation, and factorization. In…

Numerical Analysis · Mathematics 2022-06-02 S. Karami , M. Ahmadnasab , M. Hadizadeh , A. Amiraslani

This paper introduces a strategy for signature-based algorithms to compute Groebner basis. The signature-based algorithms generate S-pairs instead of S-polynomials, and use s-reduction instead of the usual reduction used in the Buchberger…

Symbolic Computation · Computer Science 2018-12-03 Kosuke Sakata

We extend the Brauer-Siegel theorem to new families of number fields, both in the classical setting of asymptotically bad families and in the more general framework due to Tsfasman and Vl\u{a}du\c{t} of asymptotically exact families. We…

Number Theory · Mathematics 2024-05-24 Richard Griffon , Philippe Lebacque , Gaël Rémond

We define a class of Euclidean distances on weighted graphs, enabling to perform thermodynamic soft graph clustering. The class can be constructed form the "raw coordinates" encountered in spectral clustering, and can be extended by means…

Machine Learning · Statistics 2010-09-15 François Bavaud