Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 0710.0510

Q-adic Transform revisited

Jean-Guillaume Dumas
View original source ↗Read source document ↗Export metadata ↓

Abstract

We present an algorithm to perform a simultaneous modular reduction of several residues. This algorithm is applied fast modular polynomial multiplication. The idea is to convert the $X$-adic representation of modular polynomials, with $X$ an indeterminate, to a $q$-adic representation where $q$ is an integer larger than the field characteristic. With some control on the different involved sizes it is then possible to perform some of the $q$-adic arithmetic directly with machine integers or floating points. Depending also on the number of performed numerical operations one can then convert back to the $q$-adic or $X$-adic representation and eventually mod out high residues. In this note we present a new version of both conversions: more tabulations and a way to reduce the number of divisions involved in the process are presented. The polynomial multiplication is then applied to arithmetic in small finite field extensions.

Explore related subjects

cs.SC

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jean-Guillaume Dumas. 2008-06-23. Q-adic Transform revisited. https://arxiv.org/abs/0710.0510

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Seven Limit Cycles in a Periodically Forced Three-Zone Equation

We construct a seven-cycle example for a periodically forced scalar equation with a continuous three-zone piecewise-linear nonlinearity. Symbolic integration and linear elimination reduce the four-flight matching conditions to two equations in the residence times. The competing exterior visits produce an additional turn of the critical response and lead to the rational parameters $(A,B,μ)=(9,3/2,181363/100000)$. At these parameters, finite Taylor estimates give three sign changes of the return displacement. Scalar order, analyticity along transverse itineraries, and half-period reflection then establish at least seven distinct isolated periodic orbits on the phase cylinder.

cs.SC↗
arXiv2026

Exact rational certificates and a Lean formalization of the eleven-variable Vasc inequality

We prove that the cyclic sum $\sum_{i=0}^{10}(x_i-x_{i+1})/(x_{i+1}+x_{i+2})$ is nonnegative for every strictly positive real eleven-tuple, with indices taken modulo eleven. Clearing denominators reduces the problem to a homogeneous polynomial. Subtracting the smallest coordinate then separates the proof into nonnegativity of a common-translation derivative and nonnegativity on a full boundary face. We supply exact rational polynomial certificates in Gram-matrix form for both statements. The derivative certificate uses a linear multiplier, centered monomials and cyclic averaging; the boundary certificate uses a strictly positive quadratic multiplier and uncentered monomials. Their verification requires 6,069 cyclic coefficient comparisons and 34,304 literal coefficient comparisons. All 606 certificate matrices are certified by integer congruences with strictly diagonally dominant images. A Lean 4 development formally verifies the certificate checks and their connection to the original inequality. The exact certificate data, replay programs, and formal sources are publicly available.

cs.SC↗
arXiv2026

RISR: Residual-Informed Scientific Equation Discovery with Large Language Models

Symbolic regression combines structural search with numerical fitting, but aggregate fit scores do not describe how the remaining error varies across inputs. We introduce RISR, a residual-informed method that uses these error patterns to guide formula discovery and learn which corrections are worth fitting. A residual encoder compresses aligned inputs, targets, current predictions, and residuals into continuous tokens that condition a language model to propose formulas. For subsequent refinement, a dual-view relational encoder uses additive and regularized multiplicative residuals to predict the post-fit utility of candidate corrections. We evaluate RISR on scientific tasks from the LLM-SRBench. RISR achieves 63.57% and 38.50% ID accuracy at the 1% and 0.1% pointwise relative-error tolerances, respectively. The corresponding OOD accuracies are 56.07% and 38.24%. RISR outperforms the reported baselines using the same backbone. The results show that our residual-informed approach can improve numerical equation recovery.

cs.SC↗