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We extend the shell and kernel reductions for hyperexponential functions over the field of rational functions to a monomial extension. Both of the reductions are incorporated into one algorithm. As an application, we present an additive…

Symbolic Computation · Computer Science 2023-10-03 Shaoshi Chen , Hao Du , Yiman Gao , Ziming Li

Existing implementations of gradient-based optimisation methods typically assume that the problem is posed in Euclidean space. When solving optimality problems on function spaces, the functional derivative is then inaccurately represented…

Optimization and Control · Mathematics 2016-06-28 Tobias Schwedes , Simon W. Funke , David A. Ham

The differential-reduction algorithm, which allows one to express generalized hypergeometric functions with parameters of arbitrary values in terms of such functions with parameters whose values differ from the original ones by integers, is…

High Energy Physics - Theory · Physics 2010-05-19 Vladimir V. Bytev , Mikhail Yu. Kalmykov , Bernd A. Kniehl

We give two algebro-geometric inspired approaches to fast algorithms for Fourier transforms in algebraic signal processing theory based on polynomial algebras in several variables. One is based on module induction and one is based on a…

Numerical Analysis · Mathematics 2024-12-20 Bastian Seifert

Dimensionally or analytically regulated Feynman integrals lead to relative twisted period integrals. We present a recent extension of the Griffiths-Dwork pole reduction algorithm for deriving the D-module of differential operators acting on…

Algebraic Geometry · Mathematics 2026-04-13 Pierre Vanhove

Feature Transformation (FT) crafts new features from original ones via mathematical operations to enhance dataset expressiveness for downstream models. However, existing FT methods exhibit critical limitations: discrete search struggles…

Machine Learning · Computer Science 2025-05-22 Nanxu Gong , Zijun Li , Sixun Dong , Haoyue Bai , Wangyang Ying , Xinyuan Wang , Yanjie Fu

Because of its ineffectiveness, the usual arithmetic Hilbert-Samuel formula is not applicable in the context of Diophantine Approximation. In order to overcome this difficulty, the present paper presents explicit estimates for arithmetic…

Algebraic Geometry · Mathematics 2016-01-27 Heinrich Massold

Assume that f is a strict convex function with a unique minimum in R^n. We divide the vector of n-variables to d groups of vector subvariables with d at least two. We assume that we can find the partial minimum of f with respect to each…

Optimization and Control · Mathematics 2019-06-06 Shmuel Friedland

In the analysis of High-Energy Physics data, it is frequently desired to separate resonant signals from a smooth, non-resonant background. This paper introduces a new technique - functional decomposition (FD) - to accomplish this task. It…

Data Analysis, Statistics and Probability · Physics 2018-05-15 Ryan Edgar , Dante Amidei , Christopher Grud , Karishma Sekhon

We introduce \emph{ReMatching}, a novel shape correspondence solution based on the functional maps framework. Our method, by exploiting a new and appropriate \emph{re}-meshing paradigm, can target shape-\emph{matching} tasks even on meshes…

Graphics · Computer Science 2025-03-14 Filippo Maggioli , Daniele Baieri , Emanuele Rodolà , Simone Melzi

We propose a new method of constraining the redshifts of individual extragalactic sources based on celestial coordinates and their ensemble statistics. Techniques from integer linear programming are utilized to optimize simultaneously for…

Instrumentation and Methods for Astrophysics · Physics 2016-11-15 Benjamin C. G. Lee , Tamás Budavári , Amitabh Basu , Mubdi Rahman

We present \emph{telescoping} recursive representations for both continuous and discrete indexed noncausal Gauss-Markov random fields. Our recursions start at the boundary (a hypersurface in $\R^d$, $d \ge 1$) and telescope inwards. For…

Information Theory · Computer Science 2015-03-13 Divyanshu Vats , Jose M. F. Moura

This work explores the application of the fast assembly and formation strategy from [8, 17] to trimmed bi-variate parameter spaces. Two concepts for the treatment of basis functions cut by the trimming curve are investigated: one employs a…

Computational Engineering, Finance, and Science · Computer Science 2023-08-29 Benjamin Marussig

In this paper, we propose a method for the approximation of the solution of high-dimensional weakly coercive problems formulated in tensor spaces using low-rank approximation formats. The method can be seen as a perturbation of a minimal…

Numerical Analysis · Mathematics 2015-02-13 Marie Billaud-Friess , Anthony Nouy , Olivier Zahm

A new algorithm for one-dimensional minimization is described in detail and the results of some tests on practical cases are reported and illustrated. The method requires only punctual computation of the function, and is suitable to be…

Optimization and Control · Mathematics 2017-08-24 Glauco Masotti

Recent advances in 3D Gaussian Splatting (3DGS) have focused on accelerating optimization while preserving reconstruction quality. However, many proposed methods entangle implementation-level improvements with fundamental algorithmic…

Computer Vision and Pattern Recognition · Computer Science 2026-02-11 Florian Hahlbohm , Linus Franke , Martin Eisemann , Marcus Magnor

HYPERDIRE is a project devoted to the creation of a set of Mathematica based programs for the differential reduction of hypergeometric functions. The current version includes two parts: one, pfq, is relevant for manipulations of…

Mathematical Physics · Physics 2013-09-09 Vladimir V. Bytev , Mikhail Yu. Kalmykov , Bernd A. Kniehl

We propose a mesh refinement technique for solving elliptic difference equations on unbounded domains based on the fast lattice Green's function (FLGF) method. The FLGF method exploits the regularity of the Cartesian mesh and uses the fast…

Computational Physics · Physics 2020-02-19 Benedikt Dorschner , Ke Yu , Gianmarco Mengaldo , Tim Colonius

We present local discriminative Gaussian (LDG) dimensionality reduction, a supervised dimensionality reduction technique for classification. The LDG objective function is an approximation to the leave-one-out training error of a local…

Machine Learning · Computer Science 2012-06-22 Nathan Parrish , Maya Gupta

Our interest lies in developing some efficient methods for minimizing the sum of two geodesically convex functions on Hadamard manifolds, with the aim to enhance the convergence of the Douglas-Rachford algorithm in Hadamard manifolds.…

Optimization and Control · Mathematics 2026-02-17 D. R. Sahu , Shikher Sharma , Pankaj Gautam
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