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In this paper, we give a detailed account of the algorithm outlined in [1] for Feynman integral reduction and $\varepsilon$-factorised differential equations. The algorithm consists of two steps. In the first step, we use a new geometric…

Recasting the $N$-point one loop scalar integral from Feynman to Schwinger parameters gives an integrand with a Gaussian form. By application of a Fourier transform, it is easy to derive explicit expressions for the two, three and…

High Energy Physics - Phenomenology · Physics 2017-11-27 Kamel Benhaddou

The goal of tensor completion is to recover a tensor from a subset of its entries, often by exploiting its low-rank property. Among several useful definitions of tensor rank, the low-tubal-rank was shown to give a valuable characterization…

Machine Learning · Computer Science 2022-10-18 Yicong He , George K. Atia

We present new versions of the Mathematica package FeynCalc and the FeynHelpers add-on that represent an important contribution to the collection of public codes for semi-automatic evaluation of multiloop Feynman diagrams. FeynHelpers…

High Energy Physics - Phenomenology · Physics 2025-12-24 Vladyslav Shtabovenko

We derive a compact analytic formula for a complete basis of conformally invariant tensor structures for three-point functions of conserved operators in arbitrary 4D Lorentz representations. The construction follows directly from a novel…

High Energy Physics - Theory · Physics 2026-01-09 Paul Heslop , Hector Puerta Ramisa

New methods for obtaining functional equations for Feynman integrals are presented. Application of these methods for finding functional equations for various one- and two- loop integrals described in detail. It is shown that with the aid of…

High Energy Physics - Phenomenology · Physics 2015-12-31 O. V. Tarasov

An efficient way to calculate one-loop counterterms within the Feynman diagrammatic approach and dimensional regularization is to expand the propagators in the integrands of the Feynman integrals around vanishing external momentum. In this…

High Energy Physics - Phenomenology · Physics 2019-09-04 Christian F. Steinwachs

Present and future high-precision tests of the Standard Model and beyond for the fundamental constituents and interactions in Nature are demanding complex perturbative calculations involving multi-leg and multi-loop Feynman diagrams.…

High Energy Physics - Phenomenology · Physics 2011-04-15 Luis G. Cabral-Rosetti , Miguel A. Sanchis-Lozano

In this article, we present the package {\tt Blade} as the first implementation of the block-triangular form improved Feynman integral reduction method. The block-triangular form has orders of magnitude fewer equations compared to the plain…

High Energy Physics - Phenomenology · Physics 2025-02-07 Xin Guan , Xiao Liu , Yan-Qing Ma , Wen-Hao Wu

Tensors play a central role in many modern machine learning and signal processing applications. In such applications, the target tensor is usually of low rank, i.e., can be expressed as a sum of a small number of rank one tensors. This…

Machine Learning · Statistics 2015-05-18 Parikshit Shah , Nikhil Rao , Gongguo Tang

We describe our method for rewriting dimensionally regulated Feynman parameter integrals in the Minkowski regime as a sum of real, positive integrands multiplied by complex prefactors. This representation eliminates the need for a contour…

High Energy Physics - Phenomenology · Physics 2026-03-06 Stephen P. Jones , Anton Olsson , Thomas Stone

The goal of tensor completion is to fill in missing entries of a partially known tensor (possibly including some noise) under a low-rank constraint. This may be formulated as a least-squares problem. The set of tensors of a given…

Numerical Analysis · Mathematics 2018-12-03 Gennadij Heidel , Volker Schulz

Classical functional calculus is primarily spectral, capturing eigenvalue information through resolvent methods while largely ignoring nilpotent structure. Building on the projector-nilpotent characterization developed in our companion…

Functional Analysis · Mathematics 2026-05-14 Shih-Yu Chang

Tensor completion is a fundamental tool for incomplete data analysis, where the goal is to predict missing entries from partial observations. However, existing methods often make the explicit or implicit assumption that the observed entries…

Machine Learning · Statistics 2022-03-18 Yuning Qiu , Guoxu Zhou , Qibin Zhao , Shengli Xie

In this paper, we introduce a method for multivariate function approximation using function evaluations, Chebyshev polynomials, and tensor-based compression techniques via the Tucker format. We develop novel randomized techniques to…

Numerical Analysis · Mathematics 2021-07-29 Arvind K. Saibaba , Rachel Minster , Misha E. Kilmer

Tensor sparse modeling as a promising approach, in the whole of science and engineering has been a huge success. As is known to all, various data in practical application are often generated by multiple factors, so the use of tensors to…

Computer Vision and Pattern Recognition · Computer Science 2022-07-12 Hongbing Zhang , Xinyi Liu , Hongtao Fan , Yajing Li , Yinlin Ye

We review recent progress that we have achieved in evaluating the class of fully massive vacuum integrals at five loops. After discussing topics that arise in classification, evaluation and algorithmic codification of this specific set of…

High Energy Physics - Phenomenology · Physics 2016-12-21 Thomas Luthe , York Schroder

A scheme for systematically achieving accurate numerical evaluation of multi-loop Feynman diagrams is developed. This shows the feasibility of a project aimed to produce a complete calculation for two-loop predictions in the Standard Model.…

High Energy Physics - Phenomenology · Physics 2008-11-26 G. Passarino

We present a generally applicable reduction formalism which makes it possible to express an arbitrary tensor and scalar one-loop Feynman integral, with N external lines and massless propagators, in terms of a basic set of eight fundamental…

High Energy Physics - Phenomenology · Physics 2007-05-23 G. Duplancic , B. Nizic

This paper introduces the continuous tensor abstraction, allowing indices to take real-number values (for example, A[3.14]). It also presents continuous tensor algebra expressions, such as C(x,y) = A(x,y) * B(x,y), where indices are defined…

Programming Languages · Computer Science 2025-10-23 Jaeyeon Won , Willow Ahrens , Teodoro Fields Collin , Joel S. Emer , Saman Amarasinghe
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