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The hypergeometric function method naturally provides the analytic expressions of scalar integrals from concerned Feynman diagrams in some connected regions of independent kinematic variables, also presents the systems of homogeneous linear…

High Energy Physics - Phenomenology · Physics 2018-01-15 Tai-Fu Feng , Chao-Hsi Chang , Jian-Bin Chen , Zhi-Hua Gu , Hai-Bin Zhang

In this talk, we discuss how ideas from geometry help to improve Feynman integral reduction and the construction of $\varepsilon$-factorised differential equations. In particular, we outline a systematic procedure to obtain an…

In these lectures I will give an introduction to Feynman integrals. In the first part of the course I review the basics of the perturbative expansion in quantum field theories. In the second part of the course I will discuss more advanced…

High Energy Physics - Phenomenology · Physics 2010-05-12 Stefan Weinzierl

Many near-term quantum computing algorithms are conceived as variational quantum algorithms, in which parameterized quantum circuits are optimized in a hybrid quantum-classical setup. Examples are variational quantum eigensolvers, quantum…

Quantum Physics · Physics 2022-05-17 Thomas Hubregtsen , Frederik Wilde , Shozab Qasim , Jens Eisert

We embed Feynman integrals in the subvarieties of Grassmannians through homogenization of the integrands in projective space, then obtain GKZ-systems satisfied by those scalar integrals. The Feynman integral can be written as linear…

High Energy Physics - Theory · Physics 2023-01-03 Tai-Fu Feng , Hai-Bin Zhang , Chao-Hsi Chang

This talk reviews Feynman integrals, which are associated to elliptic curves. The talk will give an introduction into the mathematics behind them, covering the topics of elliptic curves, elliptic integrals, modular forms and the moduli…

High Energy Physics - Theory · Physics 2020-12-16 Stefan Weinzierl

We describe a seriation algorithm for ranking a set of items given pairwise comparisons between these items. Intuitively, the algorithm assigns similar rankings to items that compare similarly with all others. It does so by constructing a…

Machine Learning · Computer Science 2016-03-11 Fajwel Fogel , Alexandre d'Aspremont , Milan Vojnovic

It is shown how strictly four-dimensional integration by parts combined with differential renormalization and its infrared analogue can be applied for calculation of Feynman diagrams.

High Energy Physics - Theory · Physics 2009-10-30 V. A. Smirnov

We apply a recently suggested new strategy to solve differential equations for Feynman integrals. We develop this method further by analyzing asymptotic expansions of the integrals. We argue that this allows the systematic application of…

High Energy Physics - Theory · Physics 2015-06-18 Johannes M. Henn , Alexander V. Smirnov , Vladimir A. Smirnov

The integration by parts recurrence relations allow to reduce some Feynman integrals to more simple ones (with some lines missing). Nevertheless the possibility of such reduction for the given particular integral was unclear. The recently…

High Energy Physics - Phenomenology · Physics 2009-10-31 P. A. Baikov

We present a means of formulating and solving graph coloring problems with probabilistic graphical models. In contrast to the prevalent literature that uses factor graphs for this purpose, we instead approach it from a cluster graph…

Machine Learning · Computer Science 2021-10-06 Simon Streicher , Johan du Preez

Efforts to give an improved mathematical meaning to Feynman's path integral formulation of quantum mechanics started soon after its introduction and continue to this day. In the present paper, one common thread of development is followed…

Quantum Physics · Physics 2016-11-23 John R. Klauder

Executing various sequences of system functions in a system under test represents one of the primary techniques in software testing. The natural way to create effective, consistent and efficient test sequences is to model the system under…

Software Engineering · Computer Science 2019-12-05 Miroslav Bures , Bestoun S. Ahmed

The classification of complex data usually requires the composition of processing steps. Here, a major challenge is the selection of optimal algorithms for preprocessing and classification (including parameterizations). Nowadays, parts of…

Machine Learning · Computer Science 2018-01-17 Mario Michael Krell

We study a neural network framework for the numerical evaluation of Feynman loop integrals that are fundamental building blocks for perturbative computations of physical observables in gauge and gravity theories. We show that such a machine…

High Energy Physics - Theory · Physics 2023-12-12 Ryusuke Jinno , Gregor Kälin , Zhengwen Liu , Henrique Rubira

A class of two-bit bit flipping algorithms for decoding low-density parity-check codes over the binary symmetric channel was proposed in [1]. Initial results showed that decoders which employ a group of these algorithms operating in…

Information Theory · Computer Science 2012-05-22 Dung Viet Nguyen , Bane Vasic , Michael W. Marcellin

In this paper we describe a variation of the classical permutation decoding algorithm that can be applied to any affine-invariant code with respect to certain type of information sets. In particular, we can apply it to the family of…

Information Theory · Computer Science 2023-02-13 José Joaquín Bernal , Juan Jacobo Simón

Building on the idea of numerically integrating differential equations satisfied by Feynman integrals, we propose a novel strategy for handling branch cuts within a numerical framework. We develop an integrator capable of evaluating a basis…

High Energy Physics - Phenomenology · Physics 2025-07-18 Pau Petit Rosàs , William J. Torres Bobadilla

We study optimal decision policies for integer linear programs with a fixed feasible set and varying cost vectors, represented as linear decision trees. Once synthesized for a given feasible set, they return an optimal solution for any…

Optimization and Control · Mathematics 2026-05-05 Théo Guyard , Cleber Oliveira , Maximilian Schiffer , Eduardo Uchoa , Thibaut Vidal

In an empirical comparisons of algorithms we might compare run times over a set of benchmark problems to decide which one is fastest, i.e. an algorithmic horse race. Ideally we would like to download source code for the algorithms, compile…

Data Structures and Algorithms · Computer Science 2014-12-11 Frod Prefect , Patrick Prosser