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We propose a more accurate variant of an algorithm for multiplying 4x4 matrices using 48 multiplications over any ring containing an inverse of 2. This algorithm has an error bound exponent of only log 4 $\gamma$$\infty$,2 $\approx$ 2.386.…

Data Structures and Algorithms · Computer Science 2026-03-20 Jean-Guillaume Dumas , Clément Pernet , Alexandre Sedoglavic

It has recently been emphasized that all known exact evaluations of gap probabilities for classical unitary matrix ensembles are in fact $\tau$-functions for certain Painlev\'e systems. We show that all exact evaluations of gap…

Mathematical Physics · Physics 2009-11-07 P. J. Forrester , N. S. Witte

I make a rough estimate of the accuracy threshold for fault tolerant quantum computing with concatenated codes. First I consider only gate errors and use the depolarizing channel error model. I will follow P.Shor (quant-ph/9505011) for…

Quantum Physics · Physics 2008-02-03 Christof Zalka

Unlike polynomials, rational functions can represent functions having poles or branch cuts with root-exponential convergence and no Runge phenomenon. Recent developments of the AAA and greedy Thiele algorithms have sparked renewed interest…

Numerical Analysis · Mathematics 2025-12-09 Tobin A. Driscoll

The Condat-V\~u algorithm is a widely used primal-dual method for optimizing composite objectives of three functions. Several algorithms for optimizing composite objectives of two functions are special cases of Condat-V\~u, including…

Optimization and Control · Mathematics 2024-03-27 Derek Driggs , Matthias J. Ehrhardt , Carola-Bibiane Schönlieb , Junqi Tang

Niederreiter [H.Niederreiter, Error bounds for quasi-Monte Carlo integration with uniform point sets, Journal of computational and applied mathematics 150 (2003), 283-292] established new bounds for quasi-Monte Carlo integration for nodes…

Number Theory · Mathematics 2010-12-01 Su Hu , Yan Li

Modern particle physics is increasingly becoming a precision science that relies on advanced theoretical predictions for the analysis and interpretation of experimental results. The planned physics program at the LHC and future colliders…

High Energy Physics - Phenomenology · Physics 2023-01-04 Ievgen Dubovyk , Ayres Freitas , Janusz Gluza , Krzysztof Grzanka , Martijn Hidding , Johann Usovitsch

Inverse problems are prevalent in numerous scientific and engineering disciplines, where the objective is to determine unknown parameters within a physical system using indirect measurements or observations. The inherent challenge lies in…

Computational Physics · Physics 2025-02-06 Georgios E. Pavlou , Vasiliki Pavlidou , Vagelis Harmandaris

Feynman loop integrals are a key ingredient for the calculation of higher order radiation effects, and are responsible for reliable and accurate theoretical prediction. We improve the efficiency of numerical integration in sector…

High Energy Physics - Phenomenology · Physics 2016-01-12 Zhao Li , Jian Wang , Qi-Shu Yan , Xiaoran Zhao

We consider approximation of diameter of a set $S$ of $n$ points in dimension $m$. E$\tilde{g}$ecio$\tilde{g}$lu and Kalantari \cite{kal} have shown that given any $p \in S$, by computing its farthest in $S$, say $q$, and in turn the…

Computational Geometry · Computer Science 2014-10-09 Sharareh Alipour , Bahman Kalantari , Hamid Homapour

This paper addresses a quadratic problem with assignment constraints, an NP-hard combinatorial optimization problem arisen from facility location, multiple-input multiple-output detection, and maximum mean discrepancy calculation et al. The…

Optimization and Control · Mathematics 2025-12-15 Lijun Xie , Ran Gu , Xin Liu

Polynomial representations of Boolean functions over various rings such as $\mathbb{Z}$ and $\mathbb{Z}_m$ have been studied since Minsky and Papert (1969). From then on, they have been employed in a large variety of fields including…

Computational Complexity · Computer Science 2020-05-04 Xiaoming Sun , Yuan Sun , Jiaheng Wang , Kewen Wu , Zhiyu Xia , Yufan Zheng

We present a double precision routine in Fortran for the precise and fast numerical evaluation of the two Master Integrals (MIs) of the equal mass two-loop sunrise graph for arbitrary momentum transfer in d=2 and d=4 dimensions. The routine…

High Energy Physics - Phenomenology · Physics 2009-11-11 S. Pozzorini , E. Remiddi

We show that for any given norm ball or proper cone, weak membership in its dual ball or dual cone is polynomial-time reducible to weak membership in the given ball or cone. A consequence is that the weak membership or membership problem…

Optimization and Control · Mathematics 2016-07-26 Shmuel Friedland , Lek-Heng Lim

In this note, we study a concatenation of quasi-Monte Carlo and plain Monte Carlo rules for high-dimensional numerical integration in weighted function spaces. In particular, we consider approximating the integral of periodic functions…

Numerical Analysis · Mathematics 2022-06-27 Takashi Goda

We study a Monte Carlo algorithm that is based on a specific (randomly shifted and dilated) lattice point set. The main result of this paper is that the mean squared error for a given compactly supported, square-integrable function is…

Numerical Analysis · Mathematics 2017-06-22 Mario Ullrich

We report an essential improvement of the plain Fourier Monte Carlo algorithm that promises to be a powerful tool for investigating critical behavior in a large class of lattice models, in particular those containing microscopic or…

Statistical Mechanics · Physics 2015-02-17 Andreas Tröster

Numerical reconstruction techniques are widely employed in the calculation of multi-loop scattering amplitudes. In recent years, it has been observed that the rational functions in multi-loop calculations greatly simplify under partial…

High Energy Physics - Phenomenology · Physics 2024-09-19 Herschel A. Chawdhry

We consider the problem of efficiently computing isolated coefficients $c_n$ in the Fourier series of the elliptic modular function $j(\tau)$. We show that a hybrid numerical-modular method with complexity $n^{1+o(1)}$ is efficient in…

Number Theory · Mathematics 2020-12-01 Fredrik Johansson

This paper presents Dual Lagrangian Learning (DLL), a principled learning methodology for dual conic optimization proxies. DLL leverages conic duality and the representation power of ML models to provide high-duality, dual-feasible…

Optimization and Control · Mathematics 2024-05-27 Mathieu Tanneau , Pascal Van Hentenryck