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Related papers: Reconstruction of rational functions made simple

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Finite Rate of Innovation (FRI) sampling theory enables reconstruction of classes of continuous non-bandlimited signals that have a small number of free parameters from their low-rate discrete samples. This task is often translated into a…

Signal Processing · Electrical Eng. & Systems 2023-07-21 Vincent C. H. Leung , Jun-Jie Huang , Yonina C. Eldar , Pier Luigi Dragotti

We propose a new reconstruction operator that aims to recover the missing parts of a function given the observed parts. This new operator belongs to a new, very large class of functional operators which includes the classical regression…

Statistics Theory · Mathematics 2019-05-14 Alois Kneip , Dominik Liebl

In this paper a sublinear time algorithm is presented for the reconstruction of functions that can be represented by just few out of a potentially large candidate set of Fourier basis functions in high spatial dimensions, a so-called…

Numerical Analysis · Mathematics 2020-06-24 Lutz Kämmerer , Felix Krahmer , Toni Volkmer

Several construction methods for rational approximations to functions of one real variable are described in the present paper; the computational results that characterize the comparative accuracy of these methods are presented; an effect of…

Numerical Analysis · Mathematics 2025-10-20 Grigori Litvinov

We introduce SP$\mathbb{Q}$R, a new Mathematica package for the division and elimination of variables from polynomial systems. SP$\mathbb{Q}$R works by sampling and reconstructing results over finite fields, in an analogous manner to many…

High Energy Physics - Theory · Physics 2025-11-20 Vsevolod Chestnov , Giulio Crisanti

Theoretical inverse problems are often studied in an ideal infinite-dimensional setting. The well-posedness theory provides a unique reconstruction of the parameter function, when an infinite amount of data is given. Through the lens of…

Numerical Analysis · Mathematics 2025-10-13 Ruhui Jin , Qin Li , Anjali Nair , Samuel Stechmann

We study the problem of stable reconstruction of the short-time Fourier transform from samples taken from trajectories in $\R^2$. We first consider the interplay between relative density of the trajectory and the reconstruction property.…

Functional Analysis · Mathematics 2021-12-06 Michael Speckbacher

We describe three algorithms for computer-aided symbolic multi-loop calculations that facilitated some recent novel results. First, we discuss an algorithm to derive the canonical form of an arbitrary Feynman integral in order to facilitate…

High Energy Physics - Phenomenology · Physics 2015-06-03 Alexey Pak

We present two new methods for multivariate exponential analysis. In [7], we developed a new algorithm for reconstruction of univariate exponential sums by exploiting the rational structure of their Fourier coefficients and reconstructing…

Numerical Analysis · Mathematics 2025-04-29 Nadiia Derevianko , Lennart Aljoscha Hübner

The free energy of a field theory can be considered as a functional of the free correlation function. As such it obeys a nonlinear functional differential equation which can be turned into a recursion relation. This is solved order by order…

High Energy Physics - Theory · Physics 2009-10-31 Hagen Kleinert , Axel Pelster , Boris Kastening , M. Bachmann

This article presents a novel, general, and effective simulation-inspired approach, called {\it repro samples method}, to conduct statistical inference. The approach studies the performance of artificial samples, referred to as {\it repro…

Methodology · Statistics 2022-06-15 Min-ge Xie , Peng Wang

Weighted average sampling is more practical and numerically more stable than sampling at single points as in the classical Shannon sampling framework. Using the frame theory, one can completely reconstruct a bandlimited function from its…

Information Theory · Computer Science 2014-07-04 Haizhang Zhang

A Krylov subspace recycling method for the efficient evaluation of a sequence of matrix functions acting on a set of vectors is developed. The method improves over the recycling methods presented in [Burke et al., arXiv:2209.14163, 2022] in…

Numerical Analysis · Mathematics 2023-08-23 Liam Burke , Stefan Güttel

Sampling from distributions of implicitly defined shapes enables analysis of various energy functionals used for image segmentation. Recent work describes a computationally efficient Metropolis-Hastings method for accomplishing this task.…

Computer Vision and Pattern Recognition · Computer Science 2012-05-17 Jason Chang , John W. Fisher

We introduce a machine-learning framework based on symbolic regression to extract the full symbol alphabet of multi-loop Feynman integrals. By targeting the analytic structure rather than reduction, the method is broadly applicable and…

High Energy Physics - Phenomenology · Physics 2025-10-28 Yuanche Liu , Yingxuan Xu , Yang Zhang

Recently we developed a new sampling methodology based on incomplete cosine expansion of the sinc function and applied it in numerical integration in order to obtain a rational approximation for the complex error function $w\left(z \right)…

Numerical Analysis · Mathematics 2019-03-08 S. M. Abrarov , B. M. Quine , R. K. Jagpal

In this paper, we first establish an evaluation formula to calculate Wiener integrals of functionals on Wiener space. We then apply our evaluation formula to carry out very easily calculating for the analytic Fourier-Feynman transform of…

Functional Analysis · Mathematics 2020-01-01 Hyun Soo Chung

This paper presents novel single and multi-shell sampling schemes for diffusion MRI. In diffusion MRI, it is paramount that the number of samples is as small as possible in order that scan times are practical in a clinical setting. The…

Signal Processing · Electrical Eng. & Systems 2019-02-01 Alice P. Bates , Zubair Khalid , Jason D. McEwen , Rodney A. Kennedy , Alessandro Daducci , Erick J. Canales-Rodríguez

We propose a functional view of matrix decomposition problems on graphs such as geometric matrix completion and graph regularized dimensionality reduction. Our unifying framework is based on the key idea that using a reduced basis to…

Machine Learning · Computer Science 2021-02-08 Abhishek Sharma , Maks Ovsjanikov

We generalize Newton-type methods for minimizing smooth functions to handle a sum of two convex functions: a smooth function and a nonsmooth function with a simple proximal mapping. We show that the resulting proximal Newton-type methods…

Machine Learning · Statistics 2014-03-19 Jason D. Lee , Yuekai Sun , Michael A. Saunders
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