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Algorithms for computing the classical Gaussian quadrature rules (Gauss--Jacobi, Gauss--Laguerre, and Gauss--Hermite) are presented, based on globally convergent fourth-order iterative methods combined with asymptotic approximations, which…

Numerical Analysis · Mathematics 2025-12-15 A. Gil , J. Segura , N. M. Temme

Methods for the computation of classical Gaussian quadrature rules are described which are effective both for small and large degree. These methods are reliable because the iterative computation of the nodes has guaranteed convergence, and…

Numerical Analysis · Mathematics 2019-06-14 A. Gil , J. Segura , N. M. Temme

Asymptotic approximations to the zeros of Jacobi polynomials are given, with methods to obtain the coefficients in the expansions. These approximations can be used as standalone methods for the non-iterative computation of the nodes of…

Numerical Analysis · Mathematics 2019-03-05 Amparo Gil , Javier Segura , Nico M. Temme

A fast and accurate algorithm for the computation of Gauss-Hermite and generalized Gauss-Hermite quadrature nodes and weights is presented. The algorithm is based on Newton's method with carefully selected initial guesses for the nodes and…

Numerical Analysis · Mathematics 2014-10-21 Alex Townsend , Thomas Trogdon , Sheehan Olver

Highly oscillatory integrals of composite type arise in electronic engineering and their calculations is a challenging problem. In this paper, we propose two Gaussian quadrature rules for computing such integrals. The first one is…

Numerical Analysis · Mathematics 2025-04-01 Menghan Wu , Haiyong Wang

We describe a strategy for rigorous arbitrary-precision evaluation of Legendre polynomials on the unit interval and its application in the generation of Gauss-Legendre quadrature rules. Our focus is on making the evaluation practical for a…

Numerical Analysis · Computer Science 2018-10-18 Fredrik Johansson , Marc Mezzarobba

We describe a suite of fast algorithms for evaluating Jacobi polynomials, applying the corresponding discrete Sturm-Liouville eigentransforms and calculating Gauss-Jacobi quadrature rules. Our approach is based on the well-known fact that…

Numerical Analysis · Mathematics 2018-03-13 James Bremer , Haizhao Yang

This paper presents a high-order accurate numerical quadrature algorithm for evaluating integrals over curved surfaces and regions defined implicitly via a level set of a given function restricted to a hyperrectangle. The domain is divided…

Numerical Analysis · Mathematics 2025-06-17 Zibo Zhao

A novel development is given of the theory of Gaussian quadrature, not relying on the theory of orthogonal polynomials. A method is given for computing the nodes and weights that is manifestly independent of choice of basis in the space of…

Numerical Analysis · Mathematics 2007-05-23 Ilan Degani , Jeremy Schiff

The aim of this paper is to develop novel quantum algorithms for Gaussian process quadrature methods. Gaussian process quadratures are numerical integration methods where Gaussian processes are used as functional priors for the integrands…

Computation · Statistics 2025-02-21 Cristian A. Galvis-Florez , Ahmad Farooq , Simo Särkkä

The discretization of velocity space plays a crucial role in the accuracy and efficiency of multiscale Boltzmann solvers. Conventional velocity space discretization methods suffer from uneven node distribution and mismatch issues, limiting…

Numerical Analysis · Mathematics 2025-11-04 Shanshan Dong , Lu Wang , Xiangxiang Chen , Guanqing Wang

A method of deriving quadrature rules has been developed which gives nodes and weights for a Gaussian-type rule which integrates functions of the form: f(x,y,t) = a(x,y,t)/((x-t)^2+y^2) + b(x,y,t)/([(x-t)^2+y^2]^{1/2}) +…

Numerical Analysis · Mathematics 2010-09-21 Michael Carley

The two most commonly used hypergeometric functions are the confluent hypergeometric function and the Gauss hypergeometric function. We review the available techniques for accurate, fast, and reliable computation of these two hypergeometric…

Numerical Analysis · Mathematics 2015-08-31 John W. Pearson , Sheehan Olver , Mason A. Porter

We present a simple yet powerful and applicable quadrature based scheme for constructing optimal iterative methods. According to the, still unproved, Kung-Traub conjecture an optimal iterative method based on $n+1$ evaluations could achieve…

Numerical Analysis · Mathematics 2010-04-20 Sanjay K. Khattri , Ravi P. Agarwal

In this paper, we introduce a novel theoretical framework for Gaussian process regression error analysis, leveraging a function-space decomposition. Based on this framework, we develop a weighted Jacobi iterative method that utilizes…

Numerical Analysis · Mathematics 2026-02-27 Tiantian Sun , Juan Zhang

We present a framework for accelerating a spectrum of machine learning algorithms that require computation of bilinear inverse forms $u^\top A^{-1}u$, where $A$ is a positive definite matrix and $u$ a given vector. Our framework is built on…

Machine Learning · Statistics 2016-05-31 Chengtao Li , Suvrit Sra , Stefanie Jegelka

This paper deals with the computation of the Lerch transcendent by means of the Gauss-Laguerre formula. An a priori estimate of the quadrature error, that allows to compute the number of quadrature nodes necessary to achieve an arbitrary…

Numerical Analysis · Mathematics 2023-02-24 Eleonora Denich , Paolo Novati

Gaussian Quadrature is a well known technique for numerical integration. Recently Gaussian quadrature with respect to discrete measures corresponding to finite sums have found some new interest. In this paper we apply these ideas to…

Numerical Analysis · Mathematics 2007-05-23 Hartmut Monien

Asymptotic approximations of Jacobi polynomials are given in terms of elementary functions for large degree $n$ and parameters $\alpha$ and $\beta$. From these new results, asymptotic expansions of the zeros are derived and methods are…

Classical Analysis and ODEs · Mathematics 2020-07-22 Amparo Gil , Javier Segura , Nico M. Temme

Non-conjugate Gaussian processes (NCGPs) define a flexible probabilistic framework to model categorical, ordinal and continuous data, and are widely used in practice. However, exact inference in NCGPs is prohibitively expensive for large…

Machine Learning · Computer Science 2025-04-18 Lukas Tatzel , Jonathan Wenger , Frank Schneider , Philipp Hennig
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