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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

In order to approximate the Riemann--Stieltjes integral $\int_a^b {f\left( t \right)dg\left( t \right)}$ by $2$--point Gaussian quadrature rule, we introduce the quadrature rule \begin{align*} \int_{ - 1}^1 {f\left( t \right)dg\left( t…

Classical Analysis and ODEs · Mathematics 2014-02-21 Mohammad W. Alomari

The purpose of this paper is to develop the anti-Gauss cubature rule for approximating integrals defined on the square whose integrand function may have algebraic singularities at the boundaries. An application of such a rule to the…

Numerical Analysis · Mathematics 2025-05-08 Patricia Diaz de Alba , Luisa Fermo , Giuseppe Rodriguez

We introduce the novel method for estimation of mean and Gaussian curvature and several related quantities for polygonal meshes. The algebraic quadric fitting curvature (AQFC) is based on local approximation of the mesh vertices and…

Differential Geometry · Mathematics 2023-04-26 Marcel Makovník , Pavel Chalmoviansky

We provide explicit expressions for quadrature rules on the space of $C^1$ quintic splines with uniform knot sequences over finite domains. The quadrature nodes and weights are derived via an explicit recursion that avoids an intervention…

Numerical Analysis · Mathematics 2015-03-04 Michael Bartoň , Rachid Ait-Haddou , Victor Manuel Calo

We discuss the advantages of using the approximate quantum Fourier transform (AQFT) in algorithms which involve periodicity estimations. We analyse quantum networks performing AQFT in the presence of decoherence and show that extensive…

Quantum Physics · Physics 2009-10-30 Adriano Barenco , Artur Ekert , Kalle-Antti Suominen , Päivi Törmä

Approximate Counting refers to the problem where we are given query access to a function $f : [N] \to \{0,1\}$, and we wish to estimate $K = #\{x : f(x) = 1\}$ to within a factor of $1+\epsilon$ (with high probability), while minimizing the…

Quantum Physics · Physics 2020-10-12 Ramgopal Venkateswaran , Ryan O'Donnell

We introduce Gaussian quadrature rules for spline spaces that are frequently used in Galerkin discretizations to build mass and stiffness matrices. By definition, these spaces are of even degrees. The optimal quadrature rules we recently…

Numerical Analysis · Mathematics 2016-02-04 Michael Bartoň , Victor Manuel Calo

We investigate the numerical approximation of integrals over $\mathbb{R}^d$ equipped with the standard Gaussian measure $\gamma$ for integrands belonging to the Gaussian-weighted Sobolev spaces $W^\alpha_p(\mathbb{R}^d, \gamma)$ of mixed…

Numerical Analysis · Mathematics 2023-06-21 Dinh Dũng , Van Kien Nguyen

In this contribution we deal with Gaussian quadrature rules based on orthogonal polynomials associated with a weight function $w(x)= x^{\alpha} e^{-x}$ supported on an interval $(0,z)$, $z>0.$ The modified Chebyshev algorithm is used in…

Numerical Analysis · Mathematics 2024-01-05 Juan C. García-Ardila , Francisco Marcellán

Some Gauss-type quadrature rules over [0, 1], which involve values and/or the derivative of the integrand at 0 and/or 1, are investigated

Numerical Analysis · Mathematics 2009-05-12 M. A. Bokhari , Asghar Qadir

Calabro et al. (2017) changed the paradigm of the mass and stiffness computation from the traditional element-wise assembly to a row-wise concept, showing that the latter one offers integration that may be orders of magnitude faster.…

Numerical Analysis · Mathematics 2017-10-04 Michael Bartoň , Vladimir Puzyrev , Quanling Deng , Victor Calo

Convex analysis and Gaussian probability are tightly connected, as mostly evident in the theory of linear regression. Our work introduces an algebraic perspective on such relationship, in the form of a diagrammatic calculus of string…

Logic in Computer Science · Computer Science 2025-09-04 Dario Stein , Fabio Zanasi , Robin Piedeleu , Richard Samuelson

In this paper, we develop a quadrature framework for large-scale kernel machines via a numerical integration representation. Considering that the integration domain and measure of typical kernels, e.g., Gaussian kernels, arc-cosine kernels,…

Machine Learning · Computer Science 2021-06-14 Fanghui Liu , Xiaolin Huang , Yudong Chen , Johan A. K. Suykens

We consider the approximation of the inverse square root of regularly accretive operators in Hilbert spaces. The approximation is of rational type and comes from the use of the Gauss-Legendre rule applied to a special integral formulation…

Numerical Analysis · Mathematics 2022-02-04 Eleonora Denich , Paolo Novati

A new method for approximating fractional derivatives of the Gaussian function and Dawson's integral are presented. Unlike previous approaches, which are dominantly based on some discretization of Riemann-Liouville integral using polynomial…

Numerical Analysis · Mathematics 2017-09-08 Can Evren Yarman

A new type of quadrature is developed. The Gaussian quadrature, for a given measure, finds optimal values of a function's argument (nodes) and the corresponding weights. In contrast, the Lebesgue quadrature developed in this paper, finds…

Numerical Analysis · Mathematics 2020-02-25 Vladislav Gennadievich Malyshkin

This paper deals with the estimation of the quadrature error of a Gaussian formula for weight functions involving fractional powers, exponentials and Bessel functions of the first kind. For this purpose, in this work the averaged and…

Numerical Analysis · Mathematics 2023-01-30 Eleonora Denich

An important question in the theory of approximate integration is to study the conditions on the nodes $x_{k,n}$ and weights $w_{k,n}$ that allow an estimate of the form $$ \sup_{f\in \mathcal{B}_\gamma}|\sum_k…

Numerical Analysis · Mathematics 2017-09-06 Hrushikesh N. Mhaskar

Approximation algorithms are widely used in many engineering problems. To obtain a data set for approximation a factorial design of experiments is often used. In such case the size of the data set can be very large. Therefore, one of the…

Methodology · Statistics 2014-07-04 Mikhail Belyaev , Evgeny Burnaev , Yermek Kapushev