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We are concerned with the numerical integration of functions from the Sobolev space $H^{r,\text{mix}}([0,1]^d)$ of dominating mixed smoothness $r\in\mathbb{N}$ over the $d$-dimensional unit cube. In 1976, K. K. Frolov introduced a…

Numerical Analysis · Mathematics 2016-03-17 David Krieg

In this paper, we consider the infinite-dimensional integration problem on weighted reproducing kernel Hilbert spaces with norms induced by an underlying function space decomposition of ANOVA-type. The weights model the relative importance…

Numerical Analysis · Mathematics 2021-09-21 Jan Baldeaux , Michael Gnewuch

We present a simple universal algorithm for high-dimensional integration which has the optimal error rate (independent of the dimension) in all weighted Korobov classes both in the randomized and the deterministic setting. Our theoretical…

Numerical Analysis · Mathematics 2024-12-02 Takashi Goda , David Krieg

We study high dimensional integration in the quantum model of computation. We develop quantum algorithms for integration of functions from Sobolev classes $W^r_p([0,1]^d)$ and analyze their convergence rates. We also prove lower bounds…

Quantum Physics · Physics 2007-05-23 Stefan Heinrich

In a recent article by two of the present authors it turned out that Frolov's cubature formulae are optimal and universal for various settings (Besov-Triebel-Lizorkin spaces) of functions with dominating mixed smoothness. Those cubature…

Numerical Analysis · Mathematics 2019-08-15 Van Kien Nguyen , Mario Ullrich , Tino Ullrich

The worst case integration error in reproducing kernel Hilbert spaces of standard Monte Carlo methods with n random points decays as $n^{-1/2}$. However, re-weighting of random points can sometimes be used to improve the convergence order.…

Numerical Analysis · Mathematics 2018-01-26 Martin Ehler , Manuel Graef , Chris. J. Oates

We introduce a novel random integration algorithm that boasts both high convergence order and polynomial tractability for functions characterized by sparse frequencies or rapidly decaying Fourier coefficients. Specifically, for integration…

Numerical Analysis · Mathematics 2025-12-30 Liang Chen , Minqiang Xu , Haizhang Zhang

Consider the numerical integration $${\rm Int}_{\mathbb S^d,w}(f)=\int_{\mathbb S^d}f({\bf x})w({\bf x}){\rm d}\sigma({\bf x}) $$ for weighted Sobolev classes $BW_{p,w}^r(\mathbb S^d)$ with a Dunkl weight $w$ and weighted Besov classes…

Numerical Analysis · Mathematics 2024-12-24 Jiansong Li , Heping Wang

We study the numerical integration problem for functions with infinitely many variables. The function spaces of integrands we consider are weighted reproducing kernel Hilbert spaces with norms related to the ANOVA decomposition of the…

Numerical Analysis · Mathematics 2021-09-21 Josef Dick , Michael Gnewuch

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 investigate the approximation of weighted integrals over $\mathbb{R}^d$ for integrands from weighted Sobolev spaces of mixed smoothness. We prove upper and lower bounds of the convergence rate of optimal quadratures with respect to $n$…

Numerical Analysis · Mathematics 2023-05-01 Dinh Dũng

We analyze a new random algorithm for numerical integration of $d$-variate functions over $[0,1]^d$ from a weighted Sobolev space with dominating mixed smoothness $\alpha\ge 0$ and product weights $1\ge\gamma_1\ge\gamma_2\ge\cdots>0$, where…

Numerical Analysis · Mathematics 2019-08-15 Peter Kritzer , Frances Y. Kuo , Dirk Nuyens , Mario Ullrich

In this paper we propose a fast algorithm for trivariate interpolation, which is based on the partition of unity method for constructing a global interpolant by blending local radial basis function interpolants and using locally supported…

Numerical Analysis · Mathematics 2015-10-20 Roberto Cavoretto , Alessandra De Rossi

Akin to the idea of complete sets of Mutually Unbiased Bases for prime dimensional Hilbert spaces, $\mathcal{H}_d$, we study its analogue for a $d$ dimensional subspace of $M (d,\mathbb{C})$, i.e. Mutually Unbiased Unitary Bases (MUUBs)…

Quantum Physics · Physics 2019-06-11 Rinie N. M. Nasir , Jesni Shamsul Shaari , Stefano Mancini

The \emph{deterministic} sparse grid method, also known as Smolyak's method, is a well-established and widely used tool to tackle multivariate approximation problems, and there is a vast literature on it. Much less is known about…

Numerical Analysis · Mathematics 2022-02-11 Marcin Wnuk , Michael Gnewuch

We study numerical integration of functions depending on an infinite number of variables. We provide lower error bounds for general deterministic linear algorithms and provide matching upper error bounds with the help of suitable multilevel…

Numerical Analysis · Mathematics 2021-02-09 Josef Dick , Michael Gnewuch

We prove an optimal order error bound in the discrete $H^2(\Omega)$ norm for finite difference approximations of the first boundary-value problem for the biharmonic equation in $n$ space dimensions, with $n \in \{2,\dots,7\}$, whose…

Numerical Analysis · Mathematics 2019-04-04 Stefan Müller , Florian Schweiger , Endre Süli

This paper deals with the construction of an optimal quadrature formula for the approximation of Fourier integrals in the Sobolev space $L_2^{(1)}[a,b]$ of non-periodic, complex valued functions which are square integrable with first order…

Numerical Analysis · Mathematics 2019-07-31 Abdullo R. Hayotov , Soomin Jeon , Chang-Ock Lee

We consider the numerical integration $${\rm INT}_d(f)=\int_{\mathbb{B}^{d}}f(x)w_\mu(x)dx $$ for the weighted Sobolev classes $BW^{r}_{p,\mu}$ and the weighted Besov classes $BB_\tau^r(L_{p,\mu})$ in the randomized case setting, where…

Numerical Analysis · Mathematics 2022-01-19 Jiansong Li , Heping Wang

We present a hybrid algorithm for optimizing a convex, smooth function over the cone of positive semidefinite matrices. Our algorithm converges to the global optimal solution and can be used to solve general large-scale semidefinite…

Machine Learning · Computer Science 2012-06-22 Soeren Laue
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