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In this note we analyze the Caffarelli-Silvestre extension function using tools from the theory of stochastic analysis applied to Dirichlet problems. We use a stochastic approach to give the explicit formulation of the kernel associated to…

Probability · Mathematics 2023-10-03 Michelangelo Cavina

We establish sharp algebraic criteria for the $L^{p}$-integrability, for $p = 1, 2, \infty$, of a natural generalization of the Siegel transform to the setting of rational representations of semisimple algebraic $\mathbb{Q}$-groups,…

Number Theory · Mathematics 2025-11-20 René Pfitscher

Convergence rates for $L_2$ approximation in a Hilbert space $H$ are a central theme in numerical analysis. The present work is inspired by Schaback (Math. Comp., 1999), who showed, in the context of best pointwise approximation for radial…

Numerical Analysis · Mathematics 2024-10-01 Ian H. Sloan , Vesa Kaarnioja

In this note we reprove the Lipschitz stability for the inverse problem for the Schr\"odinger operator with finite-dimensional potentials by using quantitative Runge approximation results. This provides a quantification of the Schr\"odinger…

Analysis of PDEs · Mathematics 2020-02-24 Angkana Rüland , Eva Sincich

In this paper we obtain degree of approximation of functions in Lp by operators associated with their Fourier series using integral modulus of continuity. These results generalize many know results and are proved under less stringent…

Classical Analysis and ODEs · Mathematics 2012-05-29 R. N. Mohapatra , B. Szal

Consider semiparametric estimation where a doubly robust estimating function for a low-dimensional parameter is available, depending on two working models. With high-dimensional data, we develop regularized calibrated estimation as a…

Methodology · Statistics 2020-09-28 Satyajit Ghosh , Zhiqiang Tan

We prove $L^p$ quantitative differentiability estimates for functions defined on uniformly rectifiable subsets of the Euclidean space. More precisely, we show that a Dorronsoro-type theorem holds in this context: the $L^p$ norm of the…

Classical Analysis and ODEs · Mathematics 2025-11-14 Jonas Azzam , Mihalis Mourgoglou , Michele Villa

It is well known that both gradient descent and stochastic coordinate descent achieve a global convergence rate of $O(1/k)$ in the objective value, when applied to a scheme for minimizing a Lipschitz-continuously differentiable,…

Optimization and Control · Mathematics 2019-05-15 Ching-pei Lee , Stephen J. Wright

For a set $\mathbb{W} \subset L_p(\bT^d)$, $1 < p < \infty$, of multivariate periodic functions on the torus $\bT^d$ and a given function $\varphi \in L_p(\bT^d)$, we study the approximation in the $L_p(\bT^d)$-norm of functions $f \in…

Functional Analysis · Mathematics 2013-04-26 Dinh Dung , Charles Micchelli

We study multivariate integration and approximation for functions belonging to a weighted reproducing kernel Hilbert space based on half-period cosine functions in the worst-case setting. The weights in the norm of the function space depend…

Numerical Analysis · Mathematics 2015-11-23 Christian Irrgeher , Peter Kritzer , Friedrich Pillichshammer

We report on some extensive computations and experiments concerning the moments of quadratic Dirichlet $L$-functions at the critical point. We computed the values of $L(1/2,\chi_d)$ for $- 5\times 10^{10} < d < 1.3 \times 10^{10}$ in order…

Number Theory · Mathematics 2012-03-02 Matthew W. Alderson , Michael O. Rubinstein

This paper concentrates on the approximation power of deep feed-forward neural networks in terms of width and depth. It is proved by construction that ReLU networks with width $\mathcal{O}\big(\max\{d\lfloor N^{1/d}\rfloor,\, N+2\}\big)$…

Machine Learning · Computer Science 2021-12-15 Zuowei Shen , Haizhao Yang , Shijun Zhang

We consider linear approximation based on function evaluations in reproducing kernel Hilbert spaces of certain analytic weighted power series kernels and stationary kernels on the interval $[-1,1]$. Both classes contain the popular Gaussian…

Numerical Analysis · Mathematics 2025-10-03 Toni Karvonen , Yuya Suzuki

The celebrated universal approximation theorems for neural networks roughly state that any reasonable function can be arbitrarily well-approximated by a network whose parameters are appropriately chosen real numbers. This paper examines the…

Machine Learning · Computer Science 2023-03-17 C. Sinan Güntürk , Weilin Li

We consider fractional Schr\"odinger operators with possibly singular potentials and derive certain spatially averaged estimates for its complex-time heat kernel. The main tool is a Phragm\'en-Lindel\"of theorem for polynomially bounded…

Analysis of PDEs · Mathematics 2022-07-13 Konstantin Merz

We consider frames F in a given Hilbert space, and we show that every F may be obtained in a constructive way from a reproducing kernel and an orthonormal basis in an ambient Hilbert space. The construction is operator-theoretic, building…

Classical Analysis and ODEs · Mathematics 2007-05-23 Palle E. T. Jorgensen

New upper bounds on the pointwise behaviour of Christoffel function on convex domains in ${\mathbb{R}}^d$ are obtained. These estimates are established by explicitly constructing the corresponding "needle"-like algebraic polynomials having…

Classical Analysis and ODEs · Mathematics 2017-07-26 A. Prymak

The purpose of this paper is to extend the result of arXiv:1810.00823 to mixed H\"older functions on $[0,1]^d$ for all $d \ge 1$. In particular, we prove that by sampling an $\alpha$-mixed H\"older function $f : [0,1]^d \rightarrow…

Classical Analysis and ODEs · Mathematics 2019-10-11 Nicholas F. Marshall

The celebrated L\'evy--Khintchine theorem is a fundamental limiting law that describes the growth rate of the denominators of the convergents in the continued fraction expansion of a Lebesgue-typical real number. In a recent breakthrough,…

Number Theory · Mathematics 2025-08-04 Gaurav Aggarwal , Anish Ghosh

Hilbertian kernel methods and their positive semidefinite kernels have been extensively used in various fields of applied mathematics and machine learning, owing to their several equivalent characterizations. We here unveil an analogy with…

Functional Analysis · Mathematics 2023-01-10 Pierre-Cyril Aubin-Frankowski , Stéphane Gaubert
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