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In this paper, we derive rates of convergence in the high-dimensional central limit theorem for Polyak--Ruppert averaged iterates generated by entropy-regularized asynchronous Q-learning with linear function approximation and a polynomial…

Machine Learning · Statistics 2026-05-19 Artemy Rubtsov , Rahul Singh , Eric Moulines , Alexey Naumov , Sergey Samsonov

We provide a priori error estimates for variational approximations of the ground state eigenvalue and eigenvector of nonlinear elliptic eigenvalue problems of the form $-{div} (A\nabla u) + Vu + f(u^2) u = \lambda u$, $\|u\|_{L^2}=1$. We…

Numerical Analysis · Mathematics 2009-06-05 Eric Cancès , Rachida Chakir , Yvon Maday

This paper investigates the mathematical properties and numerical approximation of a class of nonlocal elliptic partial differential equations of the form \begin{equation*} -\Delta u + \lambda \, G(u) = f, \end{equation*} where $\Delta$…

Analysis of PDEs · Mathematics 2026-02-09 Dragos-Patru Covei

The article presents, in an elementary way, but with mathematical precision and without harm to the intuition, the path from the integral representation to the Dirac delta, starting with Schwartz's functional approach. Next, the considered…

History and Overview · Mathematics 2025-08-26 Grzegorz M. Koczan , Piotr Stachura

The computational complexity of some depths that satisfy the projection property, such as the halfspace depth or the projection depth, is known to be high, especially for data of higher dimensionality. In such scenarios, the exact depth is…

Statistics Theory · Mathematics 2021-05-28 Stanislav Nagy , Rainer Dyckerhoff , Pavlo Mozharovskyi

We consider the numerical approximation of parabolic-elliptic interface problems by the non-symmetric coupling method of MacCamy and Suri [Quart. Appl. Math., 44 (1987), pp. 675--690]. We establish well-posedness of this formulation for…

Numerical Analysis · Mathematics 2017-11-27 Herbert Egger , Christoph Erath , Robert Schorr

We generalize the Brezzi-Rappaz-Raviart approximation theorem, which allows to obtain existence and a priori error estimates for approximations of solutions to some nonlinear partial differential equations. Our contribution lies in the fact…

Numerical Analysis · Mathematics 2026-05-08 Jules Berry , Olivier Ley , Francisco José Silva

In this paper we establish the convergence of a numerical scheme based, on the Finite Element Method, for a time-independent problem modelling the deformation of a linearly elastic elliptic membrane shell subjected to remaining confined in…

Analysis of PDEs · Mathematics 2023-10-25 Aaron Meixner , Paolo Piersanti

Velocity fields with low regularity (below the Lipschitz threshold) naturally arise in many models from mathematical physics, such as the inhomogeneous incompressible Navier-Stokes equations, and play a fundamental role in the analysis of…

Analysis of PDEs · Mathematics 2025-06-04 Gennaro Ciampa , Tommaso Cortopassi , Gianluca Crippa , Raffaele D'Ambrosio , Stefano Spirito

We derive in this note a high-order corrector estimate for the homogenization of a microscopic semi-linear elliptic system posed in perforated domains. The major challenges are the presence of nonlinear volume and surface reaction rates.…

Analysis of PDEs · Mathematics 2017-05-24 Vo Anh Khoa

In this paper, we develop a unified framework able to certify both exponential and subexponential convergence rates for a wide range of iterative first-order optimization algorithms. To this end, we construct a family of parameter-dependent…

Optimization and Control · Mathematics 2018-02-26 Mahyar Fazlyab , Alejandro Ribeiro , Manfred Morari , Victor M. Preciado

In this work is considered an elliptic problem, referred to as the Ventcel problem, involvinga second order term on the domain boundary (the Laplace-Beltrami operator). A variationalformulation of the Ventcel problem is studied, leading to…

Numerical Analysis · Mathematics 2024-11-08 Fabien Caubet , Joyce Ghantous , Charles Pierre

Ordinary differential equations (ODEs) are commonly used to model dynamic behavior of a system. Because many parameters are unknown and have to be estimated from the observed data, there is growing interest in statistics to develop…

Statistics Theory · Mathematics 2010-01-13 Xin Qi , Hongyu Zhao

The discretization of optimal transport problems often leads to large linear programs with sparse solutions. We derive error estimates for the approximation of the problem using convex combinations of Dirac measures and devise an active-set…

Numerical Analysis · Mathematics 2017-10-16 Sören Bartels , Stephan Hertzog

This paper is concerned with the adaptive numerical treatment of stochastic partial differential equations. Our method of choice is Rothe's method. We use the implicit Euler scheme for the time discretization. Consequently, in each step, an…

Incremental gradient and incremental proximal methods are a fundamental class of optimization algorithms used for solving finite sum problems, broadly studied in the literature. Yet, without strong convexity, their convergence guarantees…

Optimization and Control · Mathematics 2024-07-01 Xufeng Cai , Jelena Diakonikolas

In this paper, we consider stochastic homogenization of elliptic equations with unbounded and non-uniformly elliptic coefficients. Extending subadditive arguments, we get an estimate for the rate of the convergence of the solution of the…

Probability · Mathematics 2023-02-03 Tomohiro Aya

This paper presents a general convergence theory of penalty based numerical methods for elliptic constrained inequality problems, including variational inequalities, hemivariational inequalities, and variational-hemivariational…

Numerical Analysis · Mathematics 2019-12-18 Weimin Han , Mircea Sofonea

In this paper, we study a generalized finite element method for solving second-order elliptic partial differential equations with rough coefficients. The method uses local approximation spaces computed by solving eigenvalue problems on…

Numerical Analysis · Mathematics 2025-07-17 Christian Alber , Peter Bastian , Moritz Hauck , Robert Scheichl

Regularizing neural networks is important for anticipating model behavior in regions of the data space that are not well represented. In this work, we propose a regularization technique for enforcing a level of smoothness in the mapping…

Machine Learning · Computer Science 2025-03-05 Ali Hasan , Haoming Yang , Yuting Ng , Vahid Tarokh