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We consider a finite element approximation for a system consisting of the evolution of a closed planar curve by forced curve shortening flow coupled to a reaction-diffusion equation on the evolving curve. The scheme for the curve evolution…

Numerical Analysis · Mathematics 2016-07-07 John W. Barrett , Klaus Deckelnick , Vanessa Styles

The Euler scheme is one of the standard schemes to obtain numerical approximations of stochastic differential equations (SDEs). Its convergence properties are well-known in the case of globally Lipschitz continuous coefficients. However, in…

Numerical Analysis · Mathematics 2019-01-29 S. Göttlich , K. Lux , A. Neuenkirch

We introduce a new approach to quantize the Euler scheme of an $\mathbb{R}^d$-valued diffusion process. This method is based on a Markovian and componentwise product quantization and allows us, from a numerical point of view, to speak of…

Probability · Mathematics 2017-03-27 Fiorin Lucio , Gilles Pagès , Abass Sagna

Score-based diffusion models have emerged as powerful tools in generative modeling, yet their theoretical foundations remain underexplored. In this work, we focus on the Wasserstein convergence analysis of score-based diffusion models.…

Machine Learning · Statistics 2025-02-10 Yifeng Yu , Lu Yu

We investigate the strong approximation of stochastic differential equations whose drift is square-integrable in time and Dini continuous in space, while the diffusion coefficient is non-constant and uniformly elliptic. Using a refined…

Probability · Mathematics 2026-02-16 Jinlong Wei , Junhao Hu , Guangying Lv , Chenggui Yuan

The rapid advancements in high-dimensional statistics and machine learning have increased the use of first-order methods. Many of these methods can be regarded as instances of the proximal point algorithm. Given the importance of the…

Optimization and Control · Mathematics 2024-11-05 Ya-xiang Yuan , Yi Zhang

We provide faster algorithms for the problem of Gaussian summation, which occurs in many machine learning methods. We develop two new extensions - an O(Dp) Taylor expansion for the Gaussian kernel with rigorous error bounds and a new error…

Machine Learning · Computer Science 2012-07-02 Dongryeol Lee , Alexander G. Gray

The 2D Euler equations are a simple but rich set of non-linear PDEs that describe the evolution of an ideal inviscid fluid, for which one dimension is negligible. Solving numerically these equations can be extremely demanding. Several…

Numerical Analysis · Mathematics 2023-01-18 Paolo Cifani , Sagy Ephrati , Milo Viviani

We prove stability and convergence of a full discretization for a class of stochastic evolution equations with super-linearly growing operators appearing in the drift term. This is done using the recently developed tamed Euler method, which…

Probability · Mathematics 2015-08-14 István Gyöngy , Sotirios Sabanis , David Šiška

In this article we show that for SDEs with a drift coefficient that is non-locally integrable, one may define a tamed Euler scheme that converges in $L^p$ at rate $1/2$ to the true solution. The taming is required in this case since one…

Probability · Mathematics 2024-08-16 Tim Johnston , Sotirios Sabanis

The present work introduces and investigates an explicit time discretization scheme, called the projected Euler method,to numerically approximate random periodic solutions of semi-linear SDEs under non-globally Lipschitz conditions. The…

Numerical Analysis · Mathematics 2024-11-26 Yujia Guo , Xiaojie Wang , Yue Wu

We show that, for any spatially discretized system of reaction-diffusion, the approximate solution given by the explicit Euler time-discretization scheme converges to the exact time-continuous solution, provided that diffusion coefficient…

Optimization and Control · Mathematics 2019-12-12 Adrien Le Coënt , Laurent Fribourg

Time discretization along with space discretization is important in the numerical simulation of subsurface flow applications for long run. In this paper, we derive theoretical convergence error estimates in discrete-time setting for…

Numerical Analysis · Mathematics 2020-03-04 Yerlan Amanbek , Mary Wheeler

We consider controlled differential equations and give new estimates for higher order Euler schemes. Our proofs are inspired by recent work of A. M. Davie who considers first and second order schemes. In order to implement the general case…

Classical Analysis and ODEs · Mathematics 2007-05-23 Peter Friz , Nicolas Victoir

Reflected diffusions in polyhedral domains are commonly used as approximate models for stochastic processing networks in heavy traffic. Stationary distributions of such models give useful information on the steady state performance of the…

Probability · Mathematics 2012-05-24 Amarjit Budhiraja , Jiang Chen , Sylvain Rubenthaler

Much recent interest has focused on the design of optimization algorithms from the discretization of an associated optimization flow, i.e., a system of differential equations (ODEs) whose trajectories solve an associated optimization…

Optimization and Control · Mathematics 2022-02-02 Pedro Cisneros-Velarde , Francesco Bullo

A family of explicit modified Euler methods (MEMs) is constructed for long-time approximations of super-linear SODEs driven by multiplicative noise. The proposed schemes can preserve the same Lyapunov structure as the continuous problems.…

Numerical Analysis · Mathematics 2025-09-11 Zhihui Liu , Xiaojie Wang , Xiaoming Wu , Xiaoyan Zhang

In a previous paper it was shown that the Forward Euler method applied to differential inclusions where the right-hand side is a Lipschitz continuous set-valued function with uniformly bounded, compact values, converges with rate one. The…

Numerical Analysis · Mathematics 2009-02-02 Mattias Sandberg

We obtain non asymptotic bounds for the Monte Carlo algorithm associated to the Euler discretization of some diffusion processes. The key tool is the Gaussian concentration satisfied by the density of the discretization scheme. This…

Probability · Mathematics 2018-02-20 Vincent Lemaire , Stephane Menozzi

Langevin Dynamics has been extensively employed in global non-convex optimization due to the concentration of its stationary distribution around the global minimum of the potential function at low temperatures. In this paper, we propose to…

Optimization and Control · Mathematics 2023-05-22 Ryo Fujino