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This paper presents a new approach to selecting knots at the same time as estimating the B-spline regression model. Such simultaneous selection of knots and model is not trivial, but our strategy can make it possible by employing a…

Optimization and Control · Mathematics 2023-04-06 Shotaro Yagishita , Jun-ya Gotoh

We present a method for approximating solutions of Stochastic Differential Equations (SDEs) with arbitrary rates. This approximation is derived for bounded and measurable test functions. Specifically, we demonstrate that, leveraging the…

Probability · Mathematics 2024-03-27 Clément Rey

The efficient simulation of the mean value of a non-linear functional of the solution to a linear stochastic partial differential equation (SPDE) with additive Gaussian noise is considered. A Galerkin finite element method is employed along…

Probability · Mathematics 2019-07-25 Andreas Petersson

We prove the optimal strong convergence rate of a fully discrete scheme, based on a splitting approach, for a stochastic nonlinear Schr\"odinger (NLS) equation. The main novelty of our method lies on the uniform a priori estimate and…

Numerical Analysis · Mathematics 2019-02-25 Jianbo Cui , Jialin Hong , Zhihui Liu , Weien Zhou

In this work, an adaptive time-stepping Milstein method is constructed for stochastic differential equations with piecewise continuous arguments (SDEPCAs), where the drift is one-sided Lipschitz continuous and the diffusion does not impose…

Numerical Analysis · Mathematics 2025-02-25 Yuhang Zhang , Minghui Song , Jiaqi Zhu

A higher-order numerical method is presented for scalar valued, coupled forward-backward stochastic differential equations. Unlike most classical references, the forward component is not only discretized by an Euler-Maruyama approximation…

Numerical Analysis · Mathematics 2025-01-22 Balint Negyesi , Cornelis W. Oosterlee

In this paper we are interested in the numerical solution of stochastic differential equations with non negative solutions. Our goal is to construct explicit numerical schemes that preserve positivity, even for super linear stochastic…

Numerical Analysis · Mathematics 2014-12-18 Nikolaos Halidias , Ioannis S. Stamatiou

This paper introduces time-continuous numerical schemes to simulate stochastic differential equations (SDEs) arising in mathematical finance, population dynamics, chemical kinetics, epidemiology, biophysics, and polymeric fluids. These…

Probability · Mathematics 2015-03-13 Nawaf Bou-Rabee , Eric Vanden-Eijnden

In this paper, we propose an inexact proximal Newton-type method for nonconvex composite problems. We establish the global convergence rate of the order $\mathcal{O}(k^{-1/2})$ in terms of the minimal norm of the KKT residual mapping and…

Optimization and Control · Mathematics 2024-12-26 Hong Zhu

A conjecture appears in \cite{milsteinscheme}, in the form of a remark, where it is stated that it is possible to construct, in a specified way, any high order explicit numerical schemes to approximate the solutions of SDEs with superlinear…

Probability · Mathematics 2018-11-07 Sotirios Sabanis , Ying Zhang

For uncertainty propagation of highly complex and/or nonlinear problems, one must resort to sample-based non-intrusive approaches [1]. In such cases, minimizing the number of function evaluations required to evaluate the response surface is…

Numerical Analysis · Mathematics 2017-12-04 Anindya Bhaduri , Lori Graham-Brady

Let a continuous random process $X$ defined on $[0,1]$ be $(m+\beta)$-smooth, $0\le m, 0<\beta\le 1$, in quadratic mean for all $t>0$ and have an isolated singularity point at $t=0$. In addition, let $X$ be locally like a $m$-fold…

Probability · Mathematics 2010-05-20 Konrad Abramowicz , Oleg Seleznjev

Although the governing equations of many systems, when derived from first principles, may be viewed as known, it is often too expensive to numerically simulate all the interactions they describe. Therefore researchers often seek simpler…

Computation · Statistics 2021-05-03 Tapio Schneider , Andrew M. Stuart , Jin-Long Wu

In traditional work on numerical schemes for solving stochastic differential equations (SDEs), it is usually assumed that the coefficients are globally Lipschitz. This assumption has been used to establish a powerful analysis of the…

Probability · Mathematics 2017-09-15 Philip Protter , Lisha Qiu , Jaime San Martin

The paper establishes the strong convergence rates of a spatio-temporal full discretization of the stochastic wave equation with nonlinear damping in dimension one and two. We discretize the SPDE by applying a spectral Galerkin method in…

Numerical Analysis · Mathematics 2024-12-30 Meng Cai , David Cohen , Xiaojie Wang

This article revisits the approximation problem of systems of nonlinear delay differential equations (DDEs) by a set of ordinary differential equations (ODEs). We work in Hilbert spaces endowed with a natural inner product including a point…

Chaotic Dynamics · Physics 2015-09-11 Mickaël D. Chekroun , Michael Ghil , Honghu Liu , Shouhong Wang

This paper is concerned with developing and analyzing two novel implicit temporal discretization methods for the stochastic semilinear wave equations with multiplicative noise. The proposed methods are natural extensions of well-known…

Numerical Analysis · Mathematics 2024-08-26 Xiaobing Feng , Yukun Li , Liet Vo

This paper examines convergence and stability of the two classes of theta-Milstein schemes for stochastic differential equations (SDEs) with non-global Lipschitz continuous coefficients: the split-step theta-Milstein (SSTM) scheme and the…

Numerical Analysis · Mathematics 2015-01-16 Xiaofeng Zong , Fuke Wu , Guiping Xu

In this paper, we prove convergence rates for time discretisation schemes for semi-linear stochastic evolution equations with additive or multiplicative Gaussian noise, where the leading operator $A$ is the generator of a strongly…

Numerical Analysis · Mathematics 2024-12-19 Katharina Klioba , Mark Veraar

This article investigates time-discrete approximations of Allen-Cahn type SPDEs driven by space-time white noise near the sharp interface limit $\epsilon\to 0$, where the small parameter $\epsilon$ is the diffuse interface thickness. We…

Numerical Analysis · Mathematics 2026-01-06 Yingsong Jiang , Chenxu Pang , Xiaojie Wang