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In this work, we propose a novel backward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations (BSDEs), where the deep neural network (DNN) models are trained not only…

Numerical Analysis · Mathematics 2024-04-15 Lorenc Kapllani , Long Teng

In this note, we present a new numerical method for solving backward stochastic differential equations. Our method can be viewed as an analogue of the classical finite element method solving deterministic partial differential equations.

Probability · Mathematics 2011-06-07 Penghui Wang , Xu Zhang

We define some approximation schemes for different kinds of generalized backward stochastic differential systems, considered in the Markovian framework. We propose a mixed approximation scheme for a decoupled system of forward reflected SDE…

Probability · Mathematics 2015-11-20 Lucian Maticiuc , Eduard Rotenstein

In this paper, we derive error estimates of the backward Euler-Maruyama method applied to multi-valued stochastic differential equations. An important example of such an equation is a stochastic gradient flow whose associated potential is…

Numerical Analysis · Mathematics 2022-05-10 Monika Eisenmann , Mihály Kovács , Raphael Kruse , Stig Larsson

In this work, we apply the Stochastic Grid Bundling Method (SGBM) to numerically solve backward stochastic differential equations (BSDEs). The SGBM algorithm is based on conditional expectations approximation by means of bundling of Monte…

Numerical Analysis · Mathematics 2019-08-26 Ki Wai Chau , Cornelis W. Oosterlee

This paper studies the numerical methods to approximate the solutions for a sort of McKean-Vlasov neutral stochastic differential delay equations (MV-NSDDEs) that the growth of the drift coefficients is super-linear. First, We obtain that…

Probability · Mathematics 2022-11-04 Yuanping Cui , Xiaoyue Li , Yi Liu , Chenggui Yuan

In this work, a new approach has been developed to obtain numerical solution of linear Volterra type integral equations by obtaining asymptotic approximation to solutions. Using the classical Bernoulli polynomials, a set of orthonormal…

Numerical Analysis · Mathematics 2020-07-22 Udaya Pratap Singh

This paper is dedicated to the presentation and the analysis of a numerical scheme for forward-backward SDEs of the McKean-Vlasov type, or equivalently for solutions to PDEs on the Wasserstein space. Because of the mean field structure of…

Probability · Mathematics 2017-03-07 Jean-François Chassagneux , Dan Crisan , François Delarue

We study the $L^1$-approximation of the log-Heston SDE at equidistant time points by Euler-type methods. We establish the convergence order $ 1/2-\epsilon$ for $\epsilon >0$ arbitrarily small, if the Feller index $\nu$ of the underlying CIR…

Numerical Analysis · Mathematics 2023-04-26 Annalena Mickel , Andreas Neuenkirch

In a recent paper by Kamrani et al. (2024), exponential Euler method for stiff stochastic differential equations with additive fractional Brownian noise was discussed, and the convergence order close to the Hurst parameter H was proved.…

Probability · Mathematics 2024-07-08 Haozhe Chen , Zhaotong Shen , Qian Yu

Backward stochastic differential equations (BSDEs) appear in numeruous applications. Classical approximation methods suffer from the curse of dimensionality and deep learning-based approximation methods are not known to converge to the BSDE…

Probability · Mathematics 2022-04-20 Martin Hutzenthaler , Tuan Anh Nguyen

The systems of nonlinear Volterra integral equations of the first kind with jump discontinuous kernels are studied. The iterative numerical method for such nonlinear systems is proposed. Proposed method employs the modified…

Numerical Analysis · Mathematics 2019-10-22 A. N. Tynda , D. N. Sidorov , N. A. Sidorov

Numerical methods for computing the solutions of Markov backward stochastic differential equations (BSDEs) driven by continuous-time Markov chains (CTMCs) are explored. The main contributions of this paper are as follows: (1) we observe…

Probability · Mathematics 2023-11-27 Akihiro Kaneko

In this paper, we investigate the asymptotic distribution of the normalized error for the Mittag--Leffler Euler (MLE) method applied to a class of multidimensional fractional stochastic differential equations. These equations are…

Numerical Analysis · Mathematics 2026-03-24 Xinjie Dai , Baiping Zhang , Diancong Jin

Motivated by fractional derivative models in viscoelasticity, a class of semilinear stochastic Volterra integro-differential equations, and their deterministic counterparts, are considered. A generalized exponential Euler method, named here…

Numerical Analysis · Mathematics 2020-01-17 Mihály Kovács , Stig Larsson , Fardin Saedpanah

In this work we consider a stochastic differential equation (SDEs) with jump. We prove the existence and the uniqueness of solution of this equation in the strong sense under global Lipschitz condition. Generally, exact solutions of SDEs…

Numerical Analysis · Mathematics 2015-10-09 Jean Daniel Mukam

In the present paper, a Nystrom-type method for second kind Volterra integral equations is introduced and studied. The method makes use of generalized Bernstein polynomials, defined for continuous functions and based on equally spaced…

Numerical Analysis · Mathematics 2022-07-15 Luisa Fermo , Domenico Mezzanotte , Donatella Occorsio

We study a numerical approximation for a nonlinear variable-order fractional differential equation via an integral equation method. Due to the lack of the monotonicity of the discretization coefficients of the variable-order fractional…

Numerical Analysis · Mathematics 2021-10-12 Xiangcheng Zheng

This paper proposes an adaptive numerical method for stochastic delay differential equations (SDDEs) with a non-global Lipschitz drift term and a non-constant delay, building upon the work of Wei Fang and others. The method adapts the step…

Numerical Analysis · Mathematics 2024-07-02 Dongyang Liu , Minghui Song , Yuhang Zhang

We report on a novel algorithm for controlling global error in a step-by-step (stepwise) sense, in the numerical solution of a scalar, autonomous, nonstiff or weakly stiff problem. The algorithm exploits the remainder term of a Taylor…

Numerical Analysis · Mathematics 2023-03-20 J. S. C. Prentice
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