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The simulation of chemical kinetics involving multiple scales constitutes a modeling challenge (from ordinary differential equations to Markov chain) and a computational challenge (multiple scales, large dynamical systems, time step…

Numerical Analysis · Mathematics 2021-06-18 Assyr Abdulle , Lia Gander , Giacomo Rosilho de Souza

We propose and analyze a novel approach to construct structure preserving approximations for the Poisson-Nernst-Planck equations, focusing on the positivity preserving and mass conservation properties. The strategy consists of a standard…

Numerical Analysis · Mathematics 2024-03-08 Fenghua Tong , Yongyong Cai

We present high-order compact schemes for a linear second-order parabolic partial differential equation (PDE) with mixed second-order derivative terms in two spatial dimensions. The schemes are applied to option pricing PDE for a family of…

Computational Finance · Quantitative Finance 2016-11-02 Bertram Düring , Christof Heuer

Simulation of many-particle system evolution by molecular dynamics takes to decrease integration step to provide numerical scheme stability on the sufficiently large time interval. It leads to a significant increase of the volume of…

Numerical Analysis · Mathematics 2016-05-19 Eduard G. Nikonov

Two-step predictor/corrector methods are provided to solve three classes of problems that present themselves as systems of ordinary differential equations (ODEs). In the first class, velocities are given from which displacements are to be…

Numerical Analysis · Computer Science 2017-07-10 Alan D. Freed

In this paper, we propose a new kind of numerical scheme for high-dimensional backward stochastic differential equations based on modified multi-level Picard iteration. The proposed scheme is very similar to the original multi-level Picard…

Numerical Analysis · Mathematics 2019-05-06 Chol-Kyu Pak , Mun-Chol Kim , Hun O

A sequential quadratic optimization algorithm is proposed for solving smooth nonlinear equality constrained optimization problems in which the objective function is defined by an expectation of a stochastic function. The algorithmic…

Optimization and Control · Mathematics 2023-03-17 Albert S. Berahas , Frank E. Curtis , Michael J. O'Neill , Daniel P. Robinson

This article presents a finite element scheme with Newton's method for solving the time-fractional nonlinear diffusion equation. For time discretization, we use the fractional Crank-Nicolson scheme based on backward Euler convolution…

Analysis of PDEs · Mathematics 2018-11-26 Dileep Kumar , Sudhakar Chaudhary , V. V. K Srinivas Kumar

In a recent paper we presented a new ultra efficient numerical method for solving kinetic equations of the Boltzmann type (G. Dimarco, R. Loubere, Towards an ultra efficient kinetic scheme. Part I: basics on the 689 BGK equation, J. Comp.…

Numerical Analysis · Mathematics 2015-06-12 Giacomo Dimarco , Raphaël Loubere

In this paper, we propose and analyze a first-order and a second-order time-stepping schemes for the anisotropic phase-field dendritic crystal growth model. The proposed schemes are based on an auxiliary variable approach for the Allen-Cahn…

Numerical Analysis · Mathematics 2021-09-06 Minghui Li , Mejdi Azaiez , Chuanju Xu

We present a high order parameter-robust numerical method for a system of (M>=2) coupled singularly perturbed parabolic reaction-diffusion problems. A small perturbation parameter {\epsilon} is multiplied with the second order spatial…

Numerical Analysis · Mathematics 2015-08-03 Mukesh Kumar , S. Chandra Sekhara Rao

We study stochastic Nash equilibrium problems with expected valued cost functions whose pseudogradient satisfies restricted monotonicity properties which hold only with respect to the solution. We propose a forward-backward algorithm and…

Optimization and Control · Mathematics 2021-11-05 Barbara Franci , Sergio Grammatico

In this paper, a class of high order numerical schemes is proposed to solve the nonlinear parabolic equations with variable coefficients. This method is based on our previous work [10] for convection-diffusion equations, which relies on a…

Numerical Analysis · Mathematics 2020-12-30 Kaipeng Wang , Andrew Christlieb , Yan Jiang , Mengping Zhang

The implicit compact finite-difference scheme was developed for evolutionary partial differential parabolic and Schr\"odinger-type equations and systems with a weak nonlinearity. To make a temporal step of the compact implicit scheme we…

Mathematical Physics · Physics 2018-12-31 Vladimir Gordin , Evgenii Tsymbalov

We develop stochastic first-order primal-dual algorithms to solve a class of convex-concave saddle-point problems. When the saddle function is strongly convex in the primal variable, we develop the first stochastic restart scheme for this…

Optimization and Control · Mathematics 2021-04-13 Renbo Zhao

Recently a useful finite-difference scheme was proposed in [Phys. Rev. E 98, 033302 (2018)] to solve Fokker-Planck equations with drift-admitting jumps. However, while the scheme is fifth order for the case with smooth drifts, it is only…

Statistical Mechanics · Physics 2019-11-05 Yaming Chen , Xiaogang Deng

In this paper, we present a novel approach to solving the American put options pricing model by hugely relying on a front-fixing Crank-Nicolson finite difference method. Since the American put option pricing model is a widely used financial…

Analysis of PDEs · Mathematics 2025-12-09 Z. I. Ali , M. A. Abebe

This paper focuses on systems of nonlinear second-order stochastic differential equations with multi-scales. The motivation for our study stems from mathematical physics and statistical mechanics, for examples, Langevin dynamics and…

Probability · Mathematics 2024-04-08 Nhu N. Nguyen , George Yin

In this work, we generalized and unified recent two completely different works of Jascha \cite{sohl2014fast} and Lee \cite{lee2012proximal} respectively into one by proposing the \textbf{prox}imal s\textbf{to}chastic \textbf{N}ewton-type…

Optimization and Control · Mathematics 2014-10-30 Ziqiang Shi

The paper looks at a scaled variant of the stochastic gradient descent algorithm for the matrix completion problem. Specifically, we propose a novel matrix-scaling of the partial derivatives that acts as an efficient preconditioning for the…

Machine Learning · Computer Science 2016-10-06 Bamdev Mishra , Rodolphe Sepulchre