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We present a new algorithmic paradigm for the decentralized solution of graph-structured optimization problems that arise in the estimation and control of network systems. A key and novel design concept of the proposed approach is that it…

Optimization and Control · Mathematics 2020-04-01 Sungho Shin , Victor M. Zavala , Mihai Anitescu

A novel fourth-order finite difference formula coupling the Crank-Nicolson explicit linearized method is proposed to solve Riesz space fractional nonlinear reaction-diffusion equations in two dimensions. Theoretically, under the Lipschitz…

Numerical Analysis · Mathematics 2024-05-07 Wei Qu , Yuan-Yuan Huang , Sean Hon , Siu-Long Lei

A linearly implicit conservative difference scheme is applied to discretize the attractive coupled nonlinear Schr\"odinger equations with fractional Laplacian. Complex symmetric linear systems can be obtained, and the system matrices are…

Numerical Analysis · Mathematics 2023-10-19 Yan Cheng , Xi Yang

This paper is concerned with stochastic incompressible Navier-Stokes equations with multiplicative noise in two dimensions with respect to periodic boundary conditions. Based on the Helmholtz decomposition of the multiplicative noise,…

Numerical Analysis · Mathematics 2022-11-28 Hailong Qiu

In this paper, we propose an efficient method for solving multi-dimensional Riesz space fractional diffusion equations with variable coefficients. The Crank-Nicolson (CN) method is used for temporal discretization, while the fourth-order…

Numerical Analysis · Mathematics 2025-08-01 Yuan-Yuan Huang , Wei Qu , Sean Y. Hon , Siu-Long Lei

Nonlinear elliptic problems arise in many fields, including plasma physics, astrophysics, and optimal transport. In this article, we propose a novel operator-splitting/finite element method for solving such problems. We begin by introducing…

Numerical Analysis · Mathematics 2025-09-12 Jingyu Yang , Shingyu Leung , Jianliang Qian , Hao Liu

Stochastic domain decomposition is proposed as a novel method for solving the two-dimensional Maxwell's equations as used in the magnetotelluric method. The stochastic form of the exact solution of Maxwell's equations is evaluated using…

The discretization of certain integral equations, e.g., the first-kind Fredholm equation of Laplace's equation, leads to symmetric positive-definite linear systems, where the coefficient matrix is dense and often ill-conditioned. We…

Numerical Analysis · Mathematics 2022-08-22 Chao Chen , George Biros

This work focuses on the development and analysis of a partitioned numerical method for moving domain, fluid-structure interaction problems. We model the fluid using incompressible Navier-Stokes equations, and the structure using linear…

Numerical Analysis · Mathematics 2020-07-03 Anyastassia Seboldt , Martina Bukač

In this paper, a parallel overlapping domain decomposition preconditioner is proposed to solve the linear system of equations arising from the extended finite element discretization of elastic crack problems. The algorithm partitions the…

Computational Engineering, Finance, and Science · Computer Science 2021-12-07 Tian Wei , Huang Jingjing , Chen Rongliang , Jiang Yi

Low rank matrix approximation is a popular topic in machine learning. In this paper, we propose a new algorithm for this topic by minimizing the least-squares estimation over the Riemannian manifold of fixed-rank matrices. The algorithm is…

Machine Learning · Computer Science 2022-02-15 Qianqian Song

We present a finite-difference integration algorithm for solution of a system of differential equations containing a diffusion equation with nonlinear terms. The approach is based on Crank-Nicolson method with predictor-corrector algorithm…

Computational Physics · Physics 2019-07-05 V. P. Lipp , B. Rethfeld , M. E. Garcia , D. S. Ivanov

We introduce a new domain decomposition strategy for time harmonic Maxwell's equations that is valid in the case of automatically generated subdomain partitions with possible presence of cross-points. The convergence of the algorithm is…

Numerical Analysis · Mathematics 2022-09-20 Xavier Claeys , Francis Collino , Emile Parolin

We study a decoupling iterative algorithm based on domain decomposition for the time-dependent nonlinear Stokes-Darcy model, in which different time steps can be used in the flow region and in the porous medium. The coupled system is…

Numerical Analysis · Mathematics 2020-07-06 Thi-Thao-Phuong Hoang , Hyesuk Lee

This paper is concerned with the problem of low rank plus sparse matrix decomposition for big data. Conventional algorithms for matrix decomposition use the entire data to extract the low-rank and sparse components, and are based on…

Numerical Analysis · Computer Science 2017-03-17 Mostafa Rahmani , George Atia

In this paper we present an algebraic dimension-oblivious two-level domain decomposition solver for discretizations of elliptic partial differential equations. The proposed parallel solver is based on a space-filling curve partitioning…

Numerical Analysis · Mathematics 2026-05-01 Michael Griebel , Marc Alexander Schweitzer , Lukas Troska

A non-intrusive proper generalized decomposition (PGD) strategy, coupled with an overlapping domain decomposition (DD) method, is proposed to efficiently construct surrogate models of parametric linear elliptic problems. A parametric…

Numerical Analysis · Mathematics 2023-10-17 Marco Discacciati , Ben J. Evans , Matteo Giacomini

We approximate the solution for the time dependent Schr\"odinger equation (TDSE) in two steps. We first use a pseudo-spectral collocation method that uses samples of functions on rank-1 or rank-r lattice points with unitary Fourier…

Numerical Analysis · Mathematics 2020-07-01 Yuya Suzuki , Gowri Suryanarayana , Dirk Nuyens

Non-overlapping domain decomposition methods necessarily have to exchange Dirichlet and Neumann traces at interfaces in order to be able to converge to the underlying mono-domain solution. Well known such non-overlapping methods are the…

Numerical Analysis · Mathematics 2014-04-21 Martin J. Gander , Kévin Santugini-Repiquet

The iterated Crank-Nicolson is a predictor-corrector algorithm commonly used in numerical relativity for the solution of both hyperbolic and parabolic partial differential equations. We here extend the recent work on the stability of this…

General Relativity and Quantum Cosmology · Physics 2009-11-11 Gregor Leiler , Luciano Rezzolla