Domain Decomposition Methods for the Monge–Ampère Equation

IF 2.8 2区 数学 Q1 MATHEMATICS, APPLIED
Yassine Boubendir, Jake Brusca, Brittany F. Hamfeldt, Tadanaga Takahashi
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引用次数: 0

Abstract

SIAM Journal on Numerical Analysis, Volume 62, Issue 4, Page 1979-2003, August 2024.
Abstract. We introduce a new overlapping domain decomposition method (DDM) to solve fully nonlinear elliptic partial differential equations (PDEs) approximated with monotone schemes. While DDMs have been extensively studied for linear problems, their application to fully nonlinear PDEs remains limited in the literature. To address this gap, we establish a proof of global convergence of these new iterative algorithms using a discrete comparison principle argument. We also provide a specific implementation for the Monge–Ampère equation. Several numerical tests are performed to validate the convergence theorem. These numerical experiments involve examples of varying regularity. Computational experiments show that method is efficient, robust, and requires relatively few iterations to converge. The results reveal great potential for DDM methods to lead to highly efficient and parallelizable solvers for large-scale problems that are computationally intractable using existing solution methods.
蒙日-安培方程的领域分解方法
SIAM 数值分析期刊》,第 62 卷第 4 期,第 1979-2003 页,2024 年 8 月。 摘要。我们介绍了一种新的重叠域分解方法 (DDM),用于求解用单调方案逼近的全非线性椭圆偏微分方程 (PDE)。虽然 DDM 已针对线性问题进行了广泛研究,但其在全非线性偏微分方程中的应用在文献中仍然有限。为了填补这一空白,我们利用离散比较原理论证了这些新迭代算法的全局收敛性。我们还提供了 Monge-Ampère 方程的具体实现方法。为了验证收敛定理,我们进行了一些数值测试。这些数值实验涉及不同规律性的例子。计算实验表明,该方法高效、稳健,只需相对较少的迭代即可收敛。这些结果揭示了 DDM 方法的巨大潜力,它可以为现有求解方法难以计算的大规模问题提供高效、可并行的求解器。
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来源期刊
CiteScore
4.80
自引率
6.90%
发文量
110
审稿时长
4-8 weeks
期刊介绍: SIAM Journal on Numerical Analysis (SINUM) contains research articles on the development and analysis of numerical methods. Topics include the rigorous study of convergence of algorithms, their accuracy, their stability, and their computational complexity. Also included are results in mathematical analysis that contribute to algorithm analysis, and computational results that demonstrate algorithm behavior and applicability.
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