Unique Solvability and Error Analysis of a Scheme Using the Lagrange Multiplier Approach for Gradient Flows

IF 2.8 2区 数学 Q1 MATHEMATICS, APPLIED
Qing Cheng, Jie Shen, Cheng Wang
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引用次数: 0

Abstract

SIAM Journal on Numerical Analysis, Volume 63, Issue 2, Page 772-799, April 2025.
Abstract. The unique solvability and error analysis of a scheme using the original Lagrange multiplier approach proposed in [Q. Cheng, C. Liu, and J. Shen, Comput. Methods Appl. Mech. Engrg., 367 (2020), 13070] for gradient flows is studied in this paper. We identify a necessary and sufficient condition that must be satisfied for the nonlinear algebraic equation arising from the original Lagrange multiplier approach to admit a unique solution in the neighborhood of its exact solution. Then we find that the unique solvability of the original Lagrange multiplier approach depends on the aforementioned condition and may be valid over a finite time period. Afterward, we propose a modified Lagrange multiplier approach to ensure that the computation can continue even if the aforementioned condition was not satisfied. Using the Cahn–Hilliard equation as an example, we prove rigorously the unique solvability and establish optimal error estimates of a second-order Lagrange multiplier scheme assuming this condition and that the time step is sufficiently small. We also present numerical results to demonstrate that the modified Lagrange multiplier approach is much more robust and can use a much larger time step than the original Lagrange multiplier approach.
梯度流拉格朗日乘子法格式的唯一可解性及误差分析
SIAM数值分析杂志,第63卷,第2期,第772-799页,2025年4月。摘要。利用[Q]中提出的原始拉格朗日乘子方法的唯一可解性和误差分析。​方法:。动力机械。Engrg。[j],[367(2020), 13070]。本文给出了由原始拉格朗日乘子法引起的非线性代数方程在其精确解的邻域中存在唯一解所必须满足的一个充分必要条件。然后我们发现原始拉格朗日乘子方法的唯一可解性依赖于上述条件,并且可能在有限时间内有效。然后,我们提出了一种改进的拉格朗日乘子方法,以确保即使不满足上述条件,计算也可以继续进行。以Cahn-Hilliard方程为例,在此条件下,在时间步长足够小的条件下,我们严格证明了二阶拉格朗日乘子格式的唯一可解性,并建立了最优误差估计。数值结果表明,改进的拉格朗日乘子方法比原始的拉格朗日乘子方法具有更强的鲁棒性,并且可以使用更大的时间步长。
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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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