最小化一类朗道自由能函数的布雷格曼迭代收敛分析

IF 2.8 2区 数学 Q1 MATHEMATICS, APPLIED SIAM Journal on Numerical Analysis Pub Date : 2024-02-07 DOI:10.1137/22m1517664
Chenglong Bao, Chang Chen, Kai Jiang, Lingyun Qiu
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引用次数: 0

摘要

SIAM 数值分析期刊》第 62 卷第 1 期第 476-499 页,2024 年 2 月。 摘要寻找朗道自由能函数的静止状态必须解决一个非凸无穷维优化问题。本文开发了一种基于 Bregman 距离的优化方法,用于最小化一类 Landau 能量函数,并重点分析了该方法在函数空间中的收敛性。我们首先分析了静止状态的正则性,并展示了所提方法的弱顺序收敛结果。此外,在 Łojasiewicz-Simon 特性下,我们证明了强序列收敛特性,并在适当的希尔伯特空间中建立了局部收敛率。我们特别分析了三个著名朗道模型的 Łojasiewicz 指数,即朗道-布拉佐夫斯基自由能函数、利夫希茨-佩特里奇自由能函数和奥塔-川崎自由能函数。最后,数值结果支持我们的理论分析。
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Convergence Analysis for Bregman Iterations in Minimizing a Class of Landau Free Energy Functionals
SIAM Journal on Numerical Analysis, Volume 62, Issue 1, Page 476-499, February 2024.
Abstract. Finding stationary states of Landau free energy functionals has to solve a nonconvex infinite-dimensional optimization problem. In this paper, we develop a Bregman distance based optimization method for minimizing a class of Landau energy functionals and focus on its convergence analysis in the function space. We first analyze the regularity of the stationary states and show the weakly sequential convergence results of the proposed method. Furthermore, under the Łojasiewicz–Simon property, we prove a strongly sequential convergent property and establish the local convergence rate in an appropriate Hilbert space. In particular, we analyze the Łojasiewicz exponent of three well-known Landau models, the Landau–Brazovskii, Lifshitz–Petrich, and Ohta–Kawasaki free energy functionals. Finally, numerical results support our theoretical analysis.
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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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