改进的高指数鞍动态寻找鞍点和解决方案景观

IF 2.9 2区 数学 Q1 MATHEMATICS, APPLIED
Hua Su, Haoran Wang, Lei Zhang, Jin Zhao, Xiangcheng Zheng
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引用次数: 0

摘要

SIAM数值分析杂志,第63卷,第4期,1757-1775页,2025年8月。摘要。本文提出了一种用于寻找鞍点和构建解景观的改进的高指数鞍区动力学(iHiSD),它是一种从梯度流到传统的高指数鞍区动力学的交叉动力学,从而可以涉及梯度流的莫尔斯理论。我们对反射流形进行了分析,并从可能不接近目标鞍点的平稳点证明了反射流形的稳定性和非局部收敛性,从而降低了反射流形收敛对初始值的依赖性。然后,我们提出并分析了一个离散的iHiSD实现。此外,基于莫尔斯理论,我们证明了任意两个鞍点可以由iHiSD的一系列轨迹连接起来。理想情况下,这意味着通过iHiSD可以完整地构建具有有限个静止点的解景观,这在一定程度上首次回答了解景观的完整性问题,并表明了在HiSD中积分梯度流的必要性。通过数值实验对不同方法进行了比较,验证了iHiSD方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved High-Index Saddle Dynamics for Finding Saddle Points and Solution Landscape
SIAM Journal on Numerical Analysis, Volume 63, Issue 4, Page 1757-1775, August 2025.
Abstract. We present an improved high-index saddle dynamics (iHiSD) for finding saddle points and constructing solution landscapes, which is a crossover dynamics from gradient flow to traditional HiSD such that the Morse theory for gradient flow could be involved. We propose analysis for the reflection manifold in iHiSD and then prove its stable and nonlocal convergence from stationary points that may not be close to the target saddle point, which reduces the dependence of the convergence of HiSD on the initial value. We then present and analyze a discretized iHiSD for implementation. Furthermore, based on Morse theory, we prove that any two saddle points could be connected by a sequence of trajectories of iHiSD. Ideally, this implies that a solution landscape with a finite number of stationary points could be completely constructed by means of iHiSD, which partly answers the completeness issue of the solution landscape for the first time and indicates the necessity of integrating the gradient flow in HiSD. Different methods are compared by numerical experiments to substantiate the effectiveness of the iHiSD method.
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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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