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Accelerated high-index saddle dynamics method for searching high-index saddle points
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abstract
The high-index saddle dynamics (HiSD) method [J. Yin, L. Zhang, and P. Zhang, {\it SIAM J. Sci. Comput., }41 (2019), pp.A3576-A3595] serves as an efficient tool for computing index-$k$ saddle points and constructing solution landscapes. Nevertheless, the conventional HiSD method often encounters slow convergence rates on ill-conditioned problems. To address this challenge, we propose an accelerated high-index saddle dynamics (A-HiSD) by incorporating the heavy ball method. We prove the linear stability theory of the continuous A-HiSD, and subsequently estimate the local convergence rate for the discrete A-HiSD. Our analysis demonstrates that the A-HiSD method exhibits a faster convergence rate compared to the conventional HiSD method, especially when dealing with ill-conditioned problems. We also perform various numerical experiments including the loss function of neural network to substantiate the effectiveness and acceleration of the A-HiSD method.
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Cited by 1 Pith paper
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Neural Network-based High-index Saddle Dynamics Method for Searching Saddle Points and Solution Landscape
A neural network surrogate replaces the explicit energy function in high-index saddle dynamics, and nearby surrogate saddle points are proven to approximate true ones when the network is accurate.
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