Semi-implicit method of high-index saddle dynamics and application to construct solution landscape

TL;DR

This paper introduces a semi-implicit numerical scheme for high-index saddle dynamics that improves convergence and efficiency in constructing solution landscapes, with rigorous error analysis and extensive numerical validation.

Contribution

It develops and analyzes a semi-implicit scheme for high-index saddle dynamics, addressing numerical challenges and proving error estimates, which enhances the construction of solution landscapes.

Findings

Semi-implicit scheme accelerates convergence compared to explicit methods.

Error estimates and convergence rates are rigorously established.

Numerical experiments confirm the scheme's efficiency and accuracy.

Abstract

We analyze the semi-implicit scheme of high-index saddle dynamics, which provides a powerful numerical method for finding the any-index saddle points and constructing the solution landscape. Compared with the explicit schemes of saddle dynamics, the semi-implicit discretization relaxes the step size and accelerates the convergence, but the corresponding numerical analysis encounters new difficulties compared to the explicit scheme. Specifically, the orthonormal property of the eigenvectors at each time step could not be fully employed due to the semi-implicit treatment, and computations of the eigenvectors are coupled with the orthonormalization procedure, which further complicates the numerical analysis. We address these issues to prove error estimates of the semi-implicit scheme via, e.g. technical splittings and multi-variable circulating induction procedure. We further analyze the convergence rate of the generalized minimum residual solver for solving the semi-implicit system. Extensive numerical experiments are carried out to substantiate the efficiency and accuracy of the semi-implicit scheme in constructing solution landscapes of complex systems.