Abstract
This paper proposes and analyzes an iterative minimization formulation for searching index-1 saddle points of an energy function. We give a general and rigorous description of eigenvector-following methodology in this iterative scheme by considering an auxiliary optimization problem at each iteration in which the new objective function is locally defined near the current guess. We prove that this scheme has a quadratic local convergence rate in terms of number of iterations, in comparison to the linear rate of the gentlest ascent dynamics [W. E and X. Zhou, Nonlinearity, 24 (2011), pp. 1831--1842] and many other existing methods. We also propose the generalization of the new methodology for saddle points of higher index and for constrained energy functions on the manifold. Preliminary numerical results on the nature of this iterative minimization formulation are presented.
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