Towards Robust Calculation of index-$k$ Saddle Point: Iterative Proximal-Minimization and Differential Game Model

TL;DR

This paper introduces a novel differential game approach with a robust iterative proximal-minimization algorithm for accurately computing index-$k$ saddle points on potential energy surfaces, demonstrating improved robustness and efficiency.

Contribution

It presents a new differential game interpretation and a robust iterative proximal-minimization algorithm for saddle point computation, with theoretical guarantees and practical advantages.

Findings

The Nash equilibrium of the game equals the saddle point.

The new algorithm is more robust than previous methods.

Numerical tests confirm improved robustness and efficiency.

Abstract

Saddle point with a given Morse index on a potential energy surface is an important object related to energy landscape in physics and chemistry. Efficient numerical methods based on iterative minimization formulation have been proposed in the forms of the sequence of minimization subproblems or the continuous dynamics. We here present a differential game interpretation of this formulation and theoretically investigate the Nash equilibrium of the proposed game and the original saddle point on potential energy surface. To define this differential game, a new proximal function growing faster than quadratic is introduced to the cost function in the game and a robust Iterative Proximal-Minimization algorithm (IPM) is then derived to compute the saddle points. We prove that the Nash equilibrium of the game is exactly the saddle point in concern and show that the new algorithm is more robust than the previous iterative minimization algorithm without proximity, while the same convergence rate and the computational cost still hold. A two dimensional problem and the Cahn-Hillard problem are tested to demonstrate this numerical advantage.