New retraction-based backtracking gradient and Newton-type algorithms on Riemannian manifolds and Banach spaces are claimed to converge to local minima and to avoid saddle points for random starting points.
Backtracking New Q-Newton's method, Schr\"oder's theorem, and Linear Conjugacy
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abstract
A new variant of Newton's method - named Backtracking New Q-Newton's method (BNQN) - which has strong theoretical guarantee, is easy to implement, and has good experimental performance, was recently introduced by the third author. Experiments performed previously showed some remarkable properties of the basins of attractions for finding roots of polynomials and meromorphic functions using BNQN. In particular, it seems that for finding roots of polynomials of degree 2, the basins of attraction of the dynamics for BNQN are the same as that for Newton's method (the latter is the classical Schr\"oder's result in Complex Dynamics). In this paper, we show that indeed the picture we obtain when finding roots of polynomials of degree 2 is the same as that in Sch\"oder's result, with a remarkable difference: on the boundary line of the basins, the dynamics of Newton's method is chaotic, while the dynamics of BNQN is more smooth. On the way to proving the result, we show that BNQN (in any dimension) is invariant under conjugation by linear operators of the form $A=cR$, where $R$ is unitary and $c>0$ a constant. This again illustrates the similarity-difference relation between BNQN and Newton's method.
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Some iterative algorithms on Riemannian manifolds and Banach spaces with good global convergence guarantee
New retraction-based backtracking gradient and Newton-type algorithms on Riemannian manifolds and Banach spaces are claimed to converge to local minima and to avoid saddle points for random starting points.