Feb. 13, 2024, 5:45 a.m. | Camille Castera

cs.LG updates on arXiv.org arxiv.org

We study the asymptotic behavior of second-order algorithms mixing Newton's method and inertial gradient descent in non-convex landscapes. We show that, despite the Newtonian behavior of these methods, they almost always escape strict saddle points. We also evidence the role played by the hyper-parameters of these methods in their qualitative behavior near critical points. The theoretical results are supported by numerical illustrations.

algorithms behavior cs.lg evidence gradient math.oc near parameters role show study

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