  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.
