Yet another fast variant of Newton’s method for nonconvex optimization


A second-order algorithm is proposed for minimizing smooth nonconvex
functions that alternates between regularized Newton and negative
curvature steps. In most cases, the Hessian matrix is regularized with
the square root of the current gradient and an additional term taking
moderate negative curvature into account, a negative curvature step
being taken only exceptionnally. As a consequence, the proposed
method only requires the solution of a single linear system
at nearly all iterations. We establish that at most
$\mathcal{O}\left( |\log\epsilon|\,\epsilon^{-3/2}\right)$
evaluations of the problem's objective function and derivatives are needed
for this algorithm to obtain an $\epsilon$-approximate first-order
minimizer, and at most
to obtain a second-order one. Initial numerical experiments with two
variants of the new method are finally presented.



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