A Randomized Algorithm for Nonconvex Minimization With Inexact Evaluations and Complexity Guarantees

We consider minimization of a smooth nonconvex function with inexact oracle access to gradient and Hessian (without assuming access to the function value) to achieve approximate second-order optimality. A novel feature of our method is that if an approximate direction of negative curvature is chosen...

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