  We consider minimization of indefinite quadratics with either trust-region
(norm) constraints or cubic regularization. Despite the nonconvexity of these
problems we prove that, under mild assumptions, gradient descent converges to
their global solutions, and give a non-asymptotic rate of convergence for the
cubic variant. We also consider Krylov subspace solutions and establish sharp
convergence guarantees to the solutions of both trust-region and
cubic-regularized problems. Our rates mirror the behavior of these methods on
convex quadratics and eigenvector problems, highlighting their scalability.
When we use Krylov subspace solutions to approximate the cubic-regularized
Newton step, our results recover the strongest known convergence guarantees to
approximate second-order stationary points of general smooth nonconvex
functions.

Optimization and Control