A Newton-CG based barrier method for finding a second-order stationary point of nonconvex conic optimization with complexity guarantees

In this paper we consider finding an approximate second-order stationary point (SOSP) of nonconvex conic optimization that minimizes a twice differentiable function over the intersection of an affine subspace and a convex cone. In particular, we propose a Newton-conjugate gradient (Newton-CG) based barrier method for finding an $(\epsilon,\sqrt{\epsilon})$-SOSP of this problem. Our method is not only implementable, but also achieves an iteration complexity of ${\cal O}(\epsilon^{-3/2})$, which matches the best known iteration complexity of second-order methods for finding an $(\epsilon,\sqrt{\epsilon})$-SOSP of unconstrained nonconvex optimization. The operation complexity, consisting of ${\cal O}(\epsilon^{-3/2})$ Cholesky factorizations and $\widetilde{\cal O}(\epsilon^{{-3/2}\min{n,\epsilon{-1/4}})$} other fundamental operations, is also established for our method.