A Stochastic Trust Region Method for Non-convex Minimization
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We target the problem of finding a local minimum in non-convex finite-sum minimization. Towards this goal, we first prove that the trust region method with inexact gradient and Hessian estimation can achieve a convergence rate of order O(1/k^2/3) as long as those differential estimations are sufficiently accurate. Combining such result with a novel Hessian estimator, we propose the sample-efficient stochastic trust region (STR) algorithm which finds an (ϵ, √(ϵ))-approximate local minimum within O(√(n)/ϵ^1.5) stochastic Hessian oracle queries. This improves state-of-the-art result by O(n^1/6). Experiments verify theoretical conclusions and the efficiency of STR.
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