Analysis of Krylov Subspace Solutions of Regularized Non-Convex Quadratic Problems

Part of Advances in Neural Information Processing Systems 31 (NeurIPS 2018)

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Yair Carmon, John C. Duchi


We provide convergence rates for Krylov subspace solutions to the trust-region and cubic-regularized (nonconvex) quadratic problems. Such solutions may be efficiently computed by the Lanczos method and have long been used in practice. We prove error bounds of the form $1/t^2$ and $e^{-4t/\sqrt{\kappa}}$, where $\kappa$ is a condition number for the problem, and $t$ is the Krylov subspace order (number of Lanczos iterations). We also provide lower bounds showing that our analysis is sharp.