NIPS Proceedingsβ

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

Part of: Advances in Neural Information Processing Systems 31 (NIPS 2018) pre-proceedings

[PDF] [BibTeX] [Supplemental]

Authors

Conference Event Type: Oral

Abstract

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.