Poster
Analysis of Krylov Subspace Solutions of Regularized Non-Convex Quadratic Problems
Yair Carmon · John Duchi
Room 210 #48
Keywords: [ Optimization ] [ Non-Convex Optimization ] [ Algorithms ]
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Abstract
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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.
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