Analysis of Krylov Subspace Solutions of Regularized Nonconvex Quadratic Problems
Yair Carmon, John C. Duchi
Abstract
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Yair Carmon, John C. Duchi
Abstract
Open-access reader
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 $e^{-4t/\sqrtκ}$, where $κ$ 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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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 $e^{-4t/\sqrtκ}$, where $κ$ 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.
Key concepts: Krylov subspace, Lanczos resampling, Mathematics, Subspace topology, Applied mathematics, Quadratic equation, Convergence (economics), Lanczos algorithm