Universality for the conjugate gradient and MINRES algorithms on sample covariance matrices

Elliot Paquette; Thomas Trogdon

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arXiv · 2007.00640

Universality for the conjugate gradient and MINRES algorithms on sample covariance matrices

Elliot Paquette·Thomas Trogdon

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Abstract

We present a probabilistic analysis of two Krylov subspace methods for solving linear systems. We prove a central limit theorem for norms of the residual vectors that are produced by the conjugate gradient and MINRES algorithms when applied to a wide class of sample covariance matrices satisfying some standard moment conditions. The proof involves establishing a four moment theorem for the so-called spectral measure, implying, in particular, universality for the matrix produced by the Lanczos iteration. The central limit theorem then implies an almost-deterministic iteration count for the iterative methods in question.

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Elliot Paquette, Thomas Trogdon. 2020-07-01. Universality for the conjugate gradient and MINRES algorithms on sample covariance matrices. https://arxiv.org/abs/2007.00640

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