Krylov Subspace Methods: Principles and Analysis

By J. Liesen, Zdenek Strakos. 2012

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A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems (2015, positive): “,Ak−1b] is replaced by an orthogonal basis robustly computed via an Arnoldi process [157, 212]).”
Randomized Iterative Methods for Linear Systems (2015, methodology): “3 Formulation (2.3) is similar to the framework often used to describe Krylov methods [20, Chapter 1], which is where K k +1 ⊂ R n is a ( k + 1)–dimensional subspace.”