Asymptotic convergence of restarted Anderson acceleration for certain normal linear systems
Oliver A. Krzysik, Hans De Sterck, Adam Smith

TL;DR
This paper provides a theoretical analysis of a restarted Anderson acceleration method for certain linear systems, showing it converges faster than the basic fixed-point iteration and exploring how initial conditions affect convergence.
Contribution
It introduces a theoretical framework for the convergence of a restarted Anderson acceleration variant on specific linear systems, relating it to eigenvalue problems and power iterations.
Findings
Convergence factor is strictly smaller than the fixed-point iteration.
For symmetric matrices, convergence depends on the initial iterate.
For skew-symmetric matrices, convergence is independent of initial conditions.
Abstract
Anderson acceleration (AA) is widely used for accelerating the convergence of an underlying fixed-point iteration , , with , . Despite AA's widespread use, relatively little is understood theoretically about the extent to which it may accelerate the underlying fixed-point iteration. To this end, we analyze a restarted variant of AA with a restart size of one, a method closely related to GMRES(1). We consider the case of with matrix either symmetric or skew-symmetric. For both classes of we compute the worst-case root-average asymptotic convergence factor of the AA method, partially relying on conjecture in the symmetric setting, proving that it is strictly smaller than that of the…
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Taxonomy
TopicsModel Reduction and Neural Networks · Matrix Theory and Algorithms · Numerical methods for differential equations
