Acceleration strategies for restarted minimum residual methods

This talk reviews some recent joint work with Michael Eiermann and Olaf

Schneider which introduced a framework for analyzing some popular

techniques for accelerating restarted Krylov subspace methods for

solving linear systems of equations. Such techniques attempt to compensate

for the loss of information due to restarting methods like GMRES, the

memory demands of which are usually too high for it to be applied to

large problems in unmodified form. We summarize the basic strategies which

have been proposed and present both theoretical and numerical comparisons.