Further analysis of minimum residual iterations

The convergence behavior of a number of algorithms based on minimizing residual norms over Krylov subspaces, is not well understood. Residual or error bounds currently available are either too loose or depend on unknown constants which can be very large. In this paper we take another look at traditional as well as alternative ways of obtaining upper bounds on residual norms. In particular, we derive new inequalities which utilize Chebyshev polynomials and compare them with standard inequalities.