  We investigate the method of conjugate gradients, exploiting inaccurate
matrix-vector products, for the solution of convex quadratic optimization
problems. Theoretical performance bounds are derived, and the necessary
quantities occurring in the theoretical bounds estimated, leading to a
practical algorithm. Numerical experiments suggest that this approach has
significant potential, including in the steadily more important context of
multi-precision computations
