Roundoff-error analysis of a new class of conjugate-gradient algorithms

We perform the rounding-error analysis of the conjugate-gradient algorithms for the solution of a large system of linear equations Ax=b where Ais an hermitian and positive definite matrix. We propose a new class of conjugate-gradient algorithms and prove that in the spectral norm the relative error of the computed sequence {x_k} (in floating-point arithmetic) depends at worst on ζк^{$\text{3}\text{2}$}, where ζ is the relative computer precision and к is the condition number of A. We show that the residual vectors r_k=Ax_k-b are at worst of order ζк?vA?v ?vx_k?v. We p oint out that with iterative refinement these algorithms are numerically stable. If ζк ² is at most of order unity, then they are also well behaved.