Excerpt from Optimal Preconditioners of a Given Sparsity Pattern To make this bound small, it should be close to 1. This bound is sharp for the Chebyshev method, in the sense that there is an initial guess for which the bound will be attained at every step. It is not sharp for the conjugate gradient method. A sharp error bound for the conjugate gradient method is more complicated involving the distribution of all eigen values of m'la, but a condition number1 x(ma) close to 1 is sufficient to ensure fast con vergence of ...
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Excerpt from Optimal Preconditioners of a Given Sparsity Pattern To make this bound small, it should be close to 1. This bound is sharp for the Chebyshev method, in the sense that there is an initial guess for which the bound will be attained at every step. It is not sharp for the conjugate gradient method. A sharp error bound for the conjugate gradient method is more complicated involving the distribution of all eigen values of m'la, but a condition number1 x(ma) close to 1 is sufficient to ensure fast con vergence of this algorithm as well, even when the effects of finite precision arithmetic are taken into account Therefore, we will define optimality in terms of the condition number k(m-1a) and minimization of this quantity will be our goal. About the Publisher Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at ... This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
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