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On the Optimal Solution of Large Linear Systems

Traub, Joseph F.; Wozniakowski, Henryk

The information-based study of the optimal solution of large linear systems is initiated by studying the case of Krylov information. Among the algorithms which use Krylov information are minimal residual, conjugate gradient, Chebyshev, and successive approximation algorithms. A "sharp" lower bound on the number of matrix-vector multiplications required to compute an E- approximation is obtained for any orthogonally invariant class of matrices. Examples of such classes include many of practical interest such as symmetric matrices, symmetric positive definite matrices, and matrices with bounded condition number. It is shown that the minimal residual algorithm is within at most one matrix-vector multiplication of the lower bound. A similar result is obtained for the generalized minimal residual algorithm. The lower bound is computed for certain classes of orthogonally invariant matrices. We show how the lack of certain properties (symmetry, positive definiteness) increases the lower bound. A conjecture and a number of open problems are stated.



More About This Work

Academic Units
Computer Science
Department of Computer Science, Columbia University
Columbia University Computer Science Technical Reports, CUCS-030-82
Published Here
October 25, 2011