next up previous contents index
Next: Generalized Minimal Residual Up: CG on the Previous: CG on the


If a system of linear equations has a nonsymmetric, possibly indefinite (but nonsingular), coefficient matrix, one obvious attempt at a solution is to apply Conjugate Gradient to a related symmetric positive definite system, . While this approach is easy to understand and code, the convergence speed of the Conjugate Gradient method now depends on the square of the condition number of the original coefficient matrix. Thus the rate of convergence of the CG procedure on the normal equations  may be slow.

Several proposals have been made to improve the numerical stability of this method. The best known is by Paige and Saunders [169] and is based upon applying the Lanczos method to the auxiliary system

A clever execution of this scheme delivers the factors and of the -decomposition of the tridiagonal matrix that would have been computed by carrying out the Lanczos procedure with .

Another means for improving the numerical stability of this normal equations approach is suggested by Björck and Elfving in [34]. The observation that the matrix is used in the construction of the iteration coefficients through an inner product like leads to the suggestion that such an inner product be replaced by .   

Jack Dongarra
Mon Nov 20 08:52:54 EST 1995