Generalized Conjugate Gradient Squareddiederik

The Conjugate Gradient Squared (CGS) is a well-known and widely used iterative method for solving non-symmetric linear systems of equations. In practice the method converges fast, often twice as fast as the Bi-Conjugate Gradient (Bi-CG) method. However, during the iteration large residual norms may appear, which may lead to inaccurate approximate solutions or may even deteriorate the convergence rate. Instead of squaring the Bi-CG polynomial as in CGS, we propose to consider products of two nearby Bi-CG polynomials which leads to a generalized CGS algorithm, of which CGS is just a particular case. From a number of numerical experiments we conclude that this may lead to more accurate approximations and faster convergence.