An incomplete generalized minimum backward perturbation algorithm for large nonsymmetric linear systems

This paper gives the truncated version of the generalized minimum backward error algorithm(GMBACK)—the incomplete generalized minimum backward perturbation algorithm(IGMBACK)for large nonsymmetric linear systems.It is based on an incomplete orthogonalization of the Krylov vectors in question,and gives an approximate or quasi-minimum backward perturbation solution over the Krylov subspace.Theoretical properties of IGMBACK including finite termination,existence and uniqueness are discussed in details,and practical implementation issues associated with the IGMBACK algorithm are considered.Numerical experiments show that,the IGMBACK method is usually more efficient than GMBACK and GMRES,and IMBACK,GMBACK often have better convergence performance than GMRES.Specially,for sensitive matrices and right-hand sides being parallel to the left singular vectors corresponding to the smallest singular values of the coefficient matrices,GMRES does not necessarily converge,and IGMBACK,GMBACK usually converge and outperform GMRES.

THE RESTRICTIVELY PRECONDITIONED CONJUGATE GRADIENT METHODS ON NORMAL RESIDUAL FOR BLOCK TWO-BY-TWO LINEAR SYSTEMS

The restrictively preconditioned conjugate gradient （RPCG） method is further developed to solve large sparse system of linear equations of a block two-by-two structure. The basic idea of this new approach is that we apply the RPCG method to the normal-residual equation of the block two-by-two linear system and construct each required approximate matrix by making use of the incomplete orthogonal factorization of the involved matrix blocks. Numerical experiments show that the new method, called the restrictively preconditioned conjugate gradient on normal residual （RPCGNR）, is more robust and effective than either the known RPCG method or the standard conjugate gradient on normal residual （CGNR） method when being used for solving the large sparse saddle point problems.