Convergence of block iterative methods applied to sparse least-squares problems

Convergence of iterative methods applied to Burgers-Huxley equation

LSMR: An iterative algorithm for sparse least-squares problems

An iterative method LSMR is presented for solving linear systems Ax = b and leastsquares problems min ‖Ax−b‖2, with A being sparse or a fast linear operator. LSMR is based on the Golub-Kahan bidiagonalization process. It is analytically equivalent to the MINRES method applied to the normal equation ATAx = ATb, so that the quantities ‖Ark‖ are monotonically decreasing (where rk = b−Axk is the re...

Convergence of Inner-Iteration GMRES Methods for Least Squares Problems

We develop a general convergence theory for the generalized minimal residual method for least squares problems preconditioned with inner iterations. The inner iterations are performed by stationary iterative methods. We also present theoretical justifications for using specific inner iterations such as the Jacobi and SOR-type methods. The theory is improved particularly in the rankdeficient cas...

Preconditioned Iterative Methods for Solving Linear Least Squares Problems

New preconditioning strategies for solving m × n overdetermined large and sparse linear least squares problems using the CGLS method are described. First, direct preconditioning of the normal equations by the Balanced Incomplete Factorization (BIF) for symmetric and positive definite matrices is studied and a new breakdown-free strategy is proposed. Preconditioning based on the incomplete LU fa...

Preconditioned Iterative Methods for Weighted Toeplitz Least Squares Problems

We consider the iterative solution of weighted Toeplitz least squares problems. Our approach is based on an augmented system formulation. We focus our attention on two types of preconditioners: a variant of constraint preconditioning, and the Hermitian/skew-Hermitian splitting (HSS) preconditioner. Bounds on the eigenvalues of the preconditioned matrices are given in terms of problem and algori...