"Automatic Preconditioning by Limited Memory Quasi-Newton Updating"
J.L. Morales, J. Nocedal.
SIAM J. Optimization, Vol. 10, No. 4, pp. 1079-1096 (2000)

The paper proposes a preconditioner for the conjugate gradient method (CG) that is designed for solving systems of equations with different right hand side vectors, or for solving a sequence of slowly varying systems. The preconditioner has the form of a limited memory quasi-Newton matrix and is generated using information from the CG iteration. The automatic preconditioner does not require explicit knowledge of the coefficient matrix A and is therefore suitable for problems where only products of A times a vector can be computed. Numerical experiments indicate that the preconditioner has most to offer when these matrix-vector products are expensive to compute, and when low accuracy in the solution is required. The effectiveness of the preconditioner is tested within a Hessian-free Newton method for optimization, and by solving certain linear systems arising in finite element models.
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