title: A regularized conjugate gradient method for symmetric positive definite system of linear equations
abstract: A class of regularized conjugate gradient methods is presented for solving the large sparse system of linear equations of which the coefficient matrix is an ill-conditioned symmetric positive definite matrix. The convergence properties of these methods are discussed in depth, and the best possible choices of the parameters involved in the new methods are investigated in detail. Numerical computations show that the new methods are more efficient and robust than both classical relaxation methods and classical conjugate direction methods
keyphrases: regularized conjugate gradient method symmetric positive definite system linear equations large sparse system coefficient matrix convergence properties classical relaxation methods classical conjugate direction methods ill-conditioned linear system