Conjugate gradient-type methods for linear systems with complex symmetric coefficient matrices
SIAM Journal on Scientific and Statistical Computing - Special issue on iterative methods in numerical linear algebra
Matrix computations (3rd ed.)
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
A Test Matrix Collection for Non-Hermitian Eigenvalue Problems
A Test Matrix Collection for Non-Hermitian Eigenvalue Problems
An extension of the conjugate residual method to nonsymmetric linear systems
Journal of Computational and Applied Mathematics
Lanczos-type variants of the COCR method for complex nonsymmetric linear systems
Journal of Computational Physics
A new family of global methods for linear systems with multiple right-hand sides
Journal of Computational and Applied Mathematics
The BiCOR and CORS Iterative Algorithms for Solving Nonsymmetric Linear Systems
SIAM Journal on Scientific Computing
Solution of generalized shifted linear systems with complex symmetric matrices
Journal of Computational Physics
A generalized product-type BiCOR method and its application in signal deconvolution
Computers & Mathematics with Applications
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The Conjugate Orthogonal Conjugate Gradient (COCG) method has been recognized as an attractive Lanczos-type Krylov subspace method for solving complex symmetric linear systems; however, it sometimes shows irregular convergence behavior in practical applications. In the present paper, we propose a Conjugate A-Orthogonal Conjugate Residual (COCR) method, which can be regarded as an extension of the Conjugate Residual (CR) method. Numerical examples show that COCR often gives smoother convergence behavior than COCG.