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Minimal Residual Methods for Complex Symmetric, Skew Symmetric, and Skew Hermitian Systems

Sou-Cheng Choi(sctchoi***at***uchicago.edu)

Abstract: While there is no lack of efficient Krylov subspace solvers for Hermitian systems, there are few for complex symmetric, skew symmetric, or skew Hermitian systems, which are increasingly important in modern applications including quantum dynamics, electromagnetics, and power systems. For a large consistent complex symmetric system, one may apply a non-Hermitian Krylov subspace method disregarding the symmetry of $A$, or a Hermitian Krylov solver on the equivalent normal equation or an augmented system twice the original dimension. These have the disadvantages of increasing either memory, conditioning, or computational costs. An exception is a special version of QMR by Freund (1992), but that may be affected by non-benign breakdowns unless look-ahead is implemented; furthermore, it is designed for only consistent and nonsingular problems. For skew symmetric systems, Greif and Varah (2009) adapted CG for nonsingular skew symmetric linear systems that are necessarily and restrictively of even order. We extend the symmetric and Hermitian algorithms MINRES and MINRES-QLP by Choi, Paige and Saunders (2011) to complex symmetric, skew symmetric, and skew Hermitian systems. In particular, MINRES-QLP uses a rank-revealing QLP decomposition of the tridiagonal matrix from a three-term recurrent complex-symmetric Lanczos process. Whether the systems are real or complex, singular or invertible, compatible or inconsistent, MINRES-QLP computes the unique minimum-length, i.e., pseudoinverse, solutions. It is a significant extension of MINRES by Paige and Saunders (1975) with enhanced stability and capability.

Keywords: MINRES, MINRES-QLP, Krylov subspace method, Lanczos process, conjugate-gradient method, minimum-residual method, singular least-squares problem, sparse matrix, complex symmetric, skew symmetric, skew Hermitian, preconditioner, structured matrices

Category 1: Convex and Nonsmooth Optimization (Convex Optimization )

Category 2: Optimization Software and Modeling Systems

Category 3: Robust Optimization

Citation: Report ANL/MCS-P3028-0812, Computation Institute, University of Chicago, 2013.

Download: [PDF]

Entry Submitted: 05/07/2013
Entry Accepted: 05/07/2013
Entry Last Modified: 05/07/2013

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