1,761 research outputs found
A biconjugate gradient type algorithm on massively parallel architectures
The biconjugate gradient (BCG) method is the natural generalization of the classical conjugate gradient algorithm for Hermitian positive definite matrices to general non-Hermitian linear systems. Unfortunately, the original BCG algorithm is susceptible to possible breakdowns and numerical instabilities. Recently, Freund and Nachtigal have proposed a novel BCG type approach, the quasi-minimal residual method (QMR), which overcomes the problems of BCG. Here, an implementation is presented of QMR based on an s-step version of the nonsymmetric look-ahead Lanczos algorithm. The main feature of the s-step Lanczos algorithm is that, in general, all inner products, except for one, can be computed in parallel at the end of each block; this is unlike the other standard Lanczos process where inner products are generated sequentially. The resulting implementation of QMR is particularly attractive on massively parallel SIMD architectures, such as the Connection Machine
Deflated GMRES for Systems with Multiple Shifts and Multiple Right-Hand Sides
We consider solution of multiply shifted systems of nonsymmetric linear
equations, possibly also with multiple right-hand sides. First, for a single
right-hand side, the matrix is shifted by several multiples of the identity.
Such problems arise in a number of applications, including lattice quantum
chromodynamics where the matrices are complex and non-Hermitian. Some Krylov
iterative methods such as GMRES and BiCGStab have been used to solve multiply
shifted systems for about the cost of solving just one system. Restarted GMRES
can be improved by deflating eigenvalues for matrices that have a few small
eigenvalues. We show that a particular deflated method, GMRES-DR, can be
applied to multiply shifted systems. In quantum chromodynamics, it is common to
have multiple right-hand sides with multiple shifts for each right-hand side.
We develop a method that efficiently solves the multiple right-hand sides by
using a deflated version of GMRES and yet keeps costs for all of the multiply
shifted systems close to those for one shift. An example is given showing this
can be extremely effective with a quantum chromodynamics matrix.Comment: 19 pages, 9 figure
Conjugate gradient type methods for linear systems with complex symmetric coefficient matrices
We consider conjugate gradient type methods for the solution of large sparse linear system Ax equals b with complex symmetric coefficient matrices A equals A(T). Such linear systems arise in important applications, such as the numerical solution of the complex Helmholtz equation. Furthermore, most complex non-Hermitian linear systems which occur in practice are actually complex symmetric. We investigate conjugate gradient type iterations which are based on a variant of the nonsymmetric Lanczos algorithm for complex symmetric matrices. We propose a new approach with iterates defined by a quasi-minimal residual property. The resulting algorithm presents several advantages over the standard biconjugate gradient method. We also include some remarks on the obvious approach to general complex linear systems by solving equivalent real linear systems for the real and imaginary parts of x. Finally, numerical experiments for linear systems arising from the complex Helmholtz equation are reported
On choice of preconditioner for minimum residual methods for nonsymmetric matrices
Existing convergence bounds for Krylov subspace methods such as GMRES for nonsymmetric linear systems give little mathematical guidance for the choice of preconditioner. Here, we establish a desirable mathematical property of a preconditioner which guarantees that convergence of a minimum residual method will essentially depend only on the eigenvalues of the preconditioned system, as is true in the symmetric case. Our theory covers only a subset of nonsymmetric coefficient matrices but computations indicate that it might be more generally applicable
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