krylov_QMR: Quasi Minimal Residual Method

In SolveLS: Iterative Methods for Solving Linear System of Equations

Description

Quasia-Minimal Resudial(QMR) method is another remedy of the BiCG which shows rather irregular convergence behavior. It adapts to solve the reduced tridiagonal system in a least squares sense and its convergence is known to be quite smoother than BiCG.

Usage

lsolve.qmr(A, B, xinit = NA, reltol = 1e-05, maxiter = 1000,
  preconditioner = diag(ncol(A)), verbose = TRUE)

Arguments

A	an (m\times n) dense or sparse matrix. See also sparseMatrix.
B	a vector of length m or an (m\times k) matrix (dense or sparse) for solving k systems simultaneously.
xinit	a length-n vector for initial starting point. NA to start from a random initial point near 0.
reltol	tolerance level for stopping iterations.
maxiter	maximum number of iterations allowed.
preconditioner	an (n\times n) preconditioning matrix; default is an identity matrix.
verbose	a logical; TRUE to show progress of computation.

Value

a named list containing

x

solution; a vector of length n or a matrix of size (n\times k).

iter

the number of iterations required.

errors

a vector of errors for stopping criterion.

References

\insertRef

freund_qmr:_1991SolveLS

Examples

## Overdetermined System
A = matrix(rnorm(10*5),nrow=10)
x = rnorm(5)
b = A%*%x

out1 = lsolve.cg(A,b)
out2 = lsolve.bicg(A,b)
out3 = lsolve.qmr(A,b)
matout = cbind(matrix(x),out1$x, out2$x, out3$x);
colnames(matout) = c("true x","CG result", "BiCG result", "QMR result")
print(matout)

SolveLS documentation built on Feb. 12, 2018, 5:03 p.m.