lsolve.bicg {Rlinsolve} | R Documentation |
Biconjugate Gradient method
Description
Biconjugate Gradient(BiCG) method is a modification of Conjugate Gradient for nonsymmetric systems using
evaluations with respect to A^T
as well as A
in matrix-vector multiplications.
For an overdetermined system where nrow(A)>ncol(A)
,
it is automatically transformed to the normal equation. Underdetermined system -
nrow(A)<ncol(A)
- is not supported. Preconditioning matrix M
, in theory, should be symmetric and positive definite
with fast computability for inverse, though it is not limited until the solver level.
Usage
lsolve.bicg(
A,
B,
xinit = NA,
reltol = 1e-05,
maxiter = 10000,
preconditioner = diag(ncol(A)),
verbose = TRUE
)
Arguments
A |
an |
B |
a vector of length |
xinit |
a length- |
reltol |
tolerance level for stopping iterations. |
maxiter |
maximum number of iterations allowed. |
preconditioner |
an |
verbose |
a logical; |
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
Fletcher R (1976). “Conjugate gradient methods for indefinite systems.” In Watson GA (ed.), Numerical Analysis, volume 506, 73–89. Springer Berlin Heidelberg, Berlin, Heidelberg. ISBN 978-3-540-07610-0 978-3-540-38129-7.
Voevodin VV (1983). “The question of non-self-adjoint extension of the conjugate gradients method is closed.” USSR Computational Mathematics and Mathematical Physics, 23(2), 143–144. ISSN 00415553.
Examples
## Overdetermined System
set.seed(100)
A = matrix(rnorm(10*5),nrow=10)
x = rnorm(5)
b = A%*%x
out1 = lsolve.cg(A,b)
out2 = lsolve.bicg(A,b)
matout = cbind(matrix(x),out1$x, out2$x);
colnames(matout) = c("true x","CG result", "BiCG result")
print(matout)