krylov_BICGSTAB: Biconjugate Gradient Stabilized Method

Description Usage Arguments Value References Examples

Description

Biconjugate Gradient Stabilized(BiCGSTAB) method is a stabilized version of Biconjugate Gradient method 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

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lsolve.bicgstab(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

van_der_vorst_bi-cgstab:_1992SolveLS

Examples

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## 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.bicgstab(A,b)
matout = cbind(matrix(x),out1$x, out2$x, out3$x);
colnames(matout) = c("true x","CG result", "BiCG result", "BiCGSTAB result")
print(matout)

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