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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lsolve_CHEBY.R
\name{lsolve.cheby}
\alias{lsolve.cheby}
\title{Chebyshev Method}
\usage{
lsolve.cheby(
A,
B,
xinit = NA,
reltol = 1e-05,
maxiter = 10000,
preconditioner = diag(ncol(A)),
adjsym = TRUE,
verbose = TRUE
)
}
\arguments{
\item{A}{an \eqn{(m\times n)} dense or sparse matrix. See also \code{\link[Matrix]{sparseMatrix}}.}
\item{B}{a vector of length \eqn{m} or an \eqn{(m\times k)} matrix (dense or sparse) for solving \eqn{k} systems simultaneously.}
\item{xinit}{a length-\eqn{n} vector for initial starting point. \code{NA} to start from a random initial point near 0.}
\item{reltol}{tolerance level for stopping iterations.}
\item{maxiter}{maximum number of iterations allowed.}
\item{preconditioner}{an \eqn{(n\times n)} preconditioning matrix; default is an identity matrix.}
\item{adjsym}{a logical; \code{TRUE} to symmetrize the system by transforming the system into normal equation, \code{FALSE} otherwise.}
\item{verbose}{a logical; \code{TRUE} to show progress of computation.}
}
\value{
a named list containing \describe{
\item{x}{solution; a vector of length \eqn{n} or a matrix of size \eqn{(n\times k)}.}
\item{iter}{the number of iterations required.}
\item{errors}{a vector of errors for stopping criterion.}
}
}
\description{
Chebyshev method - also known as Chebyshev iteration - avoids computation of inner product,
enabling distributed-memory computation to be more efficient at the cost of requiring
a priori knowledge on the range of spectrum for matrix \code{A}.
}
\examples{
## Overdetermined System
set.seed(100)
A = matrix(rnorm(10*5),nrow=10)
x = rnorm(5)
b = A\%*\%x
out1 = lsolve.sor(A,b,w=0.5)
out2 = lsolve.cheby(A,b)
matout = cbind(x, out1$x, out2$x);
colnames(matout) = c("original x","SOR result", "Chebyshev result")
print(matout)
}
\references{
\insertRef{gutknecht_chebyshev_2002}{Rlinsolve}
}
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