lsolve.cheby {Rlinsolve}

Chebyshev Method

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 A.

Usage

lsolve.cheby(
  A,
  B,
  xinit = NA,
  reltol = 1e-05,
  maxiter = 10000,
  preconditioner = diag(ncol(A)),
  adjsym = TRUE,
  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.

adjsym

a logical; TRUE to symmetrize the system by transforming the system into normal equation, FALSE otherwise.

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

Gutknecht MH, Röllin S (2002). “The Chebyshev iteration revisited.” Parallel Computing, 28(2), 263–283. ISSN 01678191.

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)