basic_SSOR: Symmetric Successive Over-Relaxation method
In SolveLS: Iterative Methods for Solving Linear System of Equations

Description Usage Arguments Value References Examples

Symmetric Successive Over-Relaxation(SSOR) method is a variant of Gauss-Seidel method for solving a system of linear equations, with a decomposition A = D+L+U where D is a diagonal matrix and L and U are strictly lower/upper triangular matrix respectively. For a square matrix A, it is required to be diagonally dominant or symmetric and positive definite like GS method. 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.

1 2	lsolve.ssor(A, B, xinit = NA, reltol = 1e-05, maxiter = 1000, w = 1, adjsym = TRUE, verbose = TRUE)

`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.
`w`	a weight value in (0,2).; `w=1` leads to Gauss-Seidel method.
`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.

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.

\insertRef

demmel_applied_1997SolveLS

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

out1 = lsolve.ssor(A,b)
out2 = lsolve.ssor(A,b,w=0.5)
out3 = lsolve.ssor(A,b,w=1.5)
matout = cbind(matrix(x),out1$x, out2$x, out3$x);
colnames(matout) = c("true x","SSOR w=1", "SSOR w=0.5", "SSOR w=1.5")
print(matout)