Various linear system solvers.
X = gf_linsolve('gmres', spmat M, vec b[, int restart][, precond P][,'noisy'][,'res', r][,'maxiter', n])
X = gf_linsolve('cg', spmat M, vec b [, precond P][,'noisy'][,'res', r][,'maxiter', n])
X = gf_linsolve('bicgstab', spmat M, vec b [, precond P][,'noisy'][,'res', r][,'maxiter', n])
{U, cond} = gf_linsolve('lu', spmat M, vec b)
{U, cond} = gf_linsolve('superlu', spmat M, vec b)
{U, cond} = gf_linsolve('mumps', spmat M, vec b)
Various linear system solvers.
X = gf_linsolve('gmres', spmat M, vec b[, int restart][, precond P][,'noisy'][,'res', r][,'maxiter', n])
Solve M.X = b
with the generalized minimum residuals method.
Optionally using P
as preconditioner. The default value of the
restart parameter is 50.
X = gf_linsolve('cg', spmat M, vec b [, precond P][,'noisy'][,'res', r][,'maxiter', n])
Solve M.X = b
with the conjugated gradient method.
Optionally using P
as preconditioner.
X = gf_linsolve('bicgstab', spmat M, vec b [, precond P][,'noisy'][,'res', r][,'maxiter', n])
Solve M.X = b
with the bi-conjugated gradient stabilized method.
Optionally using P
as a preconditioner.
{U, cond} = gf_linsolve('lu', spmat M, vec b)
Alias for gf_linsolve('superlu',...)
{U, cond} = gf_linsolve('superlu', spmat M, vec b)
Solve M.U = b
apply the SuperLU solver (sparse LU factorization).
The condition number estimate cond
is returned with the solution U
.
{U, cond} = gf_linsolve('mumps', spmat M, vec b)
Solve M.U = b
using the MUMPS solver.
Y. Collette