# gf_linsolve¶

Synopsis

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)


Description :

Various linear system solvers.

Command list :

X = gf_linsolve('gmres', spmat M, vec b[, int restart][, precond P][,'noisy'][,'res', r][,'maxiter', n])

Solve <literal>M.X = b</literal> with the generalized minimum residuals method.

Optionally using <literal>P</literal> 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 <literal>M.X = b</literal> with the conjugated gradient method.

Optionally using <literal>P</literal> as preconditioner.

X = gf_linsolve('bicgstab', spmat M, vec b [, precond P][,'noisy'][,'res', r][,'maxiter', n])

Solve <literal>M.X = b</literal> with the bi-conjugated gradient stabilized method.

Optionally using <literal>P</literal> 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 <literal>M.U = b</literal> apply the SuperLU solver (sparse LU factorization).

The condition number estimate <literal>cond</literal> is returned with the solution <literal>U</literal>.

{U, cond} = gf_linsolve('mumps', spmat M, vec b)

Solve <literal>M.U = b</literal> using the MUMPS solver.