gf_linsolve — The linear solver
gf_linsolve('gmres', spmat M, vec b [, int restart=50][, precond P][, 'noisy'][,'res', r][,'maxiter', n])
gf_linsolve('cg', spmat M, vec b [, precond P][, 'noisy'][,'res', r][,'maxiter', n])
gf_linsolve('bicgstab', spmat M, vec b [, precond P][, 'noisy'][,'res', r][,'maxiter', n])[U,cond] = gf_linsolve('lu'|'superlu', spmat M, vec b [, precond P])
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.
list(U, cond) = gf_linsolve('lu',Spmat M, vec b) Alias for gf_linsolve('superlu',...)
list(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.