code: def xstep(self):
          r
  
          ngsit = 0
          gsrrs = np.inf
          YU = self.Y - self.U
          SYU = self.S + YU[..., -1]
          YU[..., -1] = 0.0
          ATYU = self.cnst_AT(YU)
          while gsrrs > self.opt['GSTol'] and ngsit < self.opt['MaxGSIter']:
              self.X = self.GaussSeidelStep(
                  SYU, self.X, ATYU, 1.0, self.lcw, 1.0)
              gsrrs = sl.rrs(
                  self.cnst_AT(self.cnst_A(self.X)),
                  self.cnst_AT(self.cnst_c() - self.cnst_B(self.Y) - self.U)
              )
              ngsit += 1
  
          self.xs = (ngsit, gsrrs)
docs: r"""Minimise Augmented Lagrangian with respect to
          :math:`\mathbf{x}`.