# Mixed formulation of the Poisson equation with a block-preconditioner/solver # noqa

This demo illustrates how to solve the Poisson equation using a mixed (two-field) formulation and a block-preconditioned iterative solver. In particular, it illustrates how to

- Use mixed and non-continuous finite element spaces.

- Set essential boundary conditions for subspaces and \(H(\mathrm{div})\) spaces.

- Construct a blocked linear system.

- Construct a block-preconditioned iterative linear solver using PETSc/petsc4y.

- Construct a Hypre Auxiliary Maxwell Space (AMS) preconditioner for \(H(\mathrm{div})\) problems in two-dimensions.

Download sources

- `Python script`

- `Jupyter notebook`

## Equation and problem definition

An alternative formulation of Poisson equation can be formulated by introducing an additional (vector) variable, namely the (negative) flux: \(\sigma = \nabla u\). The partial differential equations then read

\[\begin{split} \begin{align} \sigma - \nabla u &= 0 \quad {\rm in} \ \Omega, \\ \nabla \cdot \sigma &= - f \quad {\rm in} \ \Omega, \end{align} \end{split}\]

with boundary conditions

\[\begin{split} u = u_0 \quad {\rm on} \ \Gamma_{D}, \\ \sigma \cdot n = g \quad {\rm on} \ \Gamma_{N}. \end{split}\]

where \(n\) is the outward unit normal vector on the boundary. Looking at the variational form, we see that the boundary condition for the flux (\(\sigma \cdot n = g\)) is now an essential boundary condition (which should be enforced in the function space), while the other boundary condition (\(u = u_0\)) is a natural boundary condition (which should be applied to the variational form). Inserting the boundary conditions, this variational problem can be phrased in the general form: find \((\sigma, u) \in \Sigma_g \times V\) such that

\[ a((\sigma, u), (\tau, v)) = L((\tau, v)) \quad \forall \ (\tau, v) \in \Sigma_0 \times V, \]

where the variational forms \(a\) and \(L\) are defined as

\[\begin{split} a((\sigma, u), (\tau, v)) &:= \int_{\Omega} \sigma \cdot \tau + \nabla \cdot \tau \ u + \nabla \cdot \sigma \ v \ {\rm d} x, \\ L((\tau, v)) &:= - \int_{\Omega} f v \ {\rm d} x + \int_{\Gamma_D} u_0 \tau \cdot n \ {\rm d} s, \end{split}\]

and \(\Sigma_g := \{ \tau \in H({\rm div})\) such that \(\tau \cdot n|_{\Gamma_N} = g \}\) and \(V := L^2(\Omega)\).

To discretize the above formulation, two discrete function spaces \(\Sigma_h \subset \Sigma\) and \(V_h \subset V\) are needed to form a mixed function space \(\Sigma_h \times V_h\). A stable choice of finite element spaces is to let \(\Sigma_h\) be the Raviart-Thomas elements of polynomial order \(k\) and let \(V_h\) be discontinuous Lagrange elements of polynomial order \(k-1\).

To solve the linear system for the mixed problem, we will use am iterative method with a block-diagonal preconditioner that is based on the Riesz map, see for example this paper.

## Implementation

Import the required modules:

    from mpi4py import MPI
    from petsc4py import PETSc

    import numpy as np

    import dolfinx
    import ufl
    from basix.ufl import element
    from dolfinx import fem, mesh
    from dolfinx.fem.petsc import discrete_gradient, interpolation_matrix
    from dolfinx.mesh import CellType, create_unit_square

    # Solution scalar (e.g., float32, complex128) and geometry (float32/64)
    # types
    dtype = dolfinx.default_scalar_type
    xdtype = dolfinx.default_real_type

Create a two-dimensional mesh. The iterative solver constructed later requires special construction that is specific to two dimensions. Application in three-dimensions would require a number of changes to the linear solver.

    msh = create_unit_square(MPI.COMM_WORLD, 96, 96, CellType.triangle, dtype=xdtype)

Here we construct compatible function spaces for the mixed Poisson problem. The `V` Raviart-Thomas (\(\mathbb{RT}\)) space is a vector-valued \(H({\rm div})\) conforming space. The `W` space is a space of discontinuous Lagrange function of degree `k`.

Note

The \(\mathbb{RT}_{k}\) element in DOLFINx/Basix is usually denoted as \(\mathbb{RT}_{k-1}\) in the literature.

In the lowest-order case \(k=1\). It can be increased, by the convergence of the iterative solver will degrade.

    k = 1
    V = fem.functionspace(msh, element("RT", msh.basix_cell(), k, dtype=xdtype))
    W = fem.functionspace(msh, element("Discontinuous Lagrange", msh.basix_cell(), k - 1, dtype=xdtype))
    Q = ufl.MixedFunctionSpace(V, W)

Trial functions for \(\sigma\) and \(u\) are declared on the space \(V\) and \(W\), with corresponding test functions \(\tau\) and \(v\):

    sigma, u = ufl.TrialFunctions(Q)
    tau, v = ufl.TestFunctions(Q)

The source function is set to be \(f = 10\exp(-((x_{0} - 0.5)^2 + (x_{1} - 0.5)^2) / 0.02)\):

    x = ufl.SpatialCoordinate(msh)
    f = 10 * ufl.exp(-((x[0] - 0.5) * (x[0] - 0.5) + (x[1] - 0.5) * (x[1] - 0.5)) / 0.02)

We now declare the blocked bilinear and linear forms. We use `ufl.extract_blocks` to extract the block structure of the bilinear and linear form. For the first block of the right-hand side, we provide a form that efficiently is 0. We do this to preserve knowledge of the test space in the block. Note that the defined `L` corresponds to \(u_{0} = 0\) on \(\Gamma_{D}\).

    dx = ufl.Measure("dx", msh)

    a = ufl.extract_blocks(
        ufl.inner(sigma, tau) * dx + ufl.inner(u, ufl.div(tau)) * dx + ufl.inner(ufl.div(sigma), v) * dx
    )
    L = [ufl.ZeroBaseForm((tau,)), -ufl.inner(f, v) * dx]

In preparation for Dirichlet boundary conditions, we use the function `locate_entities_boundary` to locate mesh entities (facets) with which degree-of-freedoms to be constrained are associated with, and then use `locate_dofs_topological` to get the degree-of-freedom indices. Below we identify the degree-of-freedom in `V` on the (i) top (\(x_{1} = 1\)) and (ii) bottom (\(x_{1} = 0\)) of the mesh/domain.

    fdim = msh.topology.dim - 1
    facets_top = mesh.locate_entities_boundary(msh, fdim, lambda x: np.isclose(x[1], 1.0))
    facets_bottom = mesh.locate_entities_boundary(msh, fdim, lambda x: np.isclose(x[1], 0.0))
    dofs_top = fem.locate_dofs_topological(V, fdim, facets_top)
    dofs_bottom = fem.locate_dofs_topological(V, fdim, facets_bottom)

Now, we create Dirichlet boundary objects for the condition \(\sigma \cdot n = \sin(5 x_(0)\) on the top and bottom boundaries:

    cells_top_ = mesh.compute_incident_entities(msh.topology, facets_top, fdim, fdim + 1)
    cells_bottom = mesh.compute_incident_entities(msh.topology, facets_bottom, fdim, fdim + 1)
    g = fem.Function(V, dtype=dtype)
    g.interpolate(lambda x: np.vstack((np.zeros_like(x[0]), np.sin(5 * x[0]))), cells0=cells_top_)
    g.interpolate(lambda x: np.vstack((np.zeros_like(x[0]), -np.sin(5 * x[0]))), cells0=cells_bottom)
    bcs = [fem.dirichletbc(g, dofs_top), fem.dirichletbc(g, dofs_bottom)]

Rather than solving the linear system \(A x = b\), we will solve the preconditioned problem \(P^{-1} A x = P^{-1} b\). Commonly \(P = A\), but this does not lead to efficient solvers for saddle point problems.

For this problem, we introduce the preconditioner

\[\begin{split} a_p((\sigma, u), (\tau, v)) = \begin{bmatrix} \int_{\Omega} \sigma \cdot \tau + (\nabla \cdot \sigma) (\nabla \cdot \tau) \ {\rm d} x & 0 \\ 0 & \int_{\Omega} u \cdot v \ {\rm d} x \end{bmatrix} \end{split}\]

and assemble it into the matrix `P`:

    a_p = ufl.extract_blocks(
        ufl.inner(sigma, tau) * dx + ufl.inner(ufl.div(sigma), ufl.div(tau)) * dx + ufl.inner(u, v) * dx
    )

We create finite element functions that will hold the \(\sigma\) and \(u\) solutions:

    sigma, u = fem.Function(V, name="sigma", dtype=dtype), fem.Function(W, name="u", dtype=dtype)

We now create a linear problem solver for the mixed problem. As we will use different preconditions for the individual blocks of the saddle point problem, we specify the matrix kind to be “nest”, so that we can use `fieldsplit` (block) type and set the ‘splits’ between the \(\sigma\) and \(u\) fields.

    problem = fem.petsc.LinearProblem(
        a,
        L,
        u=[sigma, u],
        P=a_p,
        kind="nest",
        bcs=bcs,
        petsc_options_prefix="demo_mixed_poisson_",
        petsc_options={
            "ksp_type": "gmres",
            "pc_type": "fieldsplit",
            "pc_fieldsplit_type": "additive",
            "ksp_rtol": 1e-8,
            "ksp_gmres_restart": 100,
            "ksp_view": "",
        },
    )

    ksp = problem.solver
    ksp.setMonitor(
        lambda _, its, rnorm: PETSc.Sys.Print(f"Iteration: {its:>4d}, residual: {rnorm:.3e}")
    )

For the \(P_{11}\) block, which is the discontinuous Lagrange mass matrix, we let the preconditioner be the default, which is incomplete LU factorisation and which can solve the block exactly in one iteration. The \(P_{00}\) requires careful handling as \(H({\rm div})\) problems require special preconditioners to be efficient.

If PETSc has been configured with Hypre, we use the Hypre Auxiliary Maxwell Space (AMS) algebraic multigrid preconditioner. We can use AMS for this \(H({\rm div})\)-type problem in two-dimensions because \(H({\rm div})\) and \(H({\rm curl})\) spaces are effectively the same in two-dimensions, just rotated by $\pi/2.

    ksp_sigma, ksp_u = ksp.getPC().getFieldSplitSubKSP()
    pc_sigma = ksp_sigma.getPC()
    if PETSc.Sys().hasExternalPackage("hypre") and not np.issubdtype(dtype, np.complexfloating):
        pc_sigma.setType("hypre")
        pc_sigma.setHYPREType("ams")

        opts = PETSc.Options()
        opts[f"{ksp_sigma.prefix}pc_hypre_ams_cycle_type"] = 7
        opts[f"{ksp_sigma.prefix}pc_hypre_ams_relax_times"] = 2

        # Construct and set the 'discrete gradient' operator, which maps
        # grad H1 -> H(curl), i.e. the gradient of a scalar Lagrange space
        # to a H(curl) space
        V_H1 = fem.functionspace(msh, element("Lagrange", msh.basix_cell(), k, dtype=xdtype))
        V_curl = fem.functionspace(msh, element("N1curl", msh.basix_cell(), k, dtype=xdtype))
        G = discrete_gradient(V_H1, V_curl)
        G.assemble()
        pc_sigma.setHYPREDiscreteGradient(G)

        assert k > 0, "Element degree must be at least 1."
        if k == 1:
            # For the lowest order base (k=1), we can supply interpolation
            # of the '1' vectors in the space V. Hypre can then construct
            # the required operators from G and the '1' vectors.
            cvec0, cvec1 = fem.Function(V), fem.Function(V)
            cvec0.interpolate(lambda x: np.vstack((np.ones_like(x[0]), np.zeros_like(x[1]))))
            cvec1.interpolate(lambda x: np.vstack((np.zeros_like(x[0]), np.ones_like(x[1]))))
            pc_sigma.setHYPRESetEdgeConstantVectors(cvec0.x.petsc_vec, cvec1.x.petsc_vec, None)
        else:
            # For high-order spaces, we must provide the (H1)^d -> H(div)
            # interpolation operator/matrix
            V_H1d = fem.functionspace(msh, ("Lagrange", k, (msh.geometry.dim,)))
            Pi = interpolation_matrix(V_H1d, V)  # (H1)^d -> H(div)
            Pi.assemble()
            pc_sigma.setHYPRESetInterpolations(msh.geometry.dim, None, None, Pi, None)

            # High-order elements generally converge less well than the
            # lowest-order case with algebraic multigrid, so we perform
            # extra work at the multigrid stage
            opts[f"{ksp_sigma.prefix}pc_hypre_ams_tol"] = 1e-12
            opts[f"{ksp_sigma.prefix}pc_hypre_ams_max_iter"] = 3

        ksp_sigma.setFromOptions()
    else:
        # If Hypre is not available, use LU factorisation on the $P_{00}$
        # block
        pc_sigma.setType("lu")
        use_superlu = PETSc.IntType == np.int64
        if PETSc.Sys().hasExternalPackage("mumps") and not use_superlu:
            pc_sigma.setFactorSolverType("mumps")
        elif PETSc.Sys().hasExternalPackage("superlu_dist"):
            pc_sigma.setFactorSolverType("superlu_dist")

Once we have set the preconditioners for the two blocks, we can solve the linear system. `LinearProblem` will automatically assemble the linear system, apply the boundary conditions, call the Krylov solver and update the solution vectors `u` and `sigma`.

    problem.solve()
    converged_reason = problem.solver.getConvergedReason()
    assert converged_reason > 0, f"Krylov solver has not converged, reason: {converged_reason}."

We save the solution `u` in VTX format:

    if dolfinx.has_adios2:
        from dolfinx.io import VTXWriter

        with VTXWriter(msh.comm, "output_mixed_poisson.bp", u) as f:
            f.write(0.0)
    else:
        print("ADIOS2 required for VTX output.")