{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Programming your solver\n",
    "\n",
    "In this notebook, we will look at some of the more advanced capabilities Firedrake has for configuring and developing preconditioners. In particular, we will show support for geometric multigrid, as well as user-defined preconditioners.\n",
    "\n",
    "As our prototypical example, we will consider the Stokes equations. Find $(u, p) \\in V \\times Q \\subset (H^1)^d \\times L^2$ such that\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "  \\nu\\int_\\Omega \\nabla u : \\nabla v\\,\\mathrm{d}x - \\int_\\Omega p\n",
    "  \\nabla \\cdot v\\,\\mathrm{d}x\n",
    "  &= \\int_\\Omega f \\cdot v\\,\\mathrm{d}x, \\\\\n",
    "  -\\int_\\Omega \\nabla \\cdot u q \\,\\mathrm{d}x&= 0.\n",
    "\\end{align}\n",
    "$$\n",
    "for all $(v, q) \\in V \\times Q$. Where $\\nu$ is the viscosity.\n",
    "\n",
    "We will use the inf-sup stable Taylor-Hood element pair of piecewise quadratic velocities and piecewise linear pressures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code in this cell makes plots appear an appropriate size and resolution in the browser window\n",
    "%matplotlib widget\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (11, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from firedrake import *\n",
    "mesh = UnitSquareMesh(8, 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now build a hierarchy of regularly refined meshes with this as the coarsest mesh, and grab the finest one to define the problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "meshes = MeshHierarchy(mesh, refinement_levels=3)\n",
    "# Grab the finest mesh\n",
    "mesh = meshes[-1]\n",
    "\n",
    "V = VectorFunctionSpace(mesh, \"Lagrange\", 2)\n",
    "Q = FunctionSpace(mesh, \"Lagrange\", 1)\n",
    "W = V*Q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We set up the problem in residual form (using `TestFunction`s but no `TrialFunction`s)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "v, q = TestFunctions(W)\n",
    "w = Function(W)\n",
    "u, p = split(w)\n",
    "\n",
    "nu = Constant(0.0001)\n",
    "F = nu*inner(grad(u), grad(v))*dx - p*div(v)*dx - div(u)*q*dx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now need to augment the problem with a forcing term and boundary conditions.  We will solve a regularised lid-driven cavity problem, and thus choose $f = 0$ and the boundary conditions:\n",
    "$$\n",
    "\\begin{align}\n",
    "u &= \\begin{pmatrix}\\frac{x^2 (2 - x)^2 y^2}{4} \\\\ 0 \\end{pmatrix} & \\text{ on $\\Gamma_1 = \\{y = 1\\}$},\\\\\n",
    "u &= 0 & \\text{ otherwise.}\\\\\n",
    "\\end{align}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = SpatialCoordinate(mesh)\n",
    "bc_value = as_vector([0.25 * x**2 * (2-x)**2 *y**2, 0])\n",
    "\n",
    "bcs = [DirichletBC(W.sub(0), bc_value, 4),\n",
    "       DirichletBC(W.sub(0), zero(mesh.geometric_dimension()), (1, 2, 3))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This problem has a null space of constant pressures, so we'll need to inform the solver about that too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "firedrake:WARNING No comm specified for VectorSpaceBasis, COMM_WORLD assumed\n"
     ]
    }
   ],
   "source": [
    "nullspace = MixedVectorSpaceBasis(W, [W.sub(0), VectorSpaceBasis(constant=True)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we're going to look at a bunch of different solver options, let's have a function that builds a solver with the provided options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_solver(solver_parameters, *, pmat=None, appctx=None):\n",
    "    p = {}\n",
    "    if solver_parameters is not None:\n",
    "        p.update(solver_parameters)\n",
    "    # Default to linear SNES\n",
    "    p.setdefault(\"snes_type\", \"ksponly\")\n",
    "    p.setdefault(\"ksp_rtol\", 1e-7)\n",
    "    problem = NonlinearVariationalProblem(F, w, bcs=bcs, Jp=pmat)\n",
    "    solver = NonlinearVariationalSolver(problem, nullspace=nullspace, options_prefix=\"\", \n",
    "                                        solver_parameters=p, appctx=appctx)\n",
    "    return solver"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's go ahead and solve the problem using a direct solver. The solver is configured with a dictionary of PETSc options. Here we select MUMPS to perform the sparse LU factorisation. (Note that these are actually the default solver parameters that Firedrake assumes.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "solver_parameters = {\n",
    "    \"mat_type\": \"aij\",\n",
    "    \"ksp_type\": \"preonly\",\n",
    "    # Use MUMPS since it handles the null space\n",
    "    \"pc_type\": \"lu\",\n",
    "    \"pc_factor_mat_solver_type\": \"mumps\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Programmatically inspect convergence of solver\n",
    "def convergence(solver):\n",
    "    from firedrake.solving_utils import KSPReasons, SNESReasons\n",
    "    snes = solver.snes\n",
    "    print(\"\"\"\n",
    "SNES iterations: {snes}; SNES converged reason: {snesreason}\n",
    "   KSP iterations: {ksp}; KSP converged reason: {kspreason}\"\"\".format(snes=snes.getIterationNumber(),\n",
    "                                                                      snesreason=SNESReasons[snes.getConvergedReason()],\n",
    "                                                                      ksp=snes.ksp.getIterationNumber(),\n",
    "                                                                      kspreason=KSPReasons[snes.ksp.getConvergedReason()]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're ready to solve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SNES iterations: 1; SNES converged reason: CONVERGED_ITS\n",
      "   KSP iterations: 1; KSP converged reason: CONVERGED_ITS\n"
     ]
    }
   ],
   "source": [
    "w.assign(0)\n",
    "solver = create_solver(solver_parameters)\n",
    "solver.solve()\n",
    "convergence(solver)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now have a look at the solution, using some simple builtin plotting that utilises matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0c53837a62cc4babbc3ba4d4ebb01a99",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
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       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=1100.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from firedrake.pyplot import streamplot\n",
    "\n",
    "u_h, p_h = w.subfunctions\n",
    "fig, axes = plt.subplots()\n",
    "streamlines = streamplot(u_h, resolution=1/30, seed=0, axes=axes)\n",
    "fig.colorbar(streamlines);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuring a better preconditioner\n",
    "\n",
    "For this small problem, we can (and probably should) use a direct factorisation method. But what if the problem is too big? Then we need an iterative method, and an appropriate preconditioner.\n",
    "\n",
    "Let's try everyone's favourite, ILU(0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "solver_parameters = {\n",
    "    \"mat_type\": \"aij\",\n",
    "    \"ksp_type\": \"gmres\",\n",
    "    \"ksp_gmres_modifiedgramschmidt\": None,\n",
    "    \"ksp_max_it\": 2000,\n",
    "    \"ksp_converged_reason\": None,\n",
    "    \"pc_type\": \"ilu\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Linear  solve converged due to CONVERGED_RTOL iterations 1618\n",
      "\n",
      "SNES iterations: 1; SNES converged reason: CONVERGED_ITS\n",
      "   KSP iterations: 1618; KSP converged reason: CONVERGED_RTOL\n"
     ]
    }
   ],
   "source": [
    "w.assign(0)\n",
    "solver = create_solver(solver_parameters)\n",
    "solver.solve()\n",
    "convergence(solver)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is, unsurprisingly, bad. Fortunately, better options are available."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Block preconditioning\n",
    "\n",
    "Firedrake hooks up all the necessary machinery to access PETSc's [`PCFIELDSPLIT`](https://www.mcs.anl.gov/petsc/petsc-current/manualpages/PC/PCFIELDSPLIT.html#PCFIELDSPLIT) preconditioner. This provides mechanisms for building preconditioners based on block factorisations. The Stokes problem \n",
    "$$\n",
    "\\begin{align}\n",
    "  \\nu\\int_\\Omega \\color{#800020}{\\nabla u : \\nabla v}\\,\\mathrm{d}x - \\int_\\Omega\n",
    "  \\color{#2A52BE}{p \\nabla \\cdot v}\\,\\mathrm{d}x\n",
    "  &= \\int_\\Omega f \\cdot v\\,\\mathrm{d}x, \\\\\n",
    "  -\\int_\\Omega \\color{#2A52BE}{\\nabla \\cdot u q} \\,\\mathrm{d}x&= 0\n",
    "\\end{align}\n",
    "$$\n",
    "is a block system with matrix\n",
    "$$\n",
    "\\mathcal{A} = \\begin{bmatrix} \\color{#800020}{A} & \\color{#2A52BE}{B^T} \\\\ \\color{#2A52BE}{B} & 0 \\end{bmatrix},\n",
    "$$\n",
    "\n",
    "admitting a factorisation\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix} I & 0 \\\\ \\color{#2A52BE}{B} \\color{#800020}{A}^{-1} & I\\end{bmatrix}\n",
    "\\begin{bmatrix}\\color{#800020}{A} & 0 \\\\ 0 & S\\end{bmatrix}\n",
    "\\begin{bmatrix} I & \\color{#800020}{A}^{-1} \\color{#2A52BE}{B^T} \\\\ 0 & I\\end{bmatrix},\n",
    "$$\n",
    "\n",
    "with $S = -\\color{#2A52BE}{B} \\color{#800020}{A}^{-1} \\color{#2A52BE}{B^T}$ the *Schur complement*.  This has an inverse\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix} I & -\\color{#800020}{A}^{-1}\\color{#2A52BE}{B^T} \\\\ 0 & I \\end{bmatrix}\n",
    "\\begin{bmatrix} \\color{#800020}{A}^{-1} & 0 \\\\ 0 & S^{-1}\\end{bmatrix}\n",
    "\\begin{bmatrix} I & 0 \\\\ -\\color{#2A52BE}{B}\\color{#800020}{A}^{-1} & I\\end{bmatrix}.\n",
    "$$\n",
    "\n",
    "$S$ is never formed, so it's inverse is approximated using an iterative method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "exact_inverse_parameters = {\n",
    "    \"ksp_type\": \"fgmres\",\n",
    "    \"pc_type\": \"fieldsplit\",\n",
    "    \"pc_fieldsplit_type\": \"schur\",\n",
    "    \"fieldsplit_0\": {\n",
    "        \"ksp_type\": \"preonly\",\n",
    "        \"pc_type\": \"lu\",\n",
    "    },\n",
    "    \"fieldsplit_1\": {\n",
    "        \"ksp_type\": \"cg\",\n",
    "        \"ksp_rtol\": 1e-8,\n",
    "        \"pc_type\": \"none\",\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SNES iterations: 1; SNES converged reason: CONVERGED_ITS\n",
      "   KSP iterations: 1; KSP converged reason: CONVERGED_RTOL\n"
     ]
    }
   ],
   "source": [
    "w.assign(0)\n",
    "solver = create_solver(exact_inverse_parameters)\n",
    "solver.solve()\n",
    "convergence(solver)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks good, but we had to use an unpreconditioned Krylov method to invert $S$. To do better we need to provide either an approximation to $S$ or $S^{-1}$.\n",
    "\n",
    "For the Stokes equations, [Silvester and Wathen (1993)](https://epubs.siam.org/doi/10.1137/0730031) show that $S \\approx -\\nu^{-1} Q$ is a good approximation, where $Q$ is the pressure mass matrix.\n",
    "\n",
    "Problem: $Q$ is not available as one of the blocks of $\\mathcal{A}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PETSc's approach is to allow us to supply a _separate_ matrix to the solver which will be used to construct the preconditioner. So, we just need to additionally supply\n",
    "\n",
    "$$\n",
    "\\mathcal{P} = \\mathcal{A} + \\begin{bmatrix} 0 & 0 \\\\ 0 & -\\nu^{-1}Q\\end{bmatrix} = \\begin{bmatrix} \\color{#800020}{A} & \\color{#2A52BE}{B^T} \\\\ \\color{#2A52BE}{B} & -\\nu^{-1} Q \\end{bmatrix},\n",
    "$$\n",
    "where $Q = \\int_\\Omega p q \\,\\mathrm{d}x$.\n",
    "\n",
    "We will construct P by symbolically computing the derivative of the residual to get $\\mathcal{A}$ and then subtracting $\\nu^{-1} Q$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "w_t = TrialFunction(W)\n",
    "_, p_t = split(w_t)\n",
    "\n",
    "pmat = lhs(derivative(F, w, w_t)) - 1/nu * p_t * q*dx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now pass this pmat form to `create_solver` and can configure an appropriate preconditioner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "pmat_parameters = {\n",
    "    \"mat_type\": \"nest\", # We only need the blocks\n",
    "    \"snes_type\": \"ksponly\",\n",
    "    \"ksp_view\": None,\n",
    "    \"ksp_monitor_true_residual\": None,\n",
    "    \"ksp_max_it\": 100,\n",
    "    \"pc_type\": \"fieldsplit\",\n",
    "    \"pc_fieldsplit_type\": \"schur\",\n",
    "    \"fieldsplit_0\": {\n",
    "        \"ksp_type\": \"preonly\",\n",
    "        \"pc_type\": \"lu\",\n",
    "    },\n",
    "    \"fieldsplit_1\": {\n",
    "        \"ksp_type\": \"preonly\",\n",
    "        \"pc_type\": \"lu\",\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Residual norms for  solve.\n",
      "    0 KSP preconditioned resid norm 5.496162170027e+00 true resid norm 7.005934058591e-04 ||r(i)||/||b|| 1.000000000000e+00\n",
      "    1 KSP preconditioned resid norm 9.288239850616e-01 true resid norm 2.453830661658e-03 ||r(i)||/||b|| 3.502503222463e+00\n",
      "    2 KSP preconditioned resid norm 4.322571804179e-01 true resid norm 1.513282272809e-03 ||r(i)||/||b|| 2.160000736737e+00\n",
      "    3 KSP preconditioned resid norm 9.747752360309e-02 true resid norm 4.889677087718e-04 ||r(i)||/||b|| 6.979336440831e-01\n",
      "    4 KSP preconditioned resid norm 2.168769655093e-02 true resid norm 2.367419610221e-04 ||r(i)||/||b|| 3.379163421211e-01\n",
      "    5 KSP preconditioned resid norm 6.602391993960e-03 true resid norm 1.323183601885e-04 ||r(i)||/||b|| 1.888661227495e-01\n",
      "    6 KSP preconditioned resid norm 3.201127618913e-03 true resid norm 5.559951304397e-05 ||r(i)||/||b|| 7.936059999850e-02\n",
      "    7 KSP preconditioned resid norm 1.583402974887e-03 true resid norm 1.881712631973e-05 ||r(i)||/||b|| 2.685884018085e-02\n",
      "    8 KSP preconditioned resid norm 5.459453643441e-04 true resid norm 3.548481386560e-06 ||r(i)||/||b|| 5.064965437705e-03\n",
      "    9 KSP preconditioned resid norm 2.435745730827e-04 true resid norm 1.708889316683e-06 ||r(i)||/||b|| 2.439202685026e-03\n",
      "   10 KSP preconditioned resid norm 8.264338667027e-05 true resid norm 4.816892100534e-07 ||r(i)||/||b|| 6.875445958027e-04\n",
      "   11 KSP preconditioned resid norm 2.869421028055e-05 true resid norm 2.297141937468e-07 ||r(i)||/||b|| 3.278851782299e-04\n",
      "   12 KSP preconditioned resid norm 1.126260471802e-05 true resid norm 1.157469218690e-07 ||r(i)||/||b|| 1.652126909860e-04\n",
      "   13 KSP preconditioned resid norm 3.183234314886e-06 true resid norm 2.964758721483e-08 ||r(i)||/||b|| 4.231782224452e-05\n",
      "   14 KSP preconditioned resid norm 1.315553328176e-06 true resid norm 1.114853947343e-08 ||r(i)||/||b|| 1.591299515553e-05\n",
      "   15 KSP preconditioned resid norm 6.727350581287e-07 true resid norm 4.697218845361e-09 ||r(i)||/||b|| 6.704628970353e-06\n",
      "   16 KSP preconditioned resid norm 2.943828880986e-07 true resid norm 2.160101650639e-09 ||r(i)||/||b|| 3.083245763626e-06\n",
      "KSP Object: () 1 MPI process\n",
      "  type: gmres\n",
      "    restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement\n",
      "    happy breakdown tolerance 1e-30\n",
      "  maximum iterations=100, initial guess is zero\n",
      "  tolerances:  relative=1e-07, absolute=1e-50, divergence=10000.\n",
      "  left preconditioning\n",
      "  using PRECONDITIONED norm type for convergence test\n",
      "PC Object: () 1 MPI process\n",
      "  type: fieldsplit\n",
      "    FieldSplit with Schur preconditioner, factorization FULL\n",
      "    Preconditioner for the Schur complement formed from A11\n",
      "    Split info:\n",
      "    Split number 0 Defined by IS\n",
      "    Split number 1 Defined by IS\n",
      "    KSP solver for A00 block\n",
      "      KSP Object: (fieldsplit_0_) 1 MPI process\n",
      "        type: preonly\n",
      "        maximum iterations=10000, initial guess is zero\n",
      "        tolerances:  relative=1e-05, absolute=1e-50, divergence=10000.\n",
      "        left preconditioning\n",
      "        using NONE norm type for convergence test\n",
      "      PC Object: (fieldsplit_0_) 1 MPI process\n",
      "        type: lu\n",
      "          out-of-place factorization\n",
      "          tolerance for zero pivot 2.22045e-14\n",
      "          matrix ordering: nd\n",
      "          factor fill ratio given 5., needed 5.8857\n",
      "            Factored matrix follows:\n",
      "              Mat Object: (fieldsplit_0_) 1 MPI process\n",
      "                type: seqaij\n",
      "                rows=33282, cols=33282, bs=2\n",
      "                package used to perform factorization: petsc\n",
      "                total: nonzeros=4459972, allocated nonzeros=4459972\n",
      "                  using I-node routines: found 16529 nodes, limit used is 5\n",
      "        linear system matrix = precond matrix:\n",
      "        Mat Object: (fieldsplit_0_) 1 MPI process\n",
      "          type: seqaij\n",
      "          rows=33282, cols=33282, bs=2\n",
      "          total: nonzeros=757764, allocated nonzeros=757764\n",
      "          total number of mallocs used during MatSetValues calls=0\n",
      "            using I-node routines: found 16638 nodes, limit used is 5\n",
      "    KSP solver for S = A11 - A10 inv(A00) A01\n",
      "      KSP Object: (fieldsplit_1_) 1 MPI process\n",
      "        type: preonly\n",
      "        maximum iterations=10000, initial guess is zero\n",
      "        tolerances:  relative=1e-05, absolute=1e-50, divergence=10000.\n",
      "        left preconditioning\n",
      "        using NONE norm type for convergence test\n",
      "      PC Object: (fieldsplit_1_) 1 MPI process\n",
      "        type: lu\n",
      "          out-of-place factorization\n",
      "          tolerance for zero pivot 2.22045e-14\n",
      "          matrix ordering: nd\n",
      "          factor fill ratio given 5., needed 6.96187\n",
      "            Factored matrix follows:\n",
      "              Mat Object: (fieldsplit_1_) 1 MPI process\n",
      "                type: seqaij\n",
      "                rows=4225, cols=4225\n",
      "                package used to perform factorization: petsc\n",
      "                total: nonzeros=202291, allocated nonzeros=202291\n",
      "                  not using I-node routines\n",
      "        linear system matrix followed by preconditioner matrix:\n",
      "        Mat Object: (fieldsplit_1_) 1 MPI process\n",
      "          type: schurcomplement\n",
      "          rows=4225, cols=4225\n",
      "            has attached null space\n",
      "            Schur complement A11 - A10 inv(A00) A01\n",
      "            A11\n",
      "              Mat Object: (fieldsplit_1_) 1 MPI process\n",
      "                type: seqaij\n",
      "                rows=4225, cols=4225\n",
      "                total: nonzeros=29057, allocated nonzeros=29057\n",
      "                total number of mallocs used during MatSetValues calls=0\n",
      "                  has attached null space\n",
      "                  not using I-node routines\n",
      "            A10\n",
      "              Mat Object: 1 MPI process\n",
      "                type: seqaij\n",
      "                rows=4225, cols=33282, rbs=1, cbs=2\n",
      "                total: nonzeros=156930, allocated nonzeros=156930\n",
      "                total number of mallocs used during MatSetValues calls=0\n",
      "                  not using I-node routines\n",
      "            KSP solver for A00 block viewable with the additional option -fieldsplit_0_ksp_view\n",
      "            A01\n",
      "              Mat Object: 1 MPI process\n",
      "                type: seqaij\n",
      "                rows=33282, cols=4225, rbs=2, cbs=1\n",
      "                total: nonzeros=156930, allocated nonzeros=156930\n",
      "                total number of mallocs used during MatSetValues calls=0\n",
      "                  using I-node routines: found 16638 nodes, limit used is 5\n",
      "        Mat Object: (fieldsplit_1_) 1 MPI process\n",
      "          type: seqaij\n",
      "          rows=4225, cols=4225\n",
      "          total: nonzeros=29057, allocated nonzeros=29057\n",
      "          total number of mallocs used during MatSetValues calls=0\n",
      "            has attached null space\n",
      "            not using I-node routines\n",
      "  linear system matrix followed by preconditioner matrix:\n",
      "  Mat Object: () 1 MPI process\n",
      "    type: nest\n",
      "    rows=37507, cols=37507\n",
      "      has attached null space\n",
      "      Matrix object:\n",
      "        type=nest, rows=2, cols=2\n",
      "        MatNest structure:\n",
      "        (0,0) : type=seqaij, rows=33282, cols=33282\n",
      "        (0,1) : type=seqaij, rows=33282, cols=4225\n",
      "        (1,0) : type=seqaij, rows=4225, cols=33282\n",
      "        (1,1) : type=seqaij, rows=4225, cols=4225\n",
      "  Mat Object: () 1 MPI process\n",
      "    type: nest\n",
      "    rows=37507, cols=37507\n",
      "      has attached null space\n",
      "      Matrix object:\n",
      "        type=nest, rows=2, cols=2\n",
      "        MatNest structure:\n",
      "        (0,0) : prefix=\"fieldsplit_0_\", type=seqaij, rows=33282, cols=33282\n",
      "        (0,1) : type=seqaij, rows=33282, cols=4225\n",
      "        (1,0) : type=seqaij, rows=4225, cols=33282\n",
      "        (1,1) : prefix=\"fieldsplit_1_\", type=seqaij, rows=4225, cols=4225\n",
      "\n",
      "SNES iterations: 1; SNES converged reason: CONVERGED_ITS\n",
      "   KSP iterations: 16; KSP converged reason: CONVERGED_RTOL\n"
     ]
    }
   ],
   "source": [
    "w.assign(0)\n",
    "solver = create_solver(pmat_parameters, pmat=pmat)\n",
    "solver.solve()\n",
    "convergence(solver)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Providing auxiliary operators\n",
    "\n",
    "An inconvenience here is that we must build $\\mathcal{P}$, even though we only need $-\\nu^{-1} Q$ in additional to $\\mathcal{A}$ in the preconditioner.\n",
    "\n",
    "Firedrake offers a facilities to build Python preconditioning objects, utilising petsc4py.\n",
    "\n",
    "In this case, we can subclass the \n",
    "[`AuxiliaryOperatorPC`](https://www.firedrakeproject.org/firedrake.preconditioners.html#firedrake.preconditioners.assembled.AuxiliaryOperatorPC) to provide the mass matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MassMatrix(AuxiliaryOperatorPC):\n",
    "    _prefix = \"mass_\"\n",
    "    def form(self, pc, test, trial):\n",
    "        # Grab the definition of nu from the user application context (a dict)\n",
    "        nu = self.get_appctx(pc)[\"nu\"]\n",
    "        return (-1/nu * test*trial*dx, None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we just need to select parameters such that this Python preconditioner is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "mass_parameters = {\n",
    "    \"mat_type\": \"nest\", # We only need the blocks\n",
    "    \"ksp_view\": None,\n",
    "    \"pc_type\": \"fieldsplit\",\n",
    "    \"pc_fieldsplit_type\": \"schur\",\n",
    "    \"fieldsplit_0\": {\n",
    "        \"ksp_type\": \"preonly\",\n",
    "        \"pc_type\": \"lu\",\n",
    "    },\n",
    "    \"fieldsplit_1\": {\n",
    "        \"ksp_type\": \"preonly\",\n",
    "        \"pc_type\": \"python\",\n",
    "        \"pc_python_type\": \"__main__.MassMatrix\",\n",
    "        \"mass_pc_type\": \"lu\",\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KSP Object: () 1 MPI process\n",
      "  type: gmres\n",
      "    restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement\n",
      "    happy breakdown tolerance 1e-30\n",
      "  maximum iterations=10000, initial guess is zero\n",
      "  tolerances:  relative=1e-07, absolute=1e-50, divergence=10000.\n",
      "  left preconditioning\n",
      "  using PRECONDITIONED norm type for convergence test\n",
      "PC Object: () 1 MPI process\n",
      "  type: fieldsplit\n",
      "    FieldSplit with Schur preconditioner, factorization FULL\n",
      "    Preconditioner for the Schur complement formed from A11\n",
      "    Split info:\n",
      "    Split number 0 Defined by IS\n",
      "    Split number 1 Defined by IS\n",
      "    KSP solver for A00 block\n",
      "      KSP Object: (fieldsplit_0_) 1 MPI process\n",
      "        type: preonly\n",
      "        maximum iterations=10000, initial guess is zero\n",
      "        tolerances:  relative=1e-05, absolute=1e-50, divergence=10000.\n",
      "        left preconditioning\n",
      "        using NONE norm type for convergence test\n",
      "      PC Object: (fieldsplit_0_) 1 MPI process\n",
      "        type: lu\n",
      "          out-of-place factorization\n",
      "          tolerance for zero pivot 2.22045e-14\n",
      "          matrix ordering: nd\n",
      "          factor fill ratio given 5., needed 5.8857\n",
      "            Factored matrix follows:\n",
      "              Mat Object: (fieldsplit_0_) 1 MPI process\n",
      "                type: seqaij\n",
      "                rows=33282, cols=33282, bs=2\n",
      "                package used to perform factorization: petsc\n",
      "                total: nonzeros=4459972, allocated nonzeros=4459972\n",
      "                  using I-node routines: found 16529 nodes, limit used is 5\n",
      "        linear system matrix = precond matrix:\n",
      "        Mat Object: (fieldsplit_0_) 1 MPI process\n",
      "          type: seqaij\n",
      "          rows=33282, cols=33282, bs=2\n",
      "          total: nonzeros=757764, allocated nonzeros=757764\n",
      "          total number of mallocs used during MatSetValues calls=0\n",
      "            using I-node routines: found 16638 nodes, limit used is 5\n",
      "    KSP solver for S = A11 - A10 inv(A00) A01\n",
      "      KSP Object: (fieldsplit_1_) 1 MPI process\n",
      "        type: preonly\n",
      "        maximum iterations=10000, initial guess is zero\n",
      "        tolerances:  relative=1e-05, absolute=1e-50, divergence=10000.\n",
      "        left preconditioning\n",
      "        using NONE norm type for convergence test\n",
      "      PC Object: (fieldsplit_1_) 1 MPI process\n",
      "        type: python\n",
      "          Python: __main__.MassMatrix\n",
      "        Firedrake custom preconditioner MassMatrix\n",
      "        PC to apply inverse\n",
      "        PC Object: (fieldsplit_1_mass_) 1 MPI process\n",
      "          type: lu\n",
      "            out-of-place factorization\n",
      "            tolerance for zero pivot 2.22045e-14\n",
      "            matrix ordering: nd\n",
      "            factor fill ratio given 5., needed 6.96187\n",
      "              Factored matrix follows:\n",
      "                Mat Object: (fieldsplit_1_mass_) 1 MPI process\n",
      "                  type: seqaij\n",
      "                  rows=4225, cols=4225\n",
      "                  package used to perform factorization: petsc\n",
      "                  total: nonzeros=202291, allocated nonzeros=202291\n",
      "                    not using I-node routines\n",
      "          linear system matrix = precond matrix:\n",
      "          Mat Object: (fieldsplit_1_mass_) 1 MPI process\n",
      "            type: seqaij\n",
      "            rows=4225, cols=4225\n",
      "            total: nonzeros=29057, allocated nonzeros=29057\n",
      "            total number of mallocs used during MatSetValues calls=0\n",
      "              has attached null space\n",
      "              not using I-node routines\n",
      "        linear system matrix followed by preconditioner matrix:\n",
      "        Mat Object: (fieldsplit_1_) 1 MPI process\n",
      "          type: schurcomplement\n",
      "          rows=4225, cols=4225\n",
      "            has attached null space\n",
      "            Schur complement A11 - A10 inv(A00) A01\n",
      "            A11\n",
      "              Mat Object: (fieldsplit_1_) 1 MPI process\n",
      "                type: seqaij\n",
      "                rows=4225, cols=4225\n",
      "                total: nonzeros=29057, allocated nonzeros=29057\n",
      "                total number of mallocs used during MatSetValues calls=0\n",
      "                  has attached null space\n",
      "                  not using I-node routines\n",
      "            A10\n",
      "              Mat Object: 1 MPI process\n",
      "                type: seqaij\n",
      "                rows=4225, cols=33282, rbs=1, cbs=2\n",
      "                total: nonzeros=156930, allocated nonzeros=156930\n",
      "                total number of mallocs used during MatSetValues calls=0\n",
      "                  not using I-node routines\n",
      "            KSP solver for A00 block viewable with the additional option -fieldsplit_0_ksp_view\n",
      "            A01\n",
      "              Mat Object: 1 MPI process\n",
      "                type: seqaij\n",
      "                rows=33282, cols=4225, rbs=2, cbs=1\n",
      "                total: nonzeros=156930, allocated nonzeros=156930\n",
      "                total number of mallocs used during MatSetValues calls=0\n",
      "                  using I-node routines: found 16638 nodes, limit used is 5\n",
      "        Mat Object: (fieldsplit_1_) 1 MPI process\n",
      "          type: seqaij\n",
      "          rows=4225, cols=4225\n",
      "          total: nonzeros=29057, allocated nonzeros=29057\n",
      "          total number of mallocs used during MatSetValues calls=0\n",
      "            has attached null space\n",
      "            not using I-node routines\n",
      "  linear system matrix = precond matrix:\n",
      "  Mat Object: () 1 MPI process\n",
      "    type: nest\n",
      "    rows=37507, cols=37507\n",
      "      has attached null space\n",
      "      Matrix object:\n",
      "        type=nest, rows=2, cols=2\n",
      "        MatNest structure:\n",
      "        (0,0) : prefix=\"fieldsplit_0_\", type=seqaij, rows=33282, cols=33282\n",
      "        (0,1) : type=seqaij, rows=33282, cols=4225\n",
      "        (1,0) : type=seqaij, rows=4225, cols=33282\n",
      "        (1,1) : prefix=\"fieldsplit_1_\", type=seqaij, rows=4225, cols=4225\n",
      "\n",
      "SNES iterations: 1; SNES converged reason: CONVERGED_ITS\n",
      "   KSP iterations: 16; KSP converged reason: CONVERGED_RTOL\n"
     ]
    }
   ],
   "source": [
    "appctx = {\"nu\": nu} # arbitrary user data that is available inside the user PC object\n",
    "w.assign(0)\n",
    "solver = create_solver(mass_parameters, appctx=appctx)\n",
    "solver.solve()\n",
    "convergence(solver)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This performs identically to the previous approach, except that the preconditioning matrix is only built for the pressure space, and constructed \"on demand\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multigrid preconditioners and smoothers\n",
    "\n",
    "So far, we've only used direct solvers for the blocks. We can also use iterative methods. Here we'll use geometric multigrid to solve\n",
    "\n",
    "In the same way that Firedrake hooks up solvers such that [`PCFIELDSPLIT`](https://www.mcs.anl.gov/petsc/petsc-current/manualpages/PC/PCFIELDSPLIT.html#PCFIELDSPLIT) is enabled, if a problem was defined on a mesh from a `MeshHierarchy`, [`PCMG`](https://www.mcs.anl.gov/petsc/petsc-current/manualpages/PC/PCMG.html) and [`SNESFAS`](https://www.mcs.anl.gov/petsc/petsc-current/manualpages/SNES/SNESFASType.html) are also available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "fieldsplit_mg_parameters = {\n",
    "    \"mat_type\": \"nest\",\n",
    "    \"ksp_view\": None,\n",
    "    \"pc_type\": \"fieldsplit\",\n",
    "    \"pc_fieldsplit_type\": \"schur\",\n",
    "    \"fieldsplit_0\": {\n",
    "        \"ksp_type\": \"preonly\",\n",
    "        \"pc_type\": \"mg\",\n",
    "        \"mg_levels\": {\n",
    "            \"ksp_type\": \"chebyshev\",\n",
    "            \"ksp_max_it\": 2,\n",
    "        }\n",
    "    },\n",
    "    \"fieldsplit_1\": {\n",
    "        \"ksp_type\": \"chebyshev\",\n",
    "        \"ksp_max_it\": 2,\n",
    "        \"pc_type\": \"python\",\n",
    "        \"pc_python_type\": \"__main__.MassMatrix\",\n",
    "        \"mass_pc_type\": \"sor\",\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, when the solver runs, PETSc will call back in to Firedrake for restriction and prolongation, as well as rediscretising $A$ on the coarser levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KSP Object: () 1 MPI process\n",
      "  type: gmres\n",
      "    restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement\n",
      "    happy breakdown tolerance 1e-30\n",
      "  maximum iterations=10000, initial guess is zero\n",
      "  tolerances:  relative=1e-07, absolute=1e-50, divergence=10000.\n",
      "  left preconditioning\n",
      "  using PRECONDITIONED norm type for convergence test\n",
      "PC Object: () 1 MPI process\n",
      "  type: fieldsplit\n",
      "    FieldSplit with Schur preconditioner, factorization FULL\n",
      "    Preconditioner for the Schur complement formed from A11\n",
      "    Split info:\n",
      "    Split number 0 Defined by IS\n",
      "    Split number 1 Defined by IS\n",
      "    KSP solver for A00 block\n",
      "      KSP Object: (fieldsplit_0_) 1 MPI process\n",
      "        type: preonly\n",
      "        maximum iterations=10000, initial guess is zero\n",
      "        tolerances:  relative=1e-05, absolute=1e-50, divergence=10000.\n",
      "        left preconditioning\n",
      "        using NONE norm type for convergence test\n",
      "      PC Object: (fieldsplit_0_) 1 MPI process\n",
      "        type: mg\n",
      "          type is MULTIPLICATIVE, levels=4 cycles=v\n",
      "            Cycles per PCApply=1\n",
      "            Not using Galerkin computed coarse grid matrices\n",
      "        Coarse grid solver -- level 0 -------------------------------\n",
      "          KSP Object: (fieldsplit_0_mg_coarse_) 1 MPI process\n",
      "            type: preonly\n",
      "            maximum iterations=10000, initial guess is zero\n",
      "            tolerances:  relative=1e-05, absolute=1e-50, divergence=10000.\n",
      "            left preconditioning\n",
      "            using NONE norm type for convergence test\n",
      "          PC Object: (fieldsplit_0_mg_coarse_) 1 MPI process\n",
      "            type: lu\n",
      "              out-of-place factorization\n",
      "              tolerance for zero pivot 2.22045e-14\n",
      "              using diagonal shift on blocks to prevent zero pivot [INBLOCKS]\n",
      "              matrix ordering: nd\n",
      "              factor fill ratio given 5., needed 2.49235\n",
      "                Factored matrix follows:\n",
      "                  Mat Object: (fieldsplit_0_mg_coarse_) 1 MPI process\n",
      "                    type: seqaij\n",
      "                    rows=578, cols=578, bs=2\n",
      "                    package used to perform factorization: petsc\n",
      "                    total: nonzeros=30636, allocated nonzeros=30636\n",
      "                      using I-node routines: found 271 nodes, limit used is 5\n",
      "            linear system matrix = precond matrix:\n",
      "            Mat Object: 1 MPI process\n",
      "              type: seqaij\n",
      "              rows=578, cols=578, bs=2\n",
      "              total: nonzeros=12292, allocated nonzeros=12292\n",
      "              total number of mallocs used during MatSetValues calls=0\n",
      "                using I-node routines: found 286 nodes, limit used is 5\n",
      "        Down solver (pre-smoother) on level 1 -------------------------------\n",
      "          KSP Object: (fieldsplit_0_mg_levels_1_) 1 MPI process\n",
      "            type: chebyshev\n",
      "              Chebyshev polynomial of first kind\n",
      "              eigenvalue targets used: min 0.0993901, max 1.09329\n",
      "              eigenvalues estimated via gmres: min 0.0274474, max 0.993901\n",
      "              eigenvalues estimated using gmres with transform: [0. 0.1; 0. 1.1]              KSP Object: (fieldsplit_0_mg_levels_1_esteig_) 1 MPI process\n",
      "                type: gmres\n",
      "                  restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement\n",
      "                  happy breakdown tolerance 1e-30\n",
      "                maximum iterations=10, initial guess is zero\n",
      "                tolerances:  relative=1e-12, absolute=1e-50, divergence=10000.\n",
      "                left preconditioning\n",
      "                using PRECONDITIONED norm type for convergence test\n",
      "              estimating eigenvalues using noisy right hand side\n",
      "            maximum iterations=2, nonzero initial guess\n",
      "            tolerances:  relative=1e-05, absolute=1e-50, divergence=10000.\n",
      "            left preconditioning\n",
      "            using NONE norm type for convergence test\n",
      "          PC Object: (fieldsplit_0_mg_levels_1_) 1 MPI process\n",
      "            type: sor\n",
      "              type = local_symmetric, iterations = 1, local iterations = 1, omega = 1.\n",
      "            linear system matrix = precond matrix:\n",
      "            Mat Object: 1 MPI process\n",
      "              type: seqaij\n",
      "              rows=2178, cols=2178, bs=2\n",
      "              total: nonzeros=48132, allocated nonzeros=48132\n",
      "              total number of mallocs used during MatSetValues calls=0\n",
      "                using I-node routines: found 1086 nodes, limit used is 5\n",
      "        Up solver (post-smoother) same as down solver (pre-smoother)\n",
      "        Down solver (pre-smoother) on level 2 -------------------------------\n",
      "          KSP Object: (fieldsplit_0_mg_levels_2_) 1 MPI process\n",
      "            type: chebyshev\n",
      "              Chebyshev polynomial of first kind\n",
      "              eigenvalue targets used: min 0.0993111, max 1.09242\n",
      "              eigenvalues estimated via gmres: min 0.0197314, max 0.993111\n",
      "              eigenvalues estimated using gmres with transform: [0. 0.1; 0. 1.1]              KSP Object: (fieldsplit_0_mg_levels_2_esteig_) 1 MPI process\n",
      "                type: gmres\n",
      "                  restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement\n",
      "                  happy breakdown tolerance 1e-30\n",
      "                maximum iterations=10, initial guess is zero\n",
      "                tolerances:  relative=1e-12, absolute=1e-50, divergence=10000.\n",
      "                left preconditioning\n",
      "                using PRECONDITIONED norm type for convergence test\n",
      "              estimating eigenvalues using noisy right hand side\n",
      "            maximum iterations=2, nonzero initial guess\n",
      "            tolerances:  relative=1e-05, absolute=1e-50, divergence=10000.\n",
      "            left preconditioning\n",
      "            using NONE norm type for convergence test\n",
      "          PC Object: (fieldsplit_0_mg_levels_2_) 1 MPI process\n",
      "            type: sor\n",
      "              type = local_symmetric, iterations = 1, local iterations = 1, omega = 1.\n",
      "            linear system matrix = precond matrix:\n",
      "            Mat Object: 1 MPI process\n",
      "              type: seqaij\n",
      "              rows=8450, cols=8450, bs=2\n",
      "              total: nonzeros=190468, allocated nonzeros=190468\n",
      "              total number of mallocs used during MatSetValues calls=0\n",
      "                using I-node routines: found 4222 nodes, limit used is 5\n",
      "        Up solver (post-smoother) same as down solver (pre-smoother)\n",
      "        Down solver (pre-smoother) on level 3 -------------------------------\n",
      "          KSP Object: (fieldsplit_0_mg_levels_3_) 1 MPI process\n",
      "            type: chebyshev\n",
      "              Chebyshev polynomial of first kind\n",
      "              eigenvalue targets used: min 0.099288, max 1.09217\n",
      "              eigenvalues estimated via gmres: min 0.0130154, max 0.99288\n",
      "              eigenvalues estimated using gmres with transform: [0. 0.1; 0. 1.1]              KSP Object: (fieldsplit_0_mg_levels_3_esteig_) 1 MPI process\n",
      "                type: gmres\n",
      "                  restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement\n",
      "                  happy breakdown tolerance 1e-30\n",
      "                maximum iterations=10, initial guess is zero\n",
      "                tolerances:  relative=1e-12, absolute=1e-50, divergence=10000.\n",
      "                left preconditioning\n",
      "                using PRECONDITIONED norm type for convergence test\n",
      "              estimating eigenvalues using noisy right hand side\n",
      "            maximum iterations=2, nonzero initial guess\n",
      "            tolerances:  relative=1e-05, absolute=1e-50, divergence=10000.\n",
      "            left preconditioning\n",
      "            using NONE norm type for convergence test\n",
      "          PC Object: (fieldsplit_0_mg_levels_3_) 1 MPI process\n",
      "            type: sor\n",
      "              type = local_symmetric, iterations = 1, local iterations = 1, omega = 1.\n",
      "            linear system matrix = precond matrix:\n",
      "            Mat Object: (fieldsplit_0_) 1 MPI process\n",
      "              type: seqaij\n",
      "              rows=33282, cols=33282, bs=2\n",
      "              total: nonzeros=757764, allocated nonzeros=757764\n",
      "              total number of mallocs used during MatSetValues calls=0\n",
      "                using I-node routines: found 16638 nodes, limit used is 5\n",
      "        Up solver (post-smoother) same as down solver (pre-smoother)\n",
      "        linear system matrix = precond matrix:\n",
      "        Mat Object: (fieldsplit_0_) 1 MPI process\n",
      "          type: seqaij\n",
      "          rows=33282, cols=33282, bs=2\n",
      "          total: nonzeros=757764, allocated nonzeros=757764\n",
      "          total number of mallocs used during MatSetValues calls=0\n",
      "            using I-node routines: found 16638 nodes, limit used is 5\n",
      "    KSP solver for S = A11 - A10 inv(A00) A01\n",
      "      KSP Object: (fieldsplit_1_) 1 MPI process\n",
      "        type: chebyshev\n",
      "          Chebyshev polynomial of first kind\n",
      "          eigenvalue targets used: min 0.0958741, max 1.05461\n",
      "          eigenvalues estimated via gmres: min 0.128801, max 0.958741\n",
      "          eigenvalues estimated using gmres with transform: [0. 0.1; 0. 1.1]          KSP Object: (fieldsplit_1_esteig_) 1 MPI process\n",
      "            type: gmres\n",
      "              restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement\n",
      "              happy breakdown tolerance 1e-30\n",
      "            maximum iterations=10, initial guess is zero\n",
      "            tolerances:  relative=1e-12, absolute=1e-50, divergence=10000.\n",
      "            left preconditioning\n",
      "            using PRECONDITIONED norm type for convergence test\n",
      "          estimating eigenvalues using noisy right hand side\n",
      "        maximum iterations=2, initial guess is zero\n",
      "        tolerances:  relative=1e-05, absolute=1e-50, divergence=10000.\n",
      "        left preconditioning\n",
      "        using PRECONDITIONED norm type for convergence test\n",
      "      PC Object: (fieldsplit_1_) 1 MPI process\n",
      "        type: python\n",
      "          Python: __main__.MassMatrix\n",
      "        Firedrake custom preconditioner MassMatrix\n",
      "        PC to apply inverse\n",
      "        PC Object: (fieldsplit_1_mass_) 1 MPI process\n",
      "          type: sor\n",
      "            type = local_symmetric, iterations = 1, local iterations = 1, omega = 1.\n",
      "          linear system matrix = precond matrix:\n",
      "          Mat Object: (fieldsplit_1_mass_) 1 MPI process\n",
      "            type: seqaij\n",
      "            rows=4225, cols=4225\n",
      "            total: nonzeros=29057, allocated nonzeros=29057\n",
      "            total number of mallocs used during MatSetValues calls=0\n",
      "              has attached null space\n",
      "              not using I-node routines\n",
      "        linear system matrix followed by preconditioner matrix:\n",
      "        Mat Object: (fieldsplit_1_) 1 MPI process\n",
      "          type: schurcomplement\n",
      "          rows=4225, cols=4225\n",
      "            has attached null space\n",
      "            Schur complement A11 - A10 inv(A00) A01\n",
      "            A11\n",
      "              Mat Object: (fieldsplit_1_) 1 MPI process\n",
      "                type: seqaij\n",
      "                rows=4225, cols=4225\n",
      "                total: nonzeros=29057, allocated nonzeros=29057\n",
      "                total number of mallocs used during MatSetValues calls=0\n",
      "                  has attached null space\n",
      "                  not using I-node routines\n",
      "            A10\n",
      "              Mat Object: 1 MPI process\n",
      "                type: seqaij\n",
      "                rows=4225, cols=33282, rbs=1, cbs=2\n",
      "                total: nonzeros=156930, allocated nonzeros=156930\n",
      "                total number of mallocs used during MatSetValues calls=0\n",
      "                  not using I-node routines\n",
      "            KSP solver for A00 block viewable with the additional option -fieldsplit_0_ksp_view\n",
      "            A01\n",
      "              Mat Object: 1 MPI process\n",
      "                type: seqaij\n",
      "                rows=33282, cols=4225, rbs=2, cbs=1\n",
      "                total: nonzeros=156930, allocated nonzeros=156930\n",
      "                total number of mallocs used during MatSetValues calls=0\n",
      "                  using I-node routines: found 16638 nodes, limit used is 5\n",
      "        Mat Object: (fieldsplit_1_) 1 MPI process\n",
      "          type: seqaij\n",
      "          rows=4225, cols=4225\n",
      "          total: nonzeros=29057, allocated nonzeros=29057\n",
      "          total number of mallocs used during MatSetValues calls=0\n",
      "            has attached null space\n",
      "            not using I-node routines\n",
      "  linear system matrix = precond matrix:\n",
      "  Mat Object: () 1 MPI process\n",
      "    type: nest\n",
      "    rows=37507, cols=37507\n",
      "      has attached null space\n",
      "      Matrix object:\n",
      "        type=nest, rows=2, cols=2\n",
      "        MatNest structure:\n",
      "        (0,0) : prefix=\"fieldsplit_0_\", type=seqaij, rows=33282, cols=33282\n",
      "        (0,1) : type=seqaij, rows=33282, cols=4225\n",
      "        (1,0) : type=seqaij, rows=4225, cols=33282\n",
      "        (1,1) : prefix=\"fieldsplit_1_\", type=seqaij, rows=4225, cols=4225\n",
      "\n",
      "SNES iterations: 1; SNES converged reason: CONVERGED_ITS\n",
      "   KSP iterations: 10; KSP converged reason: CONVERGED_RTOL\n"
     ]
    }
   ],
   "source": [
    "appctx = {\"nu\": nu} # arbitrary user data that is available inside the user PC object\n",
    "w.assign(0)\n",
    "solver = create_solver(fieldsplit_mg_parameters, appctx=appctx)\n",
    "solver.solve()\n",
    "convergence(solver)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also do monolithic, or \"all at once\" multigrid. Here we're using Vanka smoothing. This is supported by a new preconditioner in PETSc `PCPATCH`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "vanka_parameters = {\n",
    "    \"mat_type\": \"matfree\", # We only need the action\n",
    "    \"ksp_type\": \"fgmres\",\n",
    "    \"ksp_max_it\": 25,\n",
    "    \"pc_type\": \"mg\",\n",
    "    \"mg_levels\": {\n",
    "        \"ksp_type\": \"chebyshev\",\n",
    "        \"ksp_convergence_test\": \"skip\",\n",
    "        \"ksp_max_it\": 2,\n",
    "        \"pc_type\": \"python\",\n",
    "        \"pc_python_type\": \"firedrake.PatchPC\",\n",
    "        \"patch\": {\n",
    "            \"pc_patch_save_operators\": 1,\n",
    "            \"pc_patch_partition_of_unity\": False,\n",
    "            \"pc_patch_construct_dim\": 0,\n",
    "            # Topological decomposition\n",
    "            \"pc_patch_construct_type\": \"vanka\",\n",
    "            # Pressure space is constraint space\n",
    "            \"pc_patch_exclude_subspaces\": 1,\n",
    "            # Configure the solver on each patch\n",
    "            \"pc_patch_sub\": {\n",
    "                \"mat_type\": \"dense\",\n",
    "                \"ksp_type\": \"preonly\",\n",
    "                \"pc_type\": \"lu\",\n",
    "                \"pc_factor_shift_type\": \"nonzero\",\n",
    "            }\n",
    "        }\n",
    "    },\n",
    "    \"mg_coarse\": {\n",
    "        \"ksp_type\": \"preonly\",\n",
    "        \"pc_type\": \"python\",\n",
    "        \"pc_python_type\": \"firedrake.AssembledPC\",\n",
    "        \"assembled\": {\n",
    "            \"pc_type\": \"lu\",\n",
    "            \"pc_factor_mat_solver_type\": \"mumps\",\n",
    "        }\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The solver can be invoked as below, but frequently crashes Jupyter notebooks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#w.assign(0)\n",
    "#solver = create_solver(vanka_parameters)\n",
    "#solver.solve()\n",
    "#convergence(solver)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}