{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "82687fa3",
   "metadata": {},
   "source": [
    "```{try_on_binder}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d6a48fa0",
   "metadata": {
    "load": "myst_code_init.py",
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The pymor.discretizers.builtin.gui.jupyter extension is already loaded. To reload it, use:\n",
      "  %reload_ext pymor.discretizers.builtin.gui.jupyter\n"
     ]
    }
   ],
   "source": [
    "from IPython import get_ipython\n",
    "ip = get_ipython()\n",
    "if ip is not None:\n",
    "    ip.run_line_magic('load_ext', 'pymor.discretizers.builtin.gui.jupyter')\n",
    "    ip.run_line_magic('matplotlib', 'inline')\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning, module='torch')\n",
    "import pymor.tools.random\n",
    "pymor.tools.random._default_random_state = None\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ee94719",
   "metadata": {},
   "source": [
    "# Tutorial: Model order reduction for PDE-constrained optimization problems\n",
    "\n",
    "A typical application of model order reduction for PDEs are\n",
    "PDE-constrained parameter optimization problems. These problems aim to\n",
    "find a local minimizer of an objective functional depending on an\n",
    "underlying PDE which has to be solved for all evaluations.\n",
    "A prototypical example of a PDE-constrained optimization problem can be defined\n",
    "in the following way. For a physical domain {math}`\\Omega \\subset \\mathbb{R}^d` and a\n",
    "parameter set {math}`\\mathcal{P} \\subset \\mathbb{R}^P`, we want to find\n",
    "a solution of the minimization problem\n",
    "\n",
    "```{math}\n",
    "\\min_{\\mu \\in \\mathcal{P}} J(u_{\\mu}, \\mu),  \\tag{P.a}\n",
    "```\n",
    "\n",
    "where {math}`u_{\\mu} \\in V := H^1_0(\\Omega)` is the solution of\n",
    "\n",
    "```{math}\n",
    "\\begin{equation} \\label{eq:primal}\n",
    "a_{\\mu}(u_{\\mu}, v) = f_{\\mu}(v) \\qquad \\forall \\, v \\in V \\tag{P.b}.\n",
    "\\end{equation}\n",
    "```\n",
    "\n",
    "The equation {math}`\\eqref{eq:primal}` is called the primal\n",
    "equation and can be arbitrarily complex. MOR methods in the context of\n",
    "PDE-constrained optimization problems thus aim to find a surrogate model\n",
    "of {math}`\\eqref{eq:primal}` to reduce the computational costs of\n",
    "an evaluation of {math}`J(u_{\\mu}, \\mu)`.\n",
    "\n",
    "If there exists a unique solution {math}`u_{\\mu}` for all\n",
    "{math}`\\mu \\in \\mathcal{P}`, we can rewrite (P) by using the so-called\n",
    "reduced objective functional {math}`\\mathcal{J}(\\mu):= J(u_{\\mu}, \\mu)`\n",
    "leading to the equivalent problem: Find a solution of\n",
    "\n",
    "```{math}\n",
    "\\min_{\\mu \\in \\mathcal{P}} \\mathcal{J}(\\mu).  \\tag{$\\hat{P}$}\n",
    "```\n",
    "\n",
    "There exist plenty of different methods to solve ({math}`\\hat{P}`) by\n",
    "using MOR methods. Some of them rely on an RB method with traditional\n",
    "offline/online splitting, which typically result in a very online\n",
    "efficient approach. Recent research also tackles overall efficiency by\n",
    "overcoming the expensive offline phase, which we will discuss further\n",
    "below.\n",
    "\n",
    "In this tutorial, we use a simple linear scalar valued objective functional\n",
    "and an elliptic primal equation to compare different approaches that solve\n",
    "({math}`\\hat{P}`).\n",
    "\n",
    "## An elliptic model problem with a linear objective functional\n",
    "\n",
    "We consider a domain {math}`\\Omega:= [-1, 1]^2`, a parameter set\n",
    "{math}`\\mathcal{P} := [0,\\pi]^2` and the elliptic equation\n",
    "\n",
    "```{math}\n",
    "- \\nabla \\cdot \\big( \\lambda(\\mu) \\nabla u_\\mu \\big) = l\n",
    "```\n",
    "\n",
    "with data functions\n",
    "\n",
    "```{math}\n",
    "\\begin{align}\n",
    "l(x, y) &= \\tfrac{1}{2} \\pi^2 \\cos(\\tfrac{1}{2} \\pi x) \\cos(\\tfrac{1}{2} \\pi y),\\\\\n",
    "\\lambda(\\mu) &= \\theta_0(\\mu) \\lambda_0 + \\theta_1(\\mu) \\lambda_1,\\\\\n",
    "\\theta_0(\\mu) &= 1.1 + \\sin(\\mu_0)\\mu_1,\\\\\n",
    "\\theta_1(\\mu) &= 1.1 + \\sin(\\mu_1),\\\\\n",
    "\\lambda_0 &= \\chi_{\\Omega \\backslash \\omega},\\\\\n",
    "\\lambda_1 &= \\chi_\\omega,\\\\\n",
    "\\omega &:= [-\\tfrac{2}{3}, -\\tfrac{1}{3}]^2 \\cup ([-\\tfrac{2}{3}, -\\tfrac{1}{3}] \\times [\\tfrac{1}{3}, \\tfrac{2}{3}]).\n",
    "\\end{align}\n",
    "```\n",
    "\n",
    "The diffusion is thus given as the linear combination of scaled\n",
    "indicator functions where {math}`\\omega` is defined by two blocks in the\n",
    "left half of the domain, roughly where the `w` is here:\n",
    "\n",
    "```\n",
    "+-----------+\n",
    "|           |\n",
    "|  w        |\n",
    "|           |\n",
    "|  w        |\n",
    "|           |\n",
    "+-----------+\n",
    "```\n",
    "\n",
    "From the definition above we can easily deduce the bilinear form\n",
    "{math}`a_{\\mu}` and the linear functional {math}`f_{\\mu}` for the primal\n",
    "equation. Moreover, we consider the linear objective functional\n",
    "\n",
    "```{math}\n",
    "\n",
    "\\mathcal{J}(\\mu) := \\theta_{\\mathcal{J}}(\\mu)\\, f_\\mu(u_\\mu)\n",
    "\n",
    "```\n",
    "\n",
    "where {math}`\\theta_{\\mathcal{J}}(\\mu) := 1 + \\frac{1}{5}(\\mu_0 + \\mu_1)`.\n",
    "\n",
    "With this data, we can construct a {{ StationaryProblem }} in pyMOR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "259e1f46",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymor.basic import *\n",
    "import numpy as np\n",
    "\n",
    "domain = RectDomain(([-1,-1], [1,1]))\n",
    "indicator_domain = ExpressionFunction(\n",
    "    '(-2/3. <= x[0]) * (x[0] <= -1/3.) * (-2/3. <= x[1]) * (x[1] <= -1/3.) * 1. \\\n",
    "   + (-2/3. <= x[0]) * (x[0] <= -1/3.) *  (1/3. <= x[1]) * (x[1] <=  2/3.) * 1.',\n",
    "    dim_domain=2)\n",
    "rest_of_domain = ConstantFunction(1, 2) - indicator_domain\n",
    "\n",
    "l = ExpressionFunction('0.5*pi*pi*cos(0.5*pi*x[0])*cos(0.5*pi*x[1])', dim_domain=2)\n",
    "\n",
    "parameters = {'diffusion': 2}\n",
    "thetas = [ExpressionParameterFunctional('1.1 + sin(diffusion[0])*diffusion[1]', parameters,\n",
    "                                       derivative_expressions={'diffusion': ['cos(diffusion[0])*diffusion[1]',\n",
    "                                                                             'sin(diffusion[0])']}),\n",
    "          ExpressionParameterFunctional('1.1 + sin(diffusion[1])', parameters,\n",
    "                                       derivative_expressions={'diffusion': ['0',\n",
    "                                                                             'cos(diffusion[1])']}),\n",
    "\n",
    "                                       ]\n",
    "diffusion = LincombFunction([rest_of_domain, indicator_domain], thetas)\n",
    "\n",
    "theta_J = ExpressionParameterFunctional('1 + 1/5 * diffusion[0] + 1/5 * diffusion[1]', parameters,\n",
    "                                        derivative_expressions={'diffusion': ['1/5','1/5']})\n",
    "\n",
    "problem = StationaryProblem(domain, l, diffusion, outputs=[('l2', l * theta_J)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f67e7292",
   "metadata": {},
   "source": [
    "We now use pyMOR's builtin discretization toolkit (see {doc}`tutorial_builtin_discretizer`)\n",
    "to construct a full order {{ StationaryModel }}. Since we intend to use a fixed\n",
    "energy norm\n",
    "\n",
    "```{math}\n",
    "\\|\\,.\\|_{\\bar{\\mu}} : = a_{\\,\\bar{\\mu}}(.,.),\n",
    "```\n",
    "\n",
    "we also define {math}`\\bar{\\mu}`, which we pass via the argument\n",
    "`mu_energy_product`. Also, we define the parameter space\n",
    "{math}`\\mathcal{P}` on which we want to optimize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8ad15211",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cdf6e8d1e2124f84b1cf05d084fbf958",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu_bar = problem.parameters.parse([np.pi/2,np.pi/2])\n",
    "\n",
    "fom, data = discretize_stationary_cg(problem, diameter=1/50, mu_energy_product=mu_bar)\n",
    "parameter_space = fom.parameters.space(0, np.pi)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68106fdf",
   "metadata": {},
   "source": [
    "We now define the first function for the output of the model that will be used by the minimizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "64371788",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fom_objective_functional(mu):\n",
    "    return fom.output(mu)[0, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47dfcff6",
   "metadata": {},
   "source": [
    "We also pick a starting parameter for the optimization method,\n",
    "which in our case is {math}`\\mu^0 = (0.25, 0.5)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4dd9c7c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_guess = [0.25, 0.5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05f4efc9",
   "metadata": {},
   "source": [
    "Next, we visualize the diffusion function {math}`\\lambda_\\mu` by using\n",
    "{class}`~pymor.discretizers.builtin.cg.InterpolationOperator` for interpolating it on the grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f92330de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pymor.discretizers.builtin.cg import InterpolationOperator\n",
    "\n",
    "diff = InterpolationOperator(data['grid'], problem.diffusion).as_vector(fom.parameters.parse(initial_guess))\n",
    "fom.visualize(diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b6c677bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tria-Grid on domain [-1,1] x [-1,1]\n",
      "x0-intervals: 100, x1-intervals: 100\n",
      "elements: 40000, edges: 60200, vertices: 20201\n"
     ]
    }
   ],
   "source": [
    "print(data['grid'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "647ebca7",
   "metadata": {},
   "source": [
    "We can see that our FOM model has 20201 DoFs which just about suffices\n",
    "to resolve the data structure in the diffusion. This suggests to use an\n",
    "even finer mesh. However, for enabling a faster runtime for this\n",
    "tutorial, we stick with this mesh and remark that refining the mesh does\n",
    "not change the interpretation of the methods that are discussed below.\n",
    "It rather further improves the speedups achieved by model reduction.\n",
    "\n",
    "Before we discuss the first optimization method, we define helpful\n",
    "functions for visualizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c44c26b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.figsize'] = (12.0, 8.0)\n",
    "mpl.rcParams['font.size'] = 12\n",
    "mpl.rcParams['savefig.dpi'] = 300\n",
    "mpl.rcParams['figure.subplot.bottom'] = .1\n",
    "mpl.rcParams['axes.facecolor'] = (0.0, 0.0, 0.0, 0.0)\n",
    "\n",
    "from mpl_toolkits.mplot3d import Axes3D # required for 3d plots\n",
    "from matplotlib import cm # required for colors\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from time import perf_counter\n",
    "\n",
    "def compute_value_matrix(f, x, y):\n",
    "    f_of_x = np.zeros((len(x), len(y)))\n",
    "    for ii in range(len(x)):\n",
    "        for jj in range(len(y)):\n",
    "            f_of_x[ii][jj] = f((x[ii], y[jj]))\n",
    "    x, y = np.meshgrid(x, y)\n",
    "    return x, y, f_of_x\n",
    "\n",
    "def plot_3d_surface(f, x, y, alpha=1):\n",
    "    X, Y = x, y\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111, projection='3d')\n",
    "    x, y, f_of_x = compute_value_matrix(f, x, y)\n",
    "    ax.plot_surface(x, y, f_of_x, cmap='Blues',\n",
    "                    linewidth=0, antialiased=False, alpha=alpha)\n",
    "    ax.view_init(elev=27.7597402597, azim=-39.6370967742)\n",
    "    ax.set_xlim3d([-0.10457963, 3.2961723])\n",
    "    ax.set_ylim3d([-0.10457963, 3.29617229])\n",
    "    return ax\n",
    "\n",
    "def addplot_xy_point_as_bar(ax, x, y, color='orange', z_range=None):\n",
    "    ax.plot([y, y], [x, x], z_range if z_range else ax.get_zlim(), color)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9dcea36",
   "metadata": {},
   "source": [
    "Now, we can visualize the objective functional on the parameter space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3dd8efbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5b2cc50d52ab4ecba7b7842fce009bf6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes3D: >"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ranges = parameter_space.ranges['diffusion']\n",
    "XX = np.linspace(ranges[0] + 0.05, ranges[1], 10)\n",
    "\n",
    "plot_3d_surface(fom_objective_functional, XX, XX)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4e155d0",
   "metadata": {},
   "source": [
    "Taking a closer look at the functional, we see that it is at least\n",
    "locally convex with a locally unique minimum. In general, however,\n",
    "PDE-constrained optimization problems are not convex. In our case\n",
    "changing the parameter functional {math}`\\theta_{\\mathcal{J}}` can\n",
    "already result in a very different non-convex output functional.\n",
    "\n",
    "In order to record some data during the optimization, we also define\n",
    "helpful functions for recording and reporting the results in this tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4a506715",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_data(offline_time=False, enrichments=False):\n",
    "    data = {'num_evals': 0, 'evaluations' : [], 'evaluation_points': [], 'time': np.inf}\n",
    "    if offline_time:\n",
    "        data['offline_time'] = offline_time\n",
    "    if enrichments:\n",
    "        data['enrichments'] = 0\n",
    "    return data\n",
    "\n",
    "def record_results(function, data, adaptive_enrichment=False, opt_dict=None, mu=None):\n",
    "    if adaptive_enrichment:\n",
    "        # this is for the adaptive case! rom is shiped via the opt_dict argument.\n",
    "        assert opt_dict is not None\n",
    "        QoI, data, rom = function(mu, data, opt_dict)\n",
    "        opt_dict['opt_rom'] = rom\n",
    "    else:\n",
    "        QoI = function(mu)\n",
    "    data['num_evals'] += 1\n",
    "    data['evaluation_points'].append(mu)\n",
    "    data['evaluations'].append(QoI)\n",
    "    return QoI\n",
    "\n",
    "def report(result, data, reference_mu=None):\n",
    "    if (result.status != 0):\n",
    "        print('\\n failed!')\n",
    "    else:\n",
    "        print('\\n succeeded!')\n",
    "        print(f'  mu_min:    {fom.parameters.parse(result.x)}')\n",
    "        print(f'  J(mu_min): {result.fun}')\n",
    "        if reference_mu is not None:\n",
    "            print(f'  absolute error w.r.t. reference solution: {np.linalg.norm(result.x-reference_mu):.2e}')\n",
    "        print(f'  num iterations:     {result.nit}')\n",
    "        print(f'  num function calls: {data[\"num_evals\"]}')\n",
    "        print(f'  time:               {data[\"time\"]:.5f} seconds')\n",
    "        if 'offline_time' in data:\n",
    "                print(f'  offline time:       {data[\"offline_time\"]:.5f} seconds')\n",
    "        if 'enrichments' in data:\n",
    "                print(f'  model enrichments:  {data[\"enrichments\"]}')\n",
    "    print('')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e51bd148",
   "metadata": {},
   "source": [
    "## Optimizing with the FOM using finite differences\n",
    "\n",
    "There exist plenty optimization methods, and this tutorial is not meant\n",
    "to discuss the design and implementation of optimization methods. We\n",
    "simply use the {func}`~scipy.optimize.minimize` function\n",
    "from `scipy.optimize` and use the\n",
    "builtin `L-BFGS-B` routine which is a quasi-Newton method that can\n",
    "also handle a constrained parameter space. For the whole tutorial, we define the\n",
    "optimization function as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7b057d6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import partial\n",
    "from scipy.optimize import minimize\n",
    "\n",
    "def optimize(J, data, ranges, gradient=False, adaptive_enrichment=False, opt_dict=None):\n",
    "    tic = perf_counter()\n",
    "    result = minimize(partial(record_results, J, data, adaptive_enrichment, opt_dict),\n",
    "                      initial_guess,\n",
    "                      method='L-BFGS-B', jac=gradient,\n",
    "                      bounds=(ranges, ranges),\n",
    "                      options={'ftol': 1e-15, 'gtol': 5e-5})\n",
    "    data['time'] = perf_counter()-tic\n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3534cf07",
   "metadata": {},
   "source": [
    "It is optional to give an expression for the gradient of the objective\n",
    "functional to the {func}`~scipy.optimize.minimize` function.\n",
    "In case no gradient is given, {func}`~scipy.optimize.minimize`\n",
    "just approximates the gradient with finite differences.\n",
    "This is not recommended because the gradient is inexact and the\n",
    "computation of finite differences requires even more evaluations of the\n",
    "primal equation. Here, we use this approach for a simple demonstration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f761cc21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9a95642234a242bda4f7ddcf31041c75",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reference_minimization_data = prepare_data()\n",
    "\n",
    "fom_result = optimize(fom_objective_functional, reference_minimization_data, ranges)\n",
    "\n",
    "reference_mu = fom_result.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "59668252",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " succeeded!\n",
      "  mu_min:    {diffusion: [1.4246801734272179, 3.141592653589793]}\n",
      "  J(mu_min): 2.3917078763077035\n",
      "  num iterations:     7\n",
      "  num function calls: 27\n",
      "  time:               2.86998 seconds\n",
      "\n"
     ]
    }
   ],
   "source": [
    "report(fom_result, reference_minimization_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87710243",
   "metadata": {},
   "source": [
    "Taking a look at the result, we see that the optimizer needs {math}`7`\n",
    "iterations to converge, but actually needs {math}`27` evaluations of the\n",
    "full order model. Obviously, this is related to the computation of the\n",
    "finite differences. We can visualize the optimization path by plotting\n",
    "the chosen points during the minimization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c542a8ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bce2dd8dcea24bfa8de24990b3f09c9f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reference_plot = plot_3d_surface(fom_objective_functional, XX, XX, alpha=0.5)\n",
    "\n",
    "for mu in reference_minimization_data['evaluation_points']:\n",
    "    addplot_xy_point_as_bar(reference_plot, mu[0], mu[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeb16ca5",
   "metadata": {},
   "source": [
    "## Optimizing with the ROM using finite differences\n",
    "\n",
    "We can use a standard RB method to build a surrogate model for the FOM.\n",
    "As a result, the solution of the primal equation is no longer expensive\n",
    "and the optimization method can evaluate the objective functional quickly.\n",
    "For this, we define a standard {class}`~pymor.reductors.coercive.CoerciveRBReductor`\n",
    "and use the {class}`~pymor.parameters.functionals.MinThetaParameterFunctional` for an\n",
    "estimation of the coerciviy constant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8c839451",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymor.algorithms.greedy import rb_greedy\n",
    "from pymor.parameters.functionals import MinThetaParameterFunctional\n",
    "from pymor.reductors.coercive import CoerciveRBReductor\n",
    "\n",
    "coercivity_estimator = MinThetaParameterFunctional(fom.operator.coefficients, mu_bar)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95c70de4",
   "metadata": {},
   "source": [
    "The online efficiency of MOR methods most likely comes with a\n",
    "rather expensive offline phase. For PDE-constrained optimization, however,\n",
    "it is not meaningful to ignore the\n",
    "offline time of the surrogate model since it can happen that FOM\n",
    "optimization methods would already converge before the surrogate model\n",
    "is even ready. Thus, RB optimization methods (at least for only one\n",
    "configuration) aims for overall efficiency which includes offline and\n",
    "online time. Of course, this effect aggravates if the parameter space is\n",
    "high dimensional because the offline phase can increase even more.\n",
    "\n",
    "In order to decrease the offline time we guess that we may not require\n",
    "a perfect surrogate model in the sense that a low error tolerance for\n",
    "the {func}`~pymor.algorithms.greedy.rb_greedy` already suffices to converge\n",
    "to the same minimum.\n",
    "In our case we choose `atol=1e-2` and yield a very low dimensional space.\n",
    "In general, however, it is not a priori clear how to choose `atol`\n",
    "in order to arrive at a minimum which is close enough to the true\n",
    "optimum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "51bfcc0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c176074214434602a5445e2934e08076",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RB system is of size 3x3\n",
      "maximum estimated model reduction error over training set: 0.00935354954250117\n"
     ]
    }
   ],
   "source": [
    "training_set = parameter_space.sample_uniformly(25)\n",
    "\n",
    "RB_reductor = CoerciveRBReductor(fom, product=fom.energy_product, coercivity_estimator=coercivity_estimator)\n",
    "RB_greedy_data = rb_greedy(fom, RB_reductor, training_set, atol=1e-2)\n",
    "\n",
    "num_RB_greedy_extensions = RB_greedy_data['extensions']\n",
    "RB_greedy_mus, RB_greedy_errors = RB_greedy_data['max_err_mus'], RB_greedy_data['max_errs']\n",
    "rom = RB_greedy_data['rom']\n",
    "\n",
    "print(f'RB system is of size {num_RB_greedy_extensions}x{num_RB_greedy_extensions}')\n",
    "print(f'maximum estimated model reduction error over training set: {RB_greedy_errors[-1]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25f07b96",
   "metadata": {},
   "source": [
    "We can see that greedy algorithm already stops after {math}`3` basis functions.\n",
    "Next, we plot the chosen parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "db038724",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "44ad4592a0cc485086d8037d8313354a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plot_3d_surface(fom_objective_functional, XX, XX, alpha=0.5)\n",
    "\n",
    "for mu in RB_greedy_mus[:-1]:\n",
    "    mu = mu.to_numpy()\n",
    "    addplot_xy_point_as_bar(ax, mu[0], mu[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4471073",
   "metadata": {},
   "source": [
    "Analogously to above, we perform the same optimization method, but use\n",
    "the resulting ROM objective functional."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "94fcff3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rom_objective_functional(mu):\n",
    "    return rom.output(mu)[0, 0]\n",
    "\n",
    "RB_minimization_data = prepare_data(offline_time=RB_greedy_data['time'])\n",
    "\n",
    "rom_result = optimize(rom_objective_functional, RB_minimization_data, ranges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ffbc2345",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " succeeded!\n",
      "  mu_min:    {diffusion: [1.424654211984247, 3.141592653589793]}\n",
      "  J(mu_min): 2.3917078350963017\n",
      "  absolute error w.r.t. reference solution: 2.60e-05\n",
      "  num iterations:     7\n",
      "  num function calls: 27\n",
      "  time:               0.01420 seconds\n",
      "  offline time:       1.93975 seconds\n",
      "\n"
     ]
    }
   ],
   "source": [
    "report(rom_result, RB_minimization_data, reference_mu)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "411c6880",
   "metadata": {},
   "source": [
    "Comparing the result to the FOM model, we see that the number of\n",
    "iterations and evaluations of the model are equal. As expected,\n",
    "we see that the optmization routine is very fast because the surrogate\n",
    "enables almost instant evaluations of the primal equation.\n",
    "\n",
    "As mentioned above, we should not forget that we required the offline\n",
    "time to build our surrogate. In our case, the offline time is still low\n",
    "enough to get a speed up over the FOM optimization. Luckily,\n",
    "`atol=1e-2` was enough to achieve an absolute error of roughly `1e-06`\n",
    "but it is important to notice that we do not know this error before\n",
    "choosing `atol`.\n",
    "\n",
    "To show that the ROM optimization roughly followed the same path as the\n",
    "FOM optimization, we visualize both of them in the following plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "80a186fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a4801a36f6dd4ad3b7b4b8b66802b751",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reference_plot = plot_3d_surface(fom_objective_functional, XX, XX, alpha=0.5)\n",
    "reference_plot_mean_z_lim = 0.5*(reference_plot.get_zlim()[0] + reference_plot.get_zlim()[1])\n",
    "\n",
    "for mu in reference_minimization_data['evaluation_points']:\n",
    "    addplot_xy_point_as_bar(reference_plot, mu[0], mu[1], color='green',\n",
    "                            z_range=(reference_plot.get_zlim()[0], reference_plot_mean_z_lim))\n",
    "\n",
    "for mu in RB_minimization_data['evaluation_points']:\n",
    "    addplot_xy_point_as_bar(reference_plot, mu[0], mu[1], color='orange',\n",
    "                           z_range=(reference_plot_mean_z_lim, reference_plot.get_zlim()[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30a9fda5",
   "metadata": {},
   "source": [
    "## Computing the gradient of the objective functional\n",
    "\n",
    "A major issue of using finite differences for computing the gradient of\n",
    "the objective functional is the number of evaluations of the objective\n",
    "functional. In the FOM example from above we saw that\n",
    "many evaluations of the model were only due to the\n",
    "computation of the finite differences. If the problem is more complex\n",
    "and the mesh is finer, this can lead to a serious waste of\n",
    "computational time. Also from an optimizational point of view it is\n",
    "always better to compute the true gradient of the objective functional.\n",
    "\n",
    "For computing the gradient of the linear objective functional\n",
    "{math}`\\mathcal{J}(\\mu)`, we can write for every direction\n",
    "{math}`i= 1, \\dots, P`\n",
    "\n",
    "```{math}\n",
    "\\begin{align} \\label{gradient:sens} \\tag{1}\n",
    "d_{\\mu_i} \\mathcal{J}(\\mu) = \\partial_{\\mu_i} J(u_{\\mu}, \\mu) + \\partial_u J(u_{\\mu}, \\mu)[d_{\\mu_i} u_{\\mu}]\n",
    "   =   \\partial_{\\mu_i} J(u_{\\mu}, \\mu) + J(d_{\\mu_i} u_{\\mu}, \\mu)\n",
    "\\end{align}\n",
    "```\n",
    "\n",
    "Thus, we need to compute the derivative of the\n",
    "solution {math}`u_{\\mu}` (also called sensitivity). For this, we need to\n",
    "solve another equation: Find {math}`d_{\\mu_i} u_{\\mu} \\in V`, such that\n",
    "\n",
    "```{math}\n",
    " \\label{sens} \\tag{2}\n",
    "a_\\mu(d_{\\mu_i} u_{\\mu}, v) = \\partial_{\\mu_i} r_\\mu^{\\text{pr}}(u_{\\mu})[v] \\qquad \\qquad \\forall v \\in V\n",
    "```\n",
    "\n",
    "where {math}`r_\\mu^{\\text{pr}}` denotes the residual of the primal\n",
    "equation, i.e.\n",
    "\n",
    "```{math}\n",
    "r_\\mu^{\\text{pr}}(u)[v] := l_\\mu(v) - a_\\mu(u, v) \\qquad \\qquad \\text{for all }v \\in V\n",
    "```\n",
    "\n",
    "A major issue of this approach is that the computation of the\n",
    "full gradient requires {math}`P` solutions of {math}`\\eqref{sens}`.\n",
    "Especially for high dimensional parameter spaces, we can instead use an\n",
    "adjoint approach to reduce the computational cost to only one solution\n",
    "of an additional problem.\n",
    "\n",
    "The adjoint approach relies on the Lagrangian of the objective\n",
    "functional\n",
    "\n",
    "```{math}\n",
    "\\mathcal{L}(u, \\mu, p) = J(u, \\mu) + r_\\mu^{\\text{pr}}(u, p)\n",
    "```\n",
    "\n",
    "where {math}`p \\in V` is the adjoint variable. Deriving optimality\n",
    "conditions for {math}`\\mathcal{L}`, we end up with the dual equation:\n",
    "Find {math}`p_{\\mu} \\in V`, such that\n",
    "\n",
    "```{math}\n",
    " \\label{dual} \\tag{3}\n",
    "a_\\mu(v, p_\\mu) = \\partial_u J(u_\\mu, \\mu)[v]\n",
    "= J(v, \\mu)\n",
    "```\n",
    "\n",
    "Note that in our case, we then have\n",
    "{math}`\\mathcal{L}(u_{\\mu}, \\mu, p_{\\mu}) = J(u, \\mu)` because the\n",
    "residual term {math}`r_\\mu^{\\text{pr}}(u_{\\mu}, p_{\\mu})` vanishes since {math}`u_{\\mu}`\n",
    "solves {math}`\\eqref{eq:primal}` and {math}`p_{\\mu}` is in the test space {math}`V`. By\n",
    "using the solution of the dual problem, we can then derive the gradient of the objective\n",
    "functional by\n",
    "\n",
    "```{math}\n",
    "\\begin{align}\n",
    "d_{\\mu_i} \\mathcal{J}(\\mu) &= \\partial_{\\mu_i} J(u_{\\mu}, \\mu) + \\partial_u J(u_{\\mu}, \\mu)[d_{\\mu_i} u_{\\mu}] \\\\\n",
    "   &=   \\partial_{\\mu_i} J(u_{\\mu}, \\mu) + a_\\mu(d_{\\mu_i} u_{\\mu}, p_\\mu) \\\\\n",
    "   &=   \\partial_{\\mu_i} J(u_{\\mu}, \\mu) + \\partial_{\\mu_i} r_\\mu^{\\text{pr}}(d_{\\mu_i} u_{\\mu})[p_\\mu]\n",
    "\\end{align}\n",
    "```\n",
    "\n",
    "We conclude that we only need to solve for {math}`u_{\\mu}` and\n",
    "{math}`p_{\\mu}` if we want to compute the gradient with the adjoint\n",
    "approach. For more information on this approach we refer to Section 1.6.2 in {cite}`HPUU09`.\n",
    "\n",
    "We now intend to use the gradient to speed up the optimization methods\n",
    "from above. All technical requirements are\n",
    "already available in pyMOR.\n",
    "\n",
    "## Optimizing using a gradient in FOM\n",
    "\n",
    "We can easily include a function to compute the gradient to {func}`~scipy.optimize.minimize`.\n",
    "Since we use a linear operator and a linear objective functional, the `use_adjoint` argument\n",
    "is automatically enabled.\n",
    "Note that using the (more general) implementation `use_adjoint=False` results\n",
    "in the exact same gradient but lacks computational speed.\n",
    "Moreover, the function `output_d_mu` returns a dict w.r.t. the parameters as default.\n",
    "In order to use the output for {func}`~scipy.optimize.minimize` we thus use the `return_array=True` argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b7b7fd17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "430fb84a1923442fa151cb8d0564dd0b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def fom_gradient_of_functional(mu):\n",
    "    return fom.output_d_mu(fom.parameters.parse(mu), return_array=True, use_adjoint=True)\n",
    "\n",
    "opt_fom_minimization_data = prepare_data()\n",
    "\n",
    "opt_fom_result = optimize(fom_objective_functional, opt_fom_minimization_data, ranges,\n",
    "                          gradient=fom_gradient_of_functional)\n",
    "\n",
    "# update the reference_mu because this is more accurate!\n",
    "reference_mu = opt_fom_result.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e8e31823",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " succeeded!\n",
      "  mu_min:    {diffusion: [1.4246556963614603, 3.141592653589793]}\n",
      "  J(mu_min): 2.3917078762139408\n",
      "  num iterations:     7\n",
      "  num function calls: 9\n",
      "  time:               3.02843 seconds\n",
      "\n"
     ]
    }
   ],
   "source": [
    "report(opt_fom_result, opt_fom_minimization_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1c5e588",
   "metadata": {},
   "source": [
    "With respect to the FOM result with finite differences, we see that we\n",
    "have a saved the evaluations for computing the gradient. Of course it is also not for free to\n",
    "compute the gradient, but since we are using the dual approach, this will only scale with the\n",
    "factor 2. Furthermore, we can expect that the result above is more accurate which is why we\n",
    "choose it as the reference parameter.\n",
    "\n",
    "## Optimizing using a gradient in ROM\n",
    "\n",
    "Obviously, we can also include the gradient of the ROM version of the\n",
    "output functional."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0976a991",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rom_gradient_of_functional(mu):\n",
    "    return rom.output_d_mu(rom.parameters.parse(mu), return_array=True, use_adjoint=True)\n",
    "\n",
    "opt_rom_minimization_data = prepare_data(offline_time=RB_greedy_data['time'])\n",
    "\n",
    "opt_rom_result = optimize(rom_objective_functional, opt_rom_minimization_data, ranges,\n",
    "                          gradient=rom_gradient_of_functional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8bc7ead3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " succeeded!\n",
      "  mu_min:    {diffusion: [1.4246542368580375, 3.141592653589793]}\n",
      "  J(mu_min): 2.391707835095847\n",
      "  absolute error w.r.t. reference solution: 1.46e-06\n",
      "  num iterations:     7\n",
      "  num function calls: 9\n",
      "  time:               0.02459 seconds\n",
      "  offline time:       1.93975 seconds\n",
      "\n"
     ]
    }
   ],
   "source": [
    "report(opt_rom_result, opt_rom_minimization_data, reference_mu)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "817d26d2",
   "metadata": {},
   "source": [
    "The online phase is even faster but the offline time of course remains the same.\n",
    "We also conclude that the ROM model eventually gives less speedup by using a better optimization\n",
    "method for the FOM and ROM.\n",
    "\n",
    "## Beyond the traditional offline/online splitting: enrich along the path of optimization\n",
    "\n",
    "We already figured out that the main drawback for using RB methods in the\n",
    "context of optimization is the expensive offline time to build the\n",
    "surrogate model. In the example above, we overcame this issue by\n",
    "choosing a large tolerance `atol`. As a result, we cannot be sure\n",
    "that our surrogate model is accurate enough for our purpuses. In other\n",
    "words, either we invest too much time to build an accurate model or we\n",
    "face the danger of reducing with a bad surrogate for the whole parameter\n",
    "space. Thinking about this issue again, it is important to notice that\n",
    "we are solving an optimization problem which will eventually converge to\n",
    "a certain parameter. Thus, it only matters that the surrogate is good in\n",
    "this particular region as long as we are able to arrive at it. This\n",
    "gives hope that there must exist a more efficient way of using RB\n",
    "methods without trying to approximate the FOM across the\n",
    "whole parameter space.\n",
    "\n",
    "One possible way for advanced RB methods is a reduction along the path\n",
    "of optimization. The idea is that we start with an empty basis and only\n",
    "enrich the model with the parameters that we will arive at. This\n",
    "approach goes beyond the classical offline/online splitting of RB\n",
    "methods since it entirely skips the offline phase. In the following\n",
    "code, we will test this method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "251fcf9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "pdeopt_reductor = CoerciveRBReductor(\n",
    "    fom, product=fom.energy_product, coercivity_estimator=coercivity_estimator)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1588eac",
   "metadata": {},
   "source": [
    "In the next function, we implement the above mentioned way of enriching\n",
    "the basis along the path of optimization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8f692048",
   "metadata": {},
   "outputs": [],
   "source": [
    "def enrich_and_compute_objective_function(mu, data, opt_dict):\n",
    "    U = fom.solve(mu)\n",
    "    try:\n",
    "        pdeopt_reductor.extend_basis(U)\n",
    "        data['enrichments'] += 1\n",
    "    except:\n",
    "        print('Extension failed')\n",
    "    opt_rom = pdeopt_reductor.reduce()\n",
    "    QoI = opt_rom.output(mu)\n",
    "    return QoI, data, opt_rom\n",
    "\n",
    "def compute_gradient_with_opt_rom(opt_dict, mu):\n",
    "    opt_rom = opt_dict['opt_rom']\n",
    "    return opt_rom.output_d_mu(opt_rom.parameters.parse(mu), return_array=True, use_adjoint=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dceab85",
   "metadata": {},
   "source": [
    "With this definitions, we can start the optimization method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "054ba947",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2b2f9f4ec2f5436e8bb2dfd48bcc1556",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "opt_along_path_minimization_data = prepare_data(enrichments=True)\n",
    "\n",
    "opt_dict = {}\n",
    "opt_along_path_result = optimize(enrich_and_compute_objective_function,\n",
    "                                 opt_along_path_minimization_data, ranges,\n",
    "                                 gradient=partial(compute_gradient_with_opt_rom, opt_dict),\n",
    "                                 adaptive_enrichment=True, opt_dict=opt_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "966abb6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " succeeded!\n",
      "  mu_min:    {diffusion: [1.4246556963614518, 3.141592653589793]}\n",
      "  J(mu_min): 2.391707876213646\n",
      "  absolute error w.r.t. reference solution: 8.44e-15\n",
      "  num iterations:     7\n",
      "  num function calls: 9\n",
      "  time:               1.75243 seconds\n",
      "  model enrichments:  9\n",
      "\n"
     ]
    }
   ],
   "source": [
    "report(opt_along_path_result, opt_along_path_minimization_data, reference_mu)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f342eda",
   "metadata": {},
   "source": [
    "The computational time looks at least better than the FOM optimization\n",
    "and we are very close to the reference parameter.\n",
    "But we are following the exact same path than the\n",
    "FOM and thus we need to solve the FOM model as often as before\n",
    "(due to the enrichments). The only computational time that we safe is the one\n",
    "for the gradients since we compute the dual solutions with the ROM.\n",
    "\n",
    "## Adaptively enriching along the path\n",
    "\n",
    "In order to further speedup the above algorithm, we enhance it\n",
    "by only adaptive enrichments of the model.\n",
    "For instance it may happen that the model is already good at\n",
    "the next iteration, which we can easily check by evaluating the standard\n",
    "error estimator which is also used in the greedy algorithm. In the next\n",
    "example we will implement this adaptive way of enriching and set a\n",
    "tolerance which is equal to the one that we had as error tolerance\n",
    "in the greedy algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6bce6ec1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "17be6f98b9ee4350a026121e8520a76a",
       "version_major": 2,
       "version_minor": 0
      },
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       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdeopt_reductor = CoerciveRBReductor(\n",
    "    fom, product=fom.energy_product, coercivity_estimator=coercivity_estimator)\n",
    "opt_rom = pdeopt_reductor.reduce()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b52ee674",
   "metadata": {},
   "outputs": [],
   "source": [
    "def enrich_adaptively_and_compute_objective_function(mu, data, opt_dict):\n",
    "    opt_rom = opt_dict['opt_rom']\n",
    "    primal_estimate = opt_rom.estimate_error(opt_rom.parameters.parse(mu))\n",
    "    if primal_estimate > 1e-2:\n",
    "        print('Enriching the space because primal estimate is {} ...'.format(primal_estimate))\n",
    "        U = fom.solve(mu)\n",
    "        try:\n",
    "            pdeopt_reductor.extend_basis(U)\n",
    "            data['enrichments'] += 1\n",
    "            opt_rom = pdeopt_reductor.reduce()\n",
    "        except:\n",
    "            print('... Extension failed')\n",
    "    else:\n",
    "        print('Do NOT enrich the space because primal estimate is {} ...'.format(primal_estimate))\n",
    "    opt_rom = pdeopt_reductor.reduce()\n",
    "    QoI = opt_rom.output(mu)[0, 0]\n",
    "    return QoI, data, opt_rom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a0e95935",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enriching the space because primal estimate is [2.98580267] ...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f11f24eaa3c84f2698d3ff5becb1eb37",
       "version_major": 2,
       "version_minor": 0
      },
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       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enriching the space because primal estimate is [0.08625045] ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Do NOT enrich the space because primal estimate is [0.00063044] ...\n",
      "Do NOT enrich the space because primal estimate is [0.00122583] ...\n",
      "Do NOT enrich the space because primal estimate is [0.00234427] ...\n",
      "Enriching the space because primal estimate is [0.0135848] ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enriching the space because primal estimate is [0.01046669] ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Do NOT enrich the space because primal estimate is [0.00078205] ...\n",
      "Do NOT enrich the space because primal estimate is [0.00078226] ...\n"
     ]
    }
   ],
   "source": [
    "opt_along_path_adaptively_minimization_data = prepare_data(enrichments=True)\n",
    "\n",
    "opt_dict = {'opt_rom': opt_rom}\n",
    "opt_along_path_adaptively_result = optimize(enrich_adaptively_and_compute_objective_function,\n",
    "                                            opt_along_path_adaptively_minimization_data, ranges,\n",
    "                                            gradient=partial(compute_gradient_with_opt_rom, opt_dict),\n",
    "                                            adaptive_enrichment=True, opt_dict=opt_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c2499c3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " succeeded!\n",
      "  mu_min:    {diffusion: [1.4246559756652408, 3.141592653589793]}\n",
      "  J(mu_min): 2.3917075561126704\n",
      "  absolute error w.r.t. reference solution: 2.79e-07\n",
      "  num iterations:     7\n",
      "  num function calls: 9\n",
      "  time:               0.67047 seconds\n",
      "  model enrichments:  4\n",
      "\n"
     ]
    }
   ],
   "source": [
    "report(opt_along_path_adaptively_result, opt_along_path_adaptively_minimization_data, reference_mu)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6abc7c68",
   "metadata": {},
   "source": [
    "Now, we actually only needed {math}`4` enrichments and ended up with an\n",
    "approximation error of about `1e-07` while getting the highest speed up\n",
    "amongst all methods that we have seen above. Note, however, that this is\n",
    "still dependent on the tolerance `atol=1e-2` that we chose without\n",
    "knowing that this tolerance suffices to reach the actual minimum.\n",
    "An easy way around this would be to do one optimization step with the FOM\n",
    "after converging. If this changes anything, the ROM tolerance `atol`\n",
    "was too large. To conclude, we once again\n",
    "compare all methods that we have discussed in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c655d9f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOM with finite differences\n",
      "\n",
      " succeeded!\n",
      "  mu_min:    {diffusion: [1.4246801734272179, 3.141592653589793]}\n",
      "  J(mu_min): 2.3917078763077035\n",
      "  absolute error w.r.t. reference solution: 2.45e-05\n",
      "  num iterations:     7\n",
      "  num function calls: 27\n",
      "  time:               2.86998 seconds\n",
      "\n",
      "\n",
      "ROM with finite differences\n",
      "\n",
      " succeeded!\n",
      "  mu_min:    {diffusion: [1.424654211984247, 3.141592653589793]}\n",
      "  J(mu_min): 2.3917078350963017\n",
      "  absolute error w.r.t. reference solution: 1.48e-06\n",
      "  num iterations:     7\n",
      "  num function calls: 27\n",
      "  time:               0.01420 seconds\n",
      "  offline time:       1.93975 seconds\n",
      "\n",
      "\n",
      "FOM with gradient\n",
      "\n",
      " succeeded!\n",
      "  mu_min:    {diffusion: [1.4246556963614603, 3.141592653589793]}\n",
      "  J(mu_min): 2.3917078762139408\n",
      "  absolute error w.r.t. reference solution: 0.00e+00\n",
      "  num iterations:     7\n",
      "  num function calls: 9\n",
      "  time:               3.02843 seconds\n",
      "\n",
      "\n",
      "ROM with gradient\n",
      "\n",
      " succeeded!\n",
      "  mu_min:    {diffusion: [1.4246542368580375, 3.141592653589793]}\n",
      "  J(mu_min): 2.391707835095847\n",
      "  absolute error w.r.t. reference solution: 1.46e-06\n",
      "  num iterations:     7\n",
      "  num function calls: 9\n",
      "  time:               0.02459 seconds\n",
      "  offline time:       1.93975 seconds\n",
      "\n",
      "\n",
      "Always enrich along the path\n",
      "\n",
      " succeeded!\n",
      "  mu_min:    {diffusion: [1.4246556963614518, 3.141592653589793]}\n",
      "  J(mu_min): 2.391707876213646\n",
      "  absolute error w.r.t. reference solution: 8.44e-15\n",
      "  num iterations:     7\n",
      "  num function calls: 9\n",
      "  time:               1.75243 seconds\n",
      "  model enrichments:  9\n",
      "\n",
      "\n",
      "Adaptively enrich along the path\n",
      "\n",
      " succeeded!\n",
      "  mu_min:    {diffusion: [1.4246559756652408, 3.141592653589793]}\n",
      "  J(mu_min): 2.3917075561126704\n",
      "  absolute error w.r.t. reference solution: 2.79e-07\n",
      "  num iterations:     7\n",
      "  num function calls: 9\n",
      "  time:               0.67047 seconds\n",
      "  model enrichments:  4\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('FOM with finite differences')\n",
    "report(fom_result, reference_minimization_data, reference_mu)\n",
    "\n",
    "print('\\nROM with finite differences')\n",
    "report(rom_result, RB_minimization_data, reference_mu)\n",
    "\n",
    "print('\\nFOM with gradient')\n",
    "report(opt_fom_result, opt_fom_minimization_data, reference_mu)\n",
    "\n",
    "print('\\nROM with gradient')\n",
    "report(opt_rom_result, opt_rom_minimization_data, reference_mu)\n",
    "\n",
    "print('\\nAlways enrich along the path')\n",
    "report(opt_along_path_result, opt_along_path_minimization_data, reference_mu)\n",
    "\n",
    "print('\\nAdaptively enrich along the path')\n",
    "report(opt_along_path_adaptively_result, opt_along_path_adaptively_minimization_data, reference_mu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2331da13",
   "metadata": {
    "tags": [
     "hide-code",
     "hide-output"
    ]
   },
   "outputs": [],
   "source": [
    "assert fom_result.nit == 7\n",
    "assert opt_along_path_result.nit == 7\n",
    "assert opt_along_path_minimization_data['num_evals'] == 9\n",
    "assert opt_along_path_minimization_data['enrichments'] == 9\n",
    "assert opt_along_path_adaptively_minimization_data['enrichments'] == 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be5f1875",
   "metadata": {},
   "source": [
    "## Conclusion and some general words about MOR methods for optimization\n",
    "\n",
    "In this tutorial we have seen how pyMOR can be used to speedup the optimizer\n",
    "for PDE-constrained optimization problems.\n",
    "We focused on several aspects of RB methods and showed how explicit gradient information\n",
    "helps to reduce the computational cost of the optimizer.\n",
    "We also saw that already standard RB methods may help to reduce the computational time.\n",
    "It is clear that standard RB methods are especially of interest if an\n",
    "optimization problem needs to be solved multiple times.\n",
    "\n",
    "Moreover, we focused on the lack of overall efficiency of standard RB methods.\n",
    "To overcome this, we reduced the (normally) expensive offline time by choosing larger\n",
    "tolerances for the greedy algorithm.\n",
    "We have also seen a way to overcome\n",
    "the traditional offline/online splitting by only enriching the model along\n",
    "the path of optimization or (even better) only enrich\n",
    "the model if the standard error estimator goes above a certain tolerance.\n",
    "\n",
    "In this tutorial we have only covered a few basic approaches to combine model\n",
    "reduction with optimization.\n",
    "For faster and more robust optimization algorithms we refer to the textbooks\n",
    "[CGT00](<https://epubs.siam.org/doi/book/10.1137/1.9780898719857>) and\n",
    "[NW06](<https://link.springer.com/book/10.1007/978-0-387-40065-5>).\n",
    "For recent research on combining trust-region methods with model reduction for\n",
    "PDE-constrained optimization problems we refer to\n",
    "[YM13](<https://epubs.siam.org/doi/abs/10.1137/120869171>),\n",
    "[QGVW17](<https://epubs.siam.org/doi/abs/10.1137/16M1081981>) and\n",
    "[KMSOV20](<https://arxiv.org/abs/2006.09297>) where for the latter a pyMOR\n",
    "implementation is available as supplementary material.\n",
    "\n",
    "Download the code:\n",
    "{download}`tutorial_optimization.md`\n",
    "{nb-download}`tutorial_optimization.ipynb`"
   ]
  }
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...</p><p style=\"line-height:120%\">00:28 |   |   <bold>CoerciveRBReductor</bold>: Building ROM ...</p><p style=\"line-height:120%\">00:28 <bold>weak_greedy</bold>: Maximum error after 0 extensions: 3.32157512055942 (mu = {diffusion: [0.0, 0.0]})</p><p style=\"line-height:120%\">00:28 <bold>weak_greedy</bold>: Extending surrogate for mu = {diffusion: [0.0, 0.0]} ...</p><p style=\"line-height:120%\">00:28 |   <bold>RBSurrogate</bold>: Computing solution snapshot for mu = {diffusion: [0.0, 0.0]} ...</p><p style=\"line-height:120%\">00:28 |   |   <bold>StationaryModel</bold>: Solving StationaryProblem_CG for {diffusion: [0.0, 0.0], input: } ...</p><p style=\"line-height:120%\">00:28 |   <bold>RBSurrogate</bold>: Extending basis with solution snapshot ...</p><p style=\"line-height:120%\">00:28 |   <bold>RBSurrogate</bold>: Reducing ...</p><p style=\"line-height:120%\">00:28 |   |   <bold>CoerciveRBReductor</bold>: Operator projection ...</p><p style=\"line-height:120%\">00:28 |   |   <bold>CoerciveRBReductor</bold>: Assembling error estimator ...</p><p style=\"line-height:120%\">00:28 |   |   |   <bold>ResidualReductor</bold>: Estimating residual range ...</p><p style=\"line-height:120%\">00:28 |   |   |   |   <bold>estimate_image_hierarchical</bold>: Estimating image for basis vector 0 ...</p><p style=\"line-height:120%\">00:28 |   |   |   |   <bold>estimate_image_hierarchical</bold>: Orthonormalizing ...</p><p style=\"line-height:120%\">00:28 |   |   |   |   |   <bold>gram_schmidt</bold>: Removing vector 1 of norm 3.668212814568363e-17</p><p style=\"line-height:120%\">00:28 |   |   |   |   |   <bold>gram_schmidt</bold>: Orthonormalizing vector 2 again</p><p style=\"line-height:120%\">00:28 |   |   |   |   |   <bold>gram_schmidt</bold>: Orthonormalizing vector 3 again</p><p style=\"line-height:120%\">00:28 |   |   |   <bold>ResidualReductor</bold>: Projecting residual operator ...</p><p style=\"line-height:120%\">00:28 |   |   <bold>CoerciveRBReductor</bold>: Building ROM ...</p><p style=\"line-height:120%\">      </p><p style=\"line-height:120%\">00:28 <bold>weak_greedy</bold>: Estimating errors ...</p><p style=\"line-height:120%\">00:28 <bold>weak_greedy</bold>: Maximum error after 1 extensions: 0.4681135780428341 (mu = {diffusion: [0.0, 1.5707963267948966]})</p><p style=\"line-height:120%\">00:28 <bold>weak_greedy</bold>: Extending surrogate for mu = {diffusion: [0.0, 1.5707963267948966]} ...</p><p style=\"line-height:120%\">00:28 |   <bold>RBSurrogate</bold>: Computing solution snapshot for mu = {diffusion: [0.0, 1.5707963267948966]} ...</p><p style=\"line-height:120%\">00:28 |   |   <bold>StationaryModel</bold>: Solving StationaryProblem_CG for {diffusion: [0.0, 1.5707963267948966], input: } ...</p><p style=\"line-height:120%\">00:28 |   <bold>RBSurrogate</bold>: Extending basis with solution snapshot ...</p><p style=\"line-height:120%\">00:28 |   |   <bold>gram_schmidt</bold>: Orthonormalizing vector 1 again</p><p style=\"line-height:120%\">00:28 |   <bold>RBSurrogate</bold>: Reducing ...</p><p style=\"line-height:120%\">00:28 |   |   <bold>CoerciveRBReductor</bold>: Operator projection ...</p><p style=\"line-height:120%\">00:28 |   |   <bold>CoerciveRBReductor</bold>: Assembling error estimator ...</p><p style=\"line-height:120%\">00:28 |   |   |   <bold>ResidualReductor</bold>: Estimating residual range ...</p><p style=\"line-height:120%\">00:28 |   |   |   |   <bold>estimate_image_hierarchical</bold>: Estimating image for basis vector 1 ...</p><p style=\"line-height:120%\">00:28 |   |   |   |   <bold>estimate_image_hierarchical</bold>: Orthonormalizing ...</p><p style=\"line-height:120%\">00:28 |   |   |   |   |   <bold>gram_schmidt</bold>: Removing vector 3 of norm 5.164938757979865e-16</p><p style=\"line-height:120%\">00:28 |   |   |   |   |   <bold>gram_schmidt</bold>: Orthonormalizing vector 4 again</p><p style=\"line-height:120%\">00:28 |   |   |   |   |   <bold>gram_schmidt</bold>: Orthonormalizing vector 5 again</p><p style=\"line-height:120%\">00:28 |   |   |   <bold>ResidualReductor</bold>: Projecting residual operator ...</p><p style=\"line-height:120%\">00:28 |   |   <bold>CoerciveRBReductor</bold>: Building ROM ...</p><p style=\"line-height:120%\">      </p><p style=\"line-height:120%\">00:28 <bold>weak_greedy</bold>: Estimating errors ...</p><p style=\"line-height:120%\">00:29 <bold>weak_greedy</bold>: Maximum error after 2 extensions: 0.09885730513283847 (mu = {diffusion: [1.5707963267948966, 3.141592653589793]})</p><p style=\"line-height:120%\">00:29 <bold>weak_greedy</bold>: Extending surrogate for mu = {diffusion: [1.5707963267948966, 3.141592653589793]} ...</p><p style=\"line-height:120%\">00:29 |   <bold>RBSurrogate</bold>: Computing solution snapshot for mu = {diffusion: [1.5707963267948966, 3.141592653589793]} ...</p><p style=\"line-height:120%\">00:29 |   |   <bold>StationaryModel</bold>: Solving StationaryProblem_CG for {diffusion: [1.5707963267948966, 3.141592653589793], input: } ...</p><p style=\"line-height:120%\">00:29 |   <bold>RBSurrogate</bold>: Extending basis with solution snapshot ...</p><p style=\"line-height:120%\">00:29 |   |   <bold>gram_schmidt</bold>: Orthonormalizing vector 2 again</p><p style=\"line-height:120%\">00:29 |   <bold>RBSurrogate</bold>: Reducing ...</p><p style=\"line-height:120%\">00:29 |   |   <bold>CoerciveRBReductor</bold>: Operator projection ...</p><p style=\"line-height:120%\">00:29 |   |   <bold>CoerciveRBReductor</bold>: Assembling error estimator ...</p><p style=\"line-height:120%\">00:29 |   |   |   <bold>ResidualReductor</bold>: Estimating residual range ...</p><p style=\"line-height:120%\">00:29 |   |   |   |   <bold>estimate_image_hierarchical</bold>: Estimating image for basis vector 2 ...</p><p style=\"line-height:120%\">00:29 |   |   |   |   <bold>estimate_image_hierarchical</bold>: Orthonormalizing ...</p><p style=\"line-height:120%\">00:29 |   |   |   |   |   <bold>gram_schmidt</bold>: Removing vector 5 of norm 3.5460506784170713e-15</p><p style=\"line-height:120%\">00:29 |   |   |   |   |   <bold>gram_schmidt</bold>: Orthonormalizing vector 6 again</p><p style=\"line-height:120%\">00:29 |   |   |   |   |   <bold>gram_schmidt</bold>: Orthonormalizing vector 7 again</p><p style=\"line-height:120%\">00:29 |   |   |   <bold>ResidualReductor</bold>: Projecting residual operator ...</p><p style=\"line-height:120%\">00:29 |   |   <bold>CoerciveRBReductor</bold>: Building ROM ...</p><p style=\"line-height:120%\">      </p><p style=\"line-height:120%\">00:29 <bold>weak_greedy</bold>: Estimating errors ...</p><p style=\"line-height:120%\">00:29 <bold>weak_greedy</bold>: Maximum error after 3 extensions: 0.00935354954250117 (mu = {diffusion: [0.39269908169872414, 3.141592653589793]})</p><p style=\"line-height:120%\">00:29 <bold>weak_greedy</bold>: Absolute error tolerance (0.01) reached! Stopping extension loop.</p><p style=\"line-height:120%\">00:29 <bold>weak_greedy</bold>: Greedy search took 1.9397502349997922 seconds</p>"
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style=\"line-height:120%\">00:23 <bold>StationaryModel</bold>: Solving StationaryProblem_CG for {diffusion: [2.111061769059862, 0.05], input: } ...</p><p style=\"line-height:120%\">00:23 <bold>StationaryModel</bold>: Solving StationaryProblem_CG for {diffusion: [2.111061769059862, 0.39351029484331035], input: } ...</p><p style=\"line-height:120%\">00:23 <bold>StationaryModel</bold>: Solving StationaryProblem_CG for {diffusion: [2.111061769059862, 0.7370205896866208], input: } ...</p><p style=\"line-height:120%\">00:23 <bold>StationaryModel</bold>: Solving StationaryProblem_CG for {diffusion: [2.111061769059862, 1.080530884529931], input: } ...</p><p style=\"line-height:120%\">00:23 <bold>StationaryModel</bold>: Solving StationaryProblem_CG for {diffusion: [2.111061769059862, 1.4240411793732415], input: } ...</p><p style=\"line-height:120%\">00:23 <bold>StationaryModel</bold>: Solving StationaryProblem_CG for {diffusion: [2.111061769059862, 1.767551474216552], input: } ...</p><p style=\"line-height:120%\">00:24 <bold>StationaryModel</bold>: Solving StationaryProblem_CG for {diffusion: [2.111061769059862, 2.111061769059862], input: } ...</p><p style=\"line-height:120%\">00:24 <bold>StationaryModel</bold>: Solving StationaryProblem_CG for {diffusion: [2.111061769059862, 2.4545720639031723], input: } ...</p><p style=\"line-height:120%\">00:24 <bold>StationaryModel</bold>: Solving StationaryProblem_CG for {diffusion: [2.111061769059862, 2.7980823587464827], input: } ...</p><p style=\"line-height:120%\">00:24 <bold>StationaryModel</bold>: Solving StationaryProblem_CG for {diffusion: [2.111061769059862, 3.141592653589793], input: } ...</p><p style=\"line-height:120%\">00:24 <bold>StationaryModel</bold>: Solving StationaryProblem_CG for {diffusion: [2.4545720639031723, 0.05], input: } ...</p><p style=\"line-height:120%\">00:24 <bold>StationaryModel</bold>: Solving StationaryProblem_CG for {diffusion: [2.4545720639031723, 0.39351029484331035], input: } ...</p><p 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input: } ...</p><p style=\"line-height:120%\">00:55 <bold>gram_schmidt</bold>: Orthonormalizing vector 2 again</p><p style=\"line-height:120%\">00:55 <bold>CoerciveRBReductor</bold>: Operator projection ...</p><p style=\"line-height:120%\">00:55 <bold>CoerciveRBReductor</bold>: Assembling error estimator ...</p><p style=\"line-height:120%\">00:55 |   <bold>ResidualReductor</bold>: Estimating residual range ...</p><p style=\"line-height:120%\">00:55 |   |   <bold>estimate_image_hierarchical</bold>: Estimating image for basis vector 2 ...</p><p style=\"line-height:120%\">00:55 |   |   <bold>estimate_image_hierarchical</bold>: Orthonormalizing ...</p><p style=\"line-height:120%\">00:55 |   |   |   <bold>gram_schmidt</bold>: Orthonormalizing vector 6 again</p><p style=\"line-height:120%\">00:55 |   |   |   <bold>gram_schmidt</bold>: Orthonormalizing vector 7 again</p><p style=\"line-height:120%\">00:55 |   <bold>ResidualReductor</bold>: Projecting residual operator ...</p><p style=\"line-height:120%\">00:55 <bold>CoerciveRBReductor</bold>: Building ROM ...</p><p style=\"line-height:120%\">00:55 <bold>StationaryModel</bold>: Solving StationaryProblem_CG for {diffusion: [1.6969762100610462, 1.0189907679443415], input: } ...</p><p style=\"line-height:120%\">00:55 <bold>gram_schmidt</bold>: Orthonormalizing vector 3 again</p><p style=\"line-height:120%\">00:55 <bold>CoerciveRBReductor</bold>: Operator projection ...</p><p style=\"line-height:120%\">00:55 <bold>CoerciveRBReductor</bold>: Assembling error estimator ...</p><p style=\"line-height:120%\">00:55 |   <bold>ResidualReductor</bold>: Estimating residual range ...</p><p style=\"line-height:120%\">00:55 |   |   <bold>estimate_image_hierarchical</bold>: Estimating image for basis vector 3 ...</p><p style=\"line-height:120%\">00:55 |   |   <bold>estimate_image_hierarchical</bold>: Orthonormalizing ...</p><p style=\"line-height:120%\">00:55 |   |   |   <bold>gram_schmidt</bold>: Orthonormalizing vector 8 again</p><p style=\"line-height:120%\">00:55 |   |   |   <bold>gram_schmidt</bold>: Orthonormalizing vector 9 again</p><p style=\"line-height:120%\">00:55 |   |   |   <bold>gram_schmidt</bold>: Orthonormalizing vector 10 again</p><p style=\"line-height:120%\">00:55 |   <bold>ResidualReductor</bold>: Projecting residual operator ...</p><p style=\"line-height:120%\">00:55 <bold>CoerciveRBReductor</bold>: Building ROM ...</p><p style=\"line-height:120%\">00:55 <bold>StationaryModel</bold>: Solving StationaryProblem_CG for {diffusion: [1.6074375618405647, 1.2777807745126548], input: } ...</p><p style=\"line-height:120%\">00:56 <bold>gram_schmidt</bold>: Orthonormalizing vector 4 again</p><p style=\"line-height:120%\">00:56 <bold>CoerciveRBReductor</bold>: Operator projection ...</p><p style=\"line-height:120%\">00:56 <bold>CoerciveRBReductor</bold>: Assembling error estimator ...</p><p style=\"line-height:120%\">00:56 |   <bold>ResidualReductor</bold>: Estimating residual range ...</p><p style=\"line-height:120%\">00:56 |   |   <bold>estimate_image_hierarchical</bold>: Estimating image for basis vector 4 ...</p><p style=\"line-height:120%\">00:56 |   |   <bold>estimate_image_hierarchical</bold>: Orthonormalizing ...</p><p style=\"line-height:120%\">00:56 |   |   |   <bold>gram_schmidt</bold>: Orthonormalizing vector 11 again</p><p style=\"line-height:120%\">00:56 |   |   |   <bold>gram_schmidt</bold>: Orthonormalizing vector 12 again</p><p style=\"line-height:120%\">00:56 |   |   |   <bold>gram_schmidt</bold>: Orthonormalizing vector 13 again</p><p style=\"line-height:120%\">00:56 |   <bold>ResidualReductor</bold>: Projecting residual operator ...</p><p style=\"line-height:120%\">00:56 <bold>CoerciveRBReductor</bold>: Building ROM ...</p><p style=\"line-height:120%\">00:56 <bold>StationaryModel</bold>: Solving StationaryProblem_CG for {diffusion: [1.4296485987327028, 2.172963423367752], input: } ...</p><p style=\"line-height:120%\">00:56 <bold>gram_schmidt</bold>: Orthonormalizing vector 5 again</p><p style=\"line-height:120%\">00:56 <bold>CoerciveRBReductor</bold>: Operator projection ...</p><p style=\"line-height:120%\">00:56 <bold>CoerciveRBReductor</bold>: Assembling error estimator ...</p><p style=\"line-height:120%\">00:56 |   <bold>ResidualReductor</bold>: Estimating residual range ...</p><p style=\"line-height:120%\">00:56 |   |   <bold>estimate_image_hierarchical</bold>: Estimating image for basis vector 5 ...</p><p style=\"line-height:120%\">00:56 |   |   <bold>estimate_image_hierarchical</bold>: Orthonormalizing ...</p><p style=\"line-height:120%\">00:56 |   |   |   <bold>gram_schmidt</bold>: Orthonormalizing vector 14 again</p><p style=\"line-height:120%\">00:56 |   |   |   <bold>gram_schmidt</bold>: Orthonormalizing vector 15 again</p><p style=\"line-height:120%\">00:56 |   |   |   <bold>gram_schmidt</bold>: Orthonormalizing vector 16 again</p><p style=\"line-height:120%\">00:56 |   <bold>ResidualReductor</bold>: Projecting residual operator ...</p><p style=\"line-height:120%\">00:56 <bold>CoerciveRBReductor</bold>: Building ROM ...</p><p style=\"line-height:120%\">00:56 <bold>StationaryModel</bold>: Solving StationaryProblem_CG for {diffusion: [1.4525257963228557, 2.9864116690625213], input: } ...</p><p style=\"line-height:120%\">00:56 <bold>gram_schmidt</bold>: Orthonormalizing vector 6 again</p><p style=\"line-height:120%\">00:56 <bold>CoerciveRBReductor</bold>: Operator projection ...</p><p style=\"line-height:120%\">00:56 <bold>CoerciveRBReductor</bold>: Assembling error estimator ...</p><p style=\"line-height:120%\">00:56 |   <bold>ResidualReductor</bold>: Estimating residual range ...</p><p style=\"line-height:120%\">00:56 |   |   <bold>estimate_image_hierarchical</bold>: Estimating image for basis vector 6 ...</p><p style=\"line-height:120%\">00:56 |   |   <bold>estimate_image_hierarchical</bold>: Orthonormalizing ...</p><p style=\"line-height:120%\">00:56 |   |   |   <bold>gram_schmidt</bold>: Orthonormalizing vector 17 again</p><p style=\"line-height:120%\">00:56 |   |   |   <bold>gram_schmidt</bold>: Orthonormalizing vector 18 again</p><p style=\"line-height:120%\">00:56 |   |   |   <bold>gram_schmidt</bold>: Orthonormalizing vector 19 again</p><p style=\"line-height:120%\">00:56 |   <bold>ResidualReductor</bold>: Projecting residual operator ...</p><p style=\"line-height:120%\">00:56 <bold>CoerciveRBReductor</bold>: Building ROM ...</p><p style=\"line-height:120%\">00:56 <bold>StationaryModel</bold>: Solving StationaryProblem_CG for {diffusion: [1.4243581519932142, 3.141592653589793], input: } ...</p><p style=\"line-height:120%\">00:56 <bold>gram_schmidt</bold>: Orthonormalizing vector 7 again</p><p style=\"line-height:120%\">00:56 <bold>CoerciveRBReductor</bold>: Operator projection ...</p><p style=\"line-height:120%\">00:56 <bold>CoerciveRBReductor</bold>: Assembling error estimator ...</p><p style=\"line-height:120%\">00:56 |   <bold>ResidualReductor</bold>: Estimating residual range ...</p><p style=\"line-height:120%\">00:56 |   |   <bold>estimate_image_hierarchical</bold>: Estimating image for basis vector 7 ...</p><p style=\"line-height:120%\">00:56 |   |   <bold>estimate_image_hierarchical</bold>: Orthonormalizing ...</p><p style=\"line-height:120%\">00:56 |   |   |   <bold>gram_schmidt</bold>: Orthonormalizing vector 20 again</p><p style=\"line-height:120%\">00:56 |   |   |   <bold>gram_schmidt</bold>: Orthonormalizing vector 21 again</p><p style=\"line-height:120%\">00:56 |   |   |   <bold>gram_schmidt</bold>: Orthonormalizing vector 22 again</p><p style=\"line-height:120%\">00:56 |   <bold>ResidualReductor</bold>: Projecting residual operator ...</p><p style=\"line-height:120%\">00:56 <bold>CoerciveRBReductor</bold>: Building ROM ...</p><p style=\"line-height:120%\">00:56 <bold>StationaryModel</bold>: Solving StationaryProblem_CG for {diffusion: [1.4246556963614518, 3.141592653589793], input: } ...</p><p style=\"line-height:120%\">00:56 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