{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "remove-input",
     "active-ipynb",
     "remove-output"
    ]
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    from openmdao.utils.notebook_utils import notebook_mode  # noqa: F401\n",
    "except ImportError:\n",
    "    !python -m pip install openmdao[notebooks]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run a Driver\n",
    "\n",
    "Once `setup()` is done, you can then run the optimization with `run_driver()`.\n",
    "\n",
    "`run_driver()` executes the driver, running the optimization, DOE, etc. that you've set up.\n",
    "\n",
    "## Examples\n",
    "\n",
    "Set up a simple optimization problem and run it, by calling `run_driver`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "remove-input",
     "remove-output"
    ]
   },
   "outputs": [],
   "source": [
    "from openmdao.utils.notebook_utils import get_code\n",
    "from myst_nb import glue\n",
    "glue(\"code_src50\", get_code(\"openmdao.test_suite.components.sellar_feature.SellarDerivatives\"), display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{Admonition} `SellarDerivatives` class definition \n",
    ":class: dropdown\n",
    "\n",
    "{glue:}`code_src50`\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import openmdao.api as om\n",
    "from openmdao.test_suite.components.sellar_feature import SellarDerivatives\n",
    "\n",
    "prob = om.Problem(model=SellarDerivatives())\n",
    "\n",
    "model = prob.model\n",
    "model.nonlinear_solver = om.NonlinearBlockGS()\n",
    "model.linear_solver = om.ScipyKrylov()\n",
    "\n",
    "prob.driver = om.ScipyOptimizeDriver()\n",
    "prob.driver.options['optimizer'] = 'SLSQP'\n",
    "prob.driver.options['tol'] = 1e-9\n",
    "\n",
    "model.add_design_var('z', lower=np.array([-10.0, 0.0]), upper=np.array([10.0, 10.0]))\n",
    "model.add_design_var('x', lower=0.0, upper=10.0)\n",
    "model.add_objective('obj')\n",
    "model.add_constraint('con1', upper=0.0)\n",
    "model.add_constraint('con2', upper=0.0)\n",
    "\n",
    "prob.setup()\n",
    "prob.run_driver()\n",
    "\n",
    "print(prob.get_val('x'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(prob.get_val('y1'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(prob.get_val('y2'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(prob.get_val('z'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(prob.get_val('obj'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "remove-input",
     "remove-output"
    ]
   },
   "outputs": [],
   "source": [
    "from openmdao.utils.assert_utils import assert_near_equal\n",
    "\n",
    "assert_near_equal(prob.get_val('x'), 0.0, 1e-5)\n",
    "assert_near_equal(prob.get_val('y1'), 3.160000, 1e-2)\n",
    "assert_near_equal(prob.get_val('y2'), 3.755278, 1e-2)\n",
    "assert_near_equal(prob.get_val('z'), [1.977639, 0.000000], 1e-2)\n",
    "assert_near_equal(prob.get_val('obj'), 3.18339395, 1e-2)"
   ]
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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