{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic User Guide\n",
    "\n",
    "The SLSQP algorithm is designed to minimize a scalar objective function \n",
    "of one or more variables, subject to constraints.\n",
    "The `optimize()` function is a wrapper to the original SLSQP \n",
    "implementation by Dieter Kraft {cite:p}`kraft1988software, kraft1994algorithm`. \n",
    "The PySLSQP wrapper provides a slightly modified interface to the\n",
    "optimization problem and many additional features, compared to the Scipy wrapper.\n",
    "\n",
    "The `optimize` function solves the general nonlinear programming problem: \n",
    "\n",
    "\\begin{align*}\n",
    "\\underset{x \\in \\mathbb{R}^n}{\\text{minimize}} \\quad & f(x) \\\\\n",
    "\\text{subject to} \\quad & c_i(x) = 0, \\quad & i = 1,...,meq \\\\\n",
    "& c_i(x) \\geq 0,  \\quad & i = meq+1,...,m \\\\\n",
    "& xl_i \\leq x_i \\leq  xu_i, \\quad & i = 1,...,n\n",
    "\\end{align*}\n",
    "\n",
    "\n",
    "where $x$ is the vector of optimization variables with size $n$, $f(x)$ is the scalar objective function,\n",
    "$c(x)$ is the vector-valued constraint function, and $xl$ and $xu$ are vectors of lower and\n",
    "upper bounds, respectively. The first $meq$ constraints are equalities\n",
    "while the remaining $ (m - meq) $ constraints are inequalities.\n",
    "\n",
    "## Defining your optimization problem\n",
    "\n",
    "We previously looked at a very simple example in the [Getting Started](./getting_started.md) section.\n",
    "Now, let's look at a more complex problem that has both equality and inequality constraints, with variable bounds.\n",
    "Here, we aim to minimize `x^2 + y^2` \n",
    "subject to the constraints `x+y=1` and `3x+2y>=1`, with the bounds `x>=0.4` and `y<=0.6`.\n",
    "\n",
    "Now we write this example in the standard problem format shown above for SLSQP:\n",
    "\\begin{align*}\n",
    "\\underset{x \\in \\mathbb{R}^2}{\\text{minimize}} \\quad & x_1^2 + x_2^2\\\\\n",
    "\\text{subject to} \\quad & x_1 + x_2 - 1 = 0, \\\\\n",
    "& 3x_1 + 2x_2 - 1 \\geq 0,  \\\\\n",
    "\\text{with} \\quad & meq = 1, xl = [0.4, +\\infty]^T, \\; \\text{and} \\; xu = [-\\infty, 0.6]^T.\n",
    "\\end{align*}\n",
    "\n",
    "The code below defines the computation for the objective, constraint and their derivatives.\n",
    "It also defines the initial guess $x0$ and other problem variables such as $meq$, $xl$, and $xu$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# \"x\" represents the vector of optimization variables\n",
    "def objective(x):\n",
    "    # the objective function\n",
    "    return x[0]**2 + x[1]**2\n",
    "\n",
    "def gradient(x):\n",
    "    # the gradient of the objective function\n",
    "    return np.array([2*x[0], 2*x[1]])\n",
    "\n",
    "def constraints(x):\n",
    "    # the constraint functions formulated as c_eq(x) = 0, c_ineq(x) >= 0\n",
    "    return  np.array([x[0] + x[1] - 1, 3*x[0] + 2*x[1] - 1])\n",
    "\n",
    "def jacobian(x):\n",
    "    # the jacobian of the constraint functions\n",
    "    return np.array([[1, 1], [3, 2]])\n",
    "\n",
    "# lower bounds on the optimization variables\n",
    "x_lower = np.array([0.4, -np.inf])\n",
    "# upper bounds on the optimization variables\n",
    "x_upper = np.array([np.inf, 0.6])\n",
    "# number of equality constraints (at the beginning of the constraint vector)\n",
    "num_eqcon = 1\n",
    "\n",
    "x0 = np.array([2,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solving the optimization problem\n",
    "We now import the `optimize()` function that actually runs the SLSQP algorithm and call it with the problem variables\n",
    "and functions defined above.\n",
    "If the derivatives `grad` and/or `jac` is not provided, `optimize()` will estimate them using finite differencing.\n",
    "See [Other optimizer options](#other-optimizer-options) for specifying custom steps for finite difference approximation.\n",
    "\n",
    "Once `optimize` is executed, the summary of the final optimization results will be printed on the console by default.\n",
    "The function returns a dictionary that holds the results of the optimization.\n",
    "A summary of the major iterations is also written to a file by default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully    (Exit mode 0)\n",
      "            Final objective value                : 5.000000e-01\n",
      "            Final optimality                     : 1.232595e-31\n",
      "            Final feasibility                    : 0.000000e+00\n",
      "            Number of major iterations           : 4\n",
      "            Number of function evaluations       : 4\n",
      "            Number of derivative evaluations     : 4\n",
      "            Average Derivative evaluation time   : 0.000051 s per evaluation\n",
      "            Average Function evaluation time     : 0.000036 s per evaluation\n",
      "            Total Function evaluation time       : 0.000206 s [  5.43%]\n",
      "            Total Derivative evaluation time     : 0.000142 s [  3.75%]\n",
      "            Optimizer time                       : 0.000145 s [  3.82%]\n",
      "            Processing time                      : 0.003300 s [ 86.99%]\n",
      "            Visualization time                   : 0.000000 s [  0.00%]\n",
      "            Total optimization time              : 0.003793 s [100.00%]\n",
      "            Summary saved to                     : slsqp_summary.out\n"
     ]
    }
   ],
   "source": [
    "from pyslsqp import optimize\n",
    "\n",
    "# optimize returns a dictionary that contains the results from optimization\n",
    "results = optimize(x0, obj=objective, grad=gradient, con=constraints, jac=jacobian, meq=num_eqcon, xl=x_lower, xu=x_upper)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Viewing the results\n",
    "We can print and see the returned results dictionary as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "        x                        : [0.5 0.5]\n",
      "        objective                : 0.5\n",
      "        optimality               : 1.232595164407831e-31\n",
      "        feasibility              : 0.0\n",
      "        constraints              : [0.  1.5]\n",
      "        multipliers              : [1. 0.]\n",
      "        gradient                 : [1. 1.]\n",
      "        jacobian                 : [[1. 1.]\n",
      " [3. 2.]]\n",
      "        num_majiter              : 4\n",
      "        nfev                     : 4\n",
      "        ngev                     : 4\n",
      "        fev_time                 : 0.00020599365234375\n",
      "        gev_time                 : 0.0001423358917236328\n",
      "        optimizer_time           : 0.00014495849609375\n",
      "        processing_time          : 0.003299713134765625\n",
      "        visualization_time       : 0.0\n",
      "        total_time               : 0.003793001174926758\n",
      "        status                   : 0\n",
      "        message                  : Optimization terminated successfully\n",
      "        success                  : True\n",
      "        summary_filename         : slsqp_summary.out\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# print the returned \"results\" dictionary as a table\n",
    "from pyslsqp.postprocessing import print_dict_as_table\n",
    "print_dict_as_table(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen from above, the summary of the optimization will be saved to `slsqp_summary.out`, unless the user provided\n",
    "the `summary_filename` keyword argument for the `optimize()` function.\n",
    "The contents of the summary file for the above optimization is shown below.\n",
    "\n",
    "    MAJOR  NFEV  NGEV           OBJFUN            GNORM            CNORM             FEAS              OPT             STEP \n",
    "        0     1     1     4.360000E+00     4.176123E+00     6.403124E+00     9.900000E+01     9.900000E+01     9.900000E+01 \n",
    "        1     2     1     5.200000E-01     4.176123E+00     1.400000E+00     1.600000E+00     3.840000E+00     1.000000E+00 \n",
    "        2     3     2     5.022222E-01     1.442221E+00     1.533333E+00     4.440892E-16     7.697546E-16     1.000000E+00 \n",
    "        3     4     3     5.000000E-01     1.417353E+00     1.500000E+00     1.110223E-16     1.517305E-16     1.000000E+00 \n",
    "        4     4     4     5.000000E-01     1.414214E+00     1.500000E+00     0.000000E+00     1.232595E-31     1.000000E+00 \n",
    "\n",
    "The following table describes the different columns in the summary file.\n",
    "\n",
    "```{list-table} Summary file description\n",
    ":header-rows: 1\n",
    ":name: summaryfile\n",
    "\n",
    "* - Column #\n",
    "  - Header\n",
    "  - Description\n",
    "* - 1\n",
    "  - MAJOR\n",
    "  - Major iteration number\n",
    "* - 2\n",
    "  - NFEV\n",
    "  - Number of function (obj, con) evaluations\n",
    "* - 3\n",
    "  - NGEV\n",
    "  - Number of derivative (grad, jac) evaluations\n",
    "* - 4\n",
    "  - OBJFUN\n",
    "  - Objective function value\n",
    "* - 5\n",
    "  - GNORM\n",
    "  - Norm of the objective gradient\n",
    "* - 6\n",
    "  - CNORM\n",
    "  - Norm of the constraint vector\n",
    "* - 7\n",
    "  - FEAS\n",
    "  - Feasibility measure\n",
    "* - 8\n",
    "  - OPT\n",
    "  - Optimality measure\n",
    "* - 9\n",
    "  - STEP\n",
    "  - Step length taken after line search\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling a problem\n",
    "We can also scale the problem defined above without changing any of the previous definitions.\n",
    "The `optimize` function can independently scale the optimization variables, objective and constraint functions using \n",
    "user-provided arguments for `x_scaler`, `obj_scaler`, and `con_scaler` as shown below.\n",
    "Here, we scale the optimization variables $x_1$ and $x_2$ by a factor of $10$ and $0.1$ respectively.\n",
    "The objective function $f$ is scaled by a factor of $0.01$ whereas the constraint functions $c_i$ are all\n",
    "scaled by $2000$.\n",
    "For $x$ and $c$, we can scale all the entries differently by inputting a *vector-valued* scaler,\n",
    "or scale them by the same value by inputting a *scalar-valued* scaler.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully    (Exit mode 0)\n",
      "            Final objective value                : 5.000000e-01\n",
      "            Final optimality                     : 7.682743e-16\n",
      "            Final feasibility                    : 3.841372e-14\n",
      "            Number of major iterations           : 9\n",
      "            Number of function evaluations       : 10\n",
      "            Number of derivative evaluations     : 9\n",
      "            Average Derivative evaluation time   : 0.000066 s per evaluation\n",
      "            Average Function evaluation time     : 0.000071 s per evaluation\n",
      "            Total Function evaluation time       : 0.000662 s [  7.51%]\n",
      "            Total Derivative evaluation time     : 0.000643 s [  7.29%]\n",
      "            Optimizer time                       : 0.001009 s [ 11.45%]\n",
      "            Processing time                      : 0.006499 s [ 73.75%]\n",
      "            Visualization time                   : 0.000000 s [  0.00%]\n",
      "            Total optimization time              : 0.008812 s [100.00%]\n",
      "            Summary saved to                     : scaled_summary.out\n",
      "--------------------------------------------------\n",
      "        x                        : [0.5 0.5]\n",
      "        objective                : 0.49999999999996164\n",
      "        optimality               : 7.682743330406234e-16\n",
      "        feasibility              : 3.8413716652030416e-14\n",
      "        constraints              : [-3.84137167e-14  1.50000000e+00]\n",
      "        multipliers              : [5.e-06 0.e+00]\n",
      "        gradient                 : [1. 1.]\n",
      "        jacobian                 : [[1. 1.]\n",
      " [3. 2.]]\n",
      "        num_majiter              : 9\n",
      "        nfev                     : 10\n",
      "        ngev                     : 9\n",
      "        fev_time                 : 0.0006620883941650391\n",
      "        gev_time                 : 0.0006425380706787109\n",
      "        optimizer_time           : 0.001008749008178711\n",
      "        processing_time          : 0.006498813629150391\n",
      "        visualization_time       : 0.0\n",
      "        total_time               : 0.008812189102172852\n",
      "        status                   : 0\n",
      "        message                  : Optimization terminated successfully\n",
      "        success                  : True\n",
      "        summary_filename         : scaled_summary.out\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "x_sc = np.array([10., 0.1])\n",
    "f_sc = 0.01\n",
    "c_sc = 2000\n",
    "results = optimize(x0, obj=objective, grad=gradient, con=constraints, jac=jacobian, \n",
    "                   meq=num_eqcon, xl=x_lower, xu=x_upper,\n",
    "                   x_scaler=x_sc, obj_scaler=f_sc, con_scaler=c_sc,\n",
    "                   summary_filename=\"scaled_summary.out\")\n",
    "\n",
    "print_dict_as_table(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that it took more iterations than before for the algorithm to converge and the results are not\n",
    "exactly the same as in the case without scaling.\n",
    "```{Note}\n",
    "The optimality, feasibility, and multipliers shown are for the scaled problem,\n",
    "whereas the optimization variables, objective, constraints, and gradients are for the unscaled problem.\n",
    "```\n",
    "We also set `summary_filename=scaled_summary.out` when calling `optimize()` this time and \n",
    "the optimization summary is now saved in a different file which is shown below.\n",
    "\n",
    "    MAJOR  NFEV  NGEV           OBJFUN            GNORM            CNORM             FEAS              OPT             STEP \n",
    "        0     1     1     4.360000E+00     4.176123E+00     6.403124E+00     9.900000E+01     9.900000E+01     9.900000E+01 \n",
    "        1     2     1     4.997361E+00     4.176123E+00     2.999560E+00     3.200000E+03     6.392961E-03     1.000000E+00 \n",
    "        2     4     2     4.993763E+00     4.019589E+00     2.998960E+00     1.598790E+03     1.595809E-02     1.000000E+00 \n",
    "        3     5     3     4.990164E+00     4.469346E+00     2.998360E+00     2.220446E-13     2.213144E-18     1.000000E+00 \n",
    "        4     6     4     4.972198E+00     4.467735E+00     2.995359E+00     6.661338E-13     6.624715E-18     1.000000E+00 \n",
    "        5     7     5     4.883084E+00     4.459685E+00     2.980386E+00     2.220446E-13     2.189196E-18     1.000000E+00 \n",
    "        6     8     6     4.455183E+00     4.419540E+00     2.906269E+00     1.332268E-12     1.258534E-17     1.000000E+00 \n",
    "        7     9     7     2.722830E+00     4.221461E+00     2.554237E+00     5.329071E-12     3.988090E-17     1.000000E+00 \n",
    "        8    10     8     5.000000E-01     3.300200E+00     1.500000E+00     2.575717E-11     1.607718E-16     1.000000E+00 \n",
    "        9    10     9     5.000000E-01     1.414214E+00     1.500000E+00     7.682743E-11     7.682743E-16     1.000000E+00 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Live Visualization\n",
    "\n",
    "To visualize the progression of values of scalar variables in real time during the optimization, set `visualize=True`.\n",
    "By default, the list of variables to visualize is set as `visualize_vars=['objective', 'optimality', 'feasibility']`.\n",
    "The complete list of variables that are available for visualization is \n",
    "`['x[i]', 'objective', 'optimality', 'feasibility', 'constraints[i]', 'gradient[i]', 'multipliers[i]', 'jacobian[i,j]']`, \n",
    "where *i* and *j* denote the indices of the the respective variable array and is necessary since only\n",
    "scalar variables can be visualized.\n",
    "To keep the plot window open after optimization, set `keep_plot_open=True` when calling `optimize()`.\n",
    "The name of the file where the final optimization plot is saved can be set using the keyword argument\n",
    "`save_figname` which is set by default as `save_figname=\"slsqp_plot.pdf\"`.\n",
    "\n",
    "The following example plots the objective $f(x)$, optimality, feasibility, the optimization variable $x_1$,\n",
    "the objective gradient $\\frac{\\partial f}{\\partial x_1}(x)$, the constraint $c_1(x)$, \n",
    "the Lagrange multiplier corresponding to the constraint $c_1(x)$, and the constraint derivative $\\frac{\\partial c_1}{\\partial x_1}(x)$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x2400 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully    (Exit mode 0)\n",
      "            Final objective value                : 5.000000e-01\n",
      "            Final optimality                     : 1.232595e-31\n",
      "            Final feasibility                    : 0.000000e+00\n",
      "            Number of major iterations           : 4\n",
      "            Number of function evaluations       : 4\n",
      "            Number of derivative evaluations     : 4\n",
      "            Average Derivative evaluation time   : 0.000033 s per evaluation\n",
      "            Average Function evaluation time     : 0.000036 s per evaluation\n",
      "            Total Function evaluation time       : 0.000132 s [  0.00%]\n",
      "            Total Derivative evaluation time     : 0.000146 s [  0.01%]\n",
      "            Optimizer time                       : 0.000093 s [  0.00%]\n",
      "            Processing time                      : 0.002785 s [  0.10%]\n",
      "            Visualization time                   : 2.808829 s [ 99.89%]\n",
      "            Total optimization time              : 2.811984 s [100.00%]\n",
      "            Summary saved to                     : visualized_summary.out\n",
      "            Plot saved to                        : slsqp_plot.pdf\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "results = optimize(x0, obj=objective, grad=gradient, con=constraints, jac=jacobian, \n",
    "                   meq=num_eqcon, xl=x_lower, xu=x_upper,\n",
    "                   visualize=True, visualize_vars=['objective', 'optimality', 'feasibility', 'x[0]', 'gradient[0]', 'constraints[0]', 'multipliers[0]', 'jacobian[0,0]'],\n",
    "                   summary_filename=\"visualized_summary.out\", keep_plot_open=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unlike in the console outputs from previous examples, see that \"Visualization time\" is nonzero now.\n",
    "It is worth noting that visualization takes up most of time in smaller problems. \n",
    "For example, in the problem above, $99.8 \\%$ of the total optimization time is spent on visualization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing optimization data to a file\n",
    "To save the data and variables generated during the optimization iterations, set `save_itr=all` or `save_itr=major` when calling `optimize()`.\n",
    "This will save the variables in the list defined by `save_vars` after every iteration if `save_itr=all`, or after each major iteration if `save_itr=major`.\n",
    "By default, `save_vars=['x', 'objective', 'optimality', 'feasibility', 'step', 'iter', 'majiter', 'ismajor', 'mode']`. \n",
    "However, the user can specify any subset of variables from the list \n",
    "`['x', 'objective', 'optimality', 'feasibility', 'step', 'mode', 'iter', 'majiter', 'ismajor', 'constraints', 'gradient', 'multipliers', 'jacobian']`.\n",
    "The user can also specify the name of file where the data is saved using the `save_filename` keyword argument.\n",
    "The default name for the save file is `slsqp_recorder.hdf5`, and the data is always stored in the *hdf5* file format.\n",
    "\n",
    "We now use the same example as before to save the optimization variables with their corresponding major iteration using `save_itr='major'` and `save_var=['majiter', 'x']`.\n",
    "We will save the data to a file named `save_file.hdf5`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully    (Exit mode 0)\n",
      "            Final objective value                : 5.000000e-01\n",
      "            Final optimality                     : 1.232595e-31\n",
      "            Final feasibility                    : 0.000000e+00\n",
      "            Number of major iterations           : 4\n",
      "            Number of function evaluations       : 4\n",
      "            Number of derivative evaluations     : 4\n",
      "            Average Derivative evaluation time   : 0.000048 s per evaluation\n",
      "            Average Function evaluation time     : 0.000074 s per evaluation\n",
      "            Total Function evaluation time       : 0.000193 s [  1.19%]\n",
      "            Total Derivative evaluation time     : 0.000296 s [  1.83%]\n",
      "            Optimizer time                       : 0.000125 s [  0.77%]\n",
      "            Processing time                      : 0.015592 s [ 96.21%]\n",
      "            Visualization time                   : 0.000000 s [  0.00%]\n",
      "            Total optimization time              : 0.016206 s [100.00%]\n",
      "            Summary saved to                     : slsqp_summary.out\n",
      "            Iteration data saved to              : save_file.hdf5\n"
     ]
    }
   ],
   "source": [
    "results = optimize(x0, obj=objective, grad=gradient, con=constraints, jac=jacobian, \n",
    "                   meq=num_eqcon, xl=x_lower, xu=x_upper,\n",
    "                   save_itr='major', save_vars=['majiter', 'x'],\n",
    "                   save_filename=\"save_file.hdf5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The console output above now tells us that the iteration data is saved to \"*save_file.hdf5*\".\n",
    "For more details on accessing the saved data or using the saved data for visualization or hot/warm start, see [Post-processing](./postprocessing.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other optimizer options\n",
    "The following table describes some of the other options that could be set while calling `optimize`.\n",
    "\n",
    "```{list-table} Other options\n",
    ":header-rows: 1\n",
    ":name: otheroptions\n",
    "\n",
    "* - Option\n",
    "  - Type (default value)\n",
    "  - Description\n",
    "* - `maxiter`\n",
    "  - *int* (100)\n",
    "  - Maximum number of iterations.\n",
    "* - `acc`\n",
    "  - *float* (1e-6)\n",
    "  - *|acc|* is the stopping criterion and controls the final accuracy. \\\n",
    "    If *acc < 0*, a maximization problem is solved. \\\n",
    "    Otherwise, a minimization problem is solved.\n",
    "* - `iprint`\n",
    "  - *int* (1)\n",
    "  - Controls the verbosity of the SLSQP algorithm. \\\n",
    "    Set *iprint <= 0* to suppress all console outputs. \\\n",
    "    Set *iprint  = 1* to print only the final result summary upon completion. \\\n",
    "    Set *iprint >= 2* to print the status of each major iteration and the final result summary.\n",
    "* - `keep_plot_open`\n",
    "  - *bool* (False)\n",
    "  - If True, the plot window will remain open after optimization.\n",
    "* - `save_figname`\n",
    "  - *str* (\"slsqp_plot.pdf\")\n",
    "  - The name of the file to which the final plot will be saved.\n",
    "* - `finite_diff_abs_step`\n",
    "  - *np.ndarray* or *float* (None)\n",
    "  - The absolute step size to use for numerical approximation of the derivatives. \\\n",
    "    If None (default), then step is selected using `finite_diff_rel_step`.\n",
    "* - `finite_diff_rel_step`\n",
    "  - *np.ndarray* or *float* \\\n",
    "     (`np.sqrt(np.finfo(float).eps)`)\n",
    "  - The relative step size to use for numerical approximation of the derivatives. \\\n",
    "    The absolute step size is computed as ``h = rel_step * max(1, abs(x))``, \\\n",
    "    possibly adjusted to fit into the bounds. Not used if finite_diff_abs_step is given.\n",
    "* - `callback`\n",
    "  - *callable* (None)\n",
    "  - Function to be called after each major iteration. \\\n",
    "    The function is called as`callback(x)`, \\\n",
    "    where ``x`` is the optimization variable vector from the current major iteration.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the complete list of options for `optimize()` and their default values as a dictionary, run `get_default_options()` as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "        obj                      : None\n",
      "        grad                     : None\n",
      "        con                      : None\n",
      "        jac                      : None\n",
      "        meq                      : 0\n",
      "        callback                 : None\n",
      "        xl                       : None\n",
      "        xu                       : None\n",
      "        x_scaler                 : 1.0\n",
      "        obj_scaler               : 1.0\n",
      "        con_scaler               : 1.0\n",
      "        maxiter                  : 100\n",
      "        acc                      : 1e-06\n",
      "        iprint                   : 1\n",
      "        finite_diff_abs_step     : None\n",
      "        finite_diff_rel_step     : 1.4901161193847656e-08\n",
      "        summary_filename         : slsqp_summary.out\n",
      "        warm_start               : False\n",
      "        hot_start                : False\n",
      "        load_filename            : None\n",
      "        save_itr                 : None\n",
      "        save_filename            : slsqp_recorder.hdf5\n",
      "        save_vars                : ['x', 'objective', 'optimality', 'feasibility', 'step', 'iter', 'majiter', 'ismajor', 'mode']\n",
      "        visualize                : False\n",
      "        visualize_vars           : ['objective', 'optimality', 'feasibility']\n",
      "        keep_plot_open           : False\n",
      "        save_figname             : slsqp_plot.pdf\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from pyslsqp import get_default_options\n",
    "\n",
    "options = get_default_options()\n",
    "print_dict_as_table(options)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For postprocessing saved data, see [Post-processing](./postprocessing.ipynb).\n",
    "\n",
    "For the complete API of PySLSQP, visit the [API Reference](./api.md)."
   ]
  }
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