ans/10219074/root_finding_secant_method.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cca11c7c",
   "metadata": {},
   "source": [
    "# root_finding_scan_range_method\n",
    "Jalankan kode pada sel-sel di bawah ini sehingga identitas Anda tertampilkan dengan benar dan carilah akar fungsi dalam suatu rentang yang dipilih."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "264a9720",
   "metadata": {},
   "source": [
    "## identity\n",
    "Koreksi kode pada sel berikut sehingga identitas Anda benar tertampilkan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4e2d555c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10219074 Muhammad Farhan https://github.com/chromaaxion\n"
     ]
    }
   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "HTML(\"<script>Jupyter.notebook.kernel.restart()</script>\")\n",
    "\n",
    "import sys, os\n",
    "sys.path.insert(1, os.path.join(sys.path[0], '../..'))\n",
    "\n",
    "from src.student import Students as stu\n",
    "nim = 10219074\n",
    "print(nim, end=' ')\n",
    "print(stu[nim]['name'], end=' ')\n",
    "print(\"https://github.com/\", stu[nim]['github'], sep='')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4d42d14",
   "metadata": {},
   "source": [
    "## function\n",
    "Sebuah fungsi matematika, misalnya saja\n",
    "\n",
    "<a name='eqn1'></a>\n",
    "\\begin{equation}\\tag{1}\n",
    "f(x) = \\left[ x - 2.5 e^{-1.5(x - 2.4)} \\right] \\sin \\tfrac12 \\pi (x-1.25)\n",
    "\\end{equation}\n",
    "\n",
    "dapat digambarkan dengan dalam rentang $x \\in [x_{\\rm beg}, x_{\\rm end}]$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "91d18261",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "# define a function of x\n",
    "def f(x):\n",
    "    y1 = (x -  2.5 * math.exp(-1.5*(x - 2.4)))\n",
    "    y2 = math.sin(2 * math.pi * (x - 1.25) / 4)\n",
    "    y = y1 * y2\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "50160c26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create data\n",
    "xbeg = 0\n",
    "xend = 10\n",
    "N = 100\n",
    "dx = (xend - xbeg) / N\n",
    "\n",
    "xx = []\n",
    "yy = []\n",
    "\n",
    "for i in range(0, N + 1):\n",
    "    x = xbeg + i * dx\n",
    "    y = f(x)\n",
    "    \n",
    "    xx.append(x)\n",
    "    yy.append(y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b290d5ab",
   "metadata": {},
   "source": [
    "## scan range method\n",
    "Suatu rentang $x \\in [x_i, x_{i+1}]$ dengan\n",
    "\n",
    "<a name='eqn2'></a>\n",
    "\\begin{equation}\\tag{2}\n",
    "f(x_i) f(x_{i+1}) < 0,\n",
    "\\end{equation}\n",
    "\n",
    "memiliki setidaknya satu akar.\n",
    "\n",
    "Kode <a name='cod1'>1</a>. Mencari rentang-rentang terdapatnya akar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3f7cb0c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root range\n",
      "[[1.0, 1.5], [2.0, 2.5], [3.0, 3.5], [5.0, 5.5], [7.0, 7.5], [9.0, 9.5]]\n"
     ]
    }
   ],
   "source": [
    "dx = 0.5\n",
    "\n",
    "root_range = []\n",
    "\n",
    "x = xbeg\n",
    "while x <= xend:\n",
    "    f1 = f(x)\n",
    "    f2 = f(x + dx)\n",
    "    \n",
    "    if f1 * f2 < 0 and x + dx <= xend:\n",
    "        a_range = [x, x + dx]\n",
    "        root_range.append(a_range)\n",
    "        \n",
    "    x += dx\n",
    "\n",
    "print(\"Root range\")\n",
    "print(root_range)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7050f55",
   "metadata": {},
   "source": [
    "Rentang-rentang yang di dalamnya terdapat akar dapat digambarkan pada fungsi yang sebelumnya pada Persamaan ([1](#eqn1)) seperti pada gambar berikut ini.\n",
    "\n",
    "Kode <a name='cod2'>2</a>. Menampilkan kurva dan rentang akar-akarnya."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e0a6ffc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# plot data\n",
    "plt.ion()\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_xlabel(\"$x$\")\n",
    "ax.set_ylabel(\"$y$\")\n",
    "ax.set_ylim([-9, 9])\n",
    "plt.plot(xx, yy)\n",
    "\n",
    "# draw horizontal line\n",
    "line = plt.Line2D((xbeg, xend), (0, 0), color='#aaa', lw=1, ls='dashed')\n",
    "plt.gca().add_line(line)\n",
    "\n",
    "# draw root range\n",
    "for r in root_range:\n",
    "    x1 = r[0]\n",
    "    x2 = r[1]\n",
    "    line = plt.Line2D((x1, x2), (0, 0), color='g', alpha=0.4, lw=4)\n",
    "    plt.gca().add_line(line)\n",
    "\n",
    "# show plot result\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "727025fe",
   "metadata": {},
   "source": [
    "Modifikasi Kode [1](#cod1) dan [2](#cod2) yang diberikan sehingga penggambaran rentang-rentang terdapatnya akar menjadi lebih tepat."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f398de4",
   "metadata": {},
   "source": [
    "## secant method\n",
    "Dalam suatu rentang pencarian $x_i \\le x \\le x_{i+1}$ yang diharapkan terdapat satu akar hubungan iteratif\n",
    "\n",
    "<a name='eqn3'></a>\n",
    "\\begin{equation}\\tag{3}\n",
    "x_{i+2} = x_{i+1} - f(x_{i+1}) \\frac{x_{i+1} - x_i}{f(x_{i+1}) - f(x_i)}\n",
    "\\end{equation}\n",
    "\n",
    "yang dikenal dengan metode secant, akan memberikan perkiraan akar $x_{\\rm root} \\approx x_{N}$ dengan $N > 2$.\n",
    "\n",
    "Kode <a name='cod3'>3</a>. Menentukan rentang yang digunakan untuk mencari akar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0d7f5fb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xbeg =  9.0\n",
      "xend =  9.5\n"
     ]
    }
   ],
   "source": [
    "# set range for secant method\n",
    "n = 5\n",
    "x1 = root_range[n][0]\n",
    "x2 = root_range[n][1]\n",
    "print(\"xbeg = \", x1)\n",
    "print(\"xend = \", x2)\n",
    "\n",
    "xrange1 = x1\n",
    "xrange2 = x2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c7260c2",
   "metadata": {},
   "source": [
    "Kode <a name='cod4'>4</a>. Mencari akar dalam rentang yang dipilih dengan metode secant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "44e1718e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root =  9.249989575333878\n"
     ]
    }
   ],
   "source": [
    "# search root\n",
    "eps = 1E-3\n",
    "err = 1\n",
    "\n",
    "root = ''\n",
    "\n",
    "while abs(err) > eps:\n",
    "    x3 = x2 - f(x2) * (x2 - x1) / (f(x2) - f(x1))\n",
    "    \n",
    "    err = abs(f(x3))\n",
    "    \n",
    "    x1 = x2\n",
    "    x2 = x3\n",
    "    \n",
    "    root = x3\n",
    "\n",
    "print(\"root = \", root)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65226db1",
   "metadata": {},
   "source": [
    "Kode <a name='cod5'>5</a>. Menggambarkan akar yang diperoleh dari pencarian akar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f1eda068",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw result\n",
    "from matplotlib.patches import Ellipse\n",
    "\n",
    "# plot data\n",
    "plt.ion()\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_xlabel(\"$x$\")\n",
    "ax.set_ylabel(\"$y$\")\n",
    "ax.set_ylim([-9, 9])\n",
    "plt.plot(xx, yy)\n",
    "\n",
    "# draw horizontal line\n",
    "line = plt.Line2D((xbeg, xend), (0, 0), color='#aaa', lw=1, ls='dashed')\n",
    "plt.gca().add_line(line)\n",
    "\n",
    "# draw root range\n",
    "line = plt.Line2D((xrange1, xrange2), (0, 0), color='g', alpha=0.4, lw=4)\n",
    "plt.gca().add_line(line)\n",
    "\n",
    "# draw root position\n",
    "point = Ellipse(\n",
    "    xy=(root, 0), width=0.4, height=1, \n",
    "    edgecolor='r', fc='None', lw=1\n",
    ")\n",
    "plt.gca().add_patch(point)\n",
    "\n",
    "# show plot result\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9782e31",
   "metadata": {},
   "source": [
    "Modifikasi Kode [1](#cod1), [2](#cod2), [3](#cod3), [4](#cod4), dan [5](#cod5) sehingga satu per satu dapat diperoleh semua akar dari Persamaan ([1](#eqn1)). Kemudian lengkapi tabel berikut ini."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdc526a0",
   "metadata": {},
   "source": [
    "Tabel <a name='tab1'>1</a> Akar-akar dari Persamaan ([1](#eqn1)).\n",
    "\n",
    "n   | x1  | x2  | root\n",
    ":-: | :-: | :-: | :-:\n",
    "0   | 1.0 | 1.5 | 1.2499579950997508\n",
    "1   | 2.0 | 2.5 | 2.421353917365304\n",
    "2   | 3.0 | 3.5 | 3.2499978719753275\n",
    "3   | 5.0 | 5.5 | 5.250000797964973\n",
    "4   | 7.0 | 7.5 | 7.249922008299404\n",
    "5   | 9.0 | 9.5 | 9.249989575333878"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}