ans/10219052/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": [
      "10219052 Tiara Novis Saputri https://github.com/tiaranovis\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 = 10219052\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='#aaaaaa', 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 =  1.0\n",
      "xend =  1.5\n"
     ]
    }
   ],
   "source": [
    "# set range for secant method\n",
    "n = 0\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\n",
    "    "
   ]
  },
  {
   "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 =  1.2499579950997508\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='#aaaaaa', 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(xy=(root, 0), width=0.4, height=1, edgecolor='r', fc='None', lw=1)\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   | 1,5    | 1,249  \n",
    "1   |   2  | 2,5    | 2,421 \n",
    "2  |   3  | 3,5    | 3,249\n",
    "3  |   5  | 5,5    | 5,250\n",
    "4  |   7  | 7,5    | 7,249\n",
    "5  |   9  | 9,5    | 9,249"
   ]
  }
 ],
 "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}