ans/10219029/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": [
      "10219029 Isnaini Mufidhatul Mughni https://github.com/isnainimufidhatulmughni\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 = 10219029\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": 15,
   "id": "3f7cb0c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root range\n",
      "[[1.2, 1.3], [2.400000000000001, 2.500000000000001], [3.2000000000000015, 3.3000000000000016], [5.1999999999999975, 5.299999999999997], [7.19999999999999, 7.29999999999999], [9.199999999999983, 9.299999999999983]]\n"
     ]
    }
   ],
   "source": [
    "dx = 0.1\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": 16,
   "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": 9,
   "id": "0d7f5fb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xbeg =  9\n",
      "xend =  10\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": 10,
   "id": "44e1718e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root =  9.249991179634087\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": 17,
   "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",
    "0   |       1.2        |        1.3       |1.2500029670299357 \n",
    "1   |2.400000000000001 |2.500000000000001 |2.4211443453893393  \n",
    "2   |3.2000000000000015|3.3000000000000016|3.2499256146863793\n",
    "3   |5.1999999999999975|5.299999999999997 |5.249995479687406\n",
    "4   |7.19999999999999  |7.29999999999999  |7.249997975796951\n",
    "5   |9.199999999999983 |9.299999999999983 |9.249998821611408"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08a10de5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}