Optimization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining the function\n",
    "\n",
    "def function(x):\n",
    "\n",
    "# Manually enter the function in the return statement\n",
    "\n",
    "    return 5*x**4 - 4*x**2 + 2*x -3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "initial_set = np.linspace(-100,100,21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-3.0\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "temp=10000000\n",
    "\n",
    "y = []\n",
    "\n",
    "for i in range (len(initial_set)):\n",
    "\n",
    "    value = function(initial_set[i])\n",
    "\n",
    "    y.append(value)\n",
    "\n",
    "    if (value)<=(temp):\n",
    "\n",
    "        temp = value\n",
    "\n",
    "        j = initial_set[i] \n",
    "    \n",
    "    else:\n",
    "    \n",
    "        continue\n",
    "\n",
    "print(temp)\n",
    "\n",
    "print(j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc6e28f0370>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IsgivPX3V3AlkpMVn5MVn1Wdh5YIyDrZ1U9ugNapFksGJtafjcFpeWNIF9Yp5ZZih2R8iSeKF2sNkpKbwoXPjY+3pSJIuqEtyM6msKFKXokgSCK49fZjLZo+Pm7WnI0m6oIbgJbq2NXSwv0VrVIsksh2HO6lv6YqbS24NJymD+r01qnVULZLI3lt7eoLXpZyVpAzq6SU5nFuWp0t0iSS4NbWNLC0vYkJeltelnJWkDGoIzv54e18LrVqjWiQhHWzrZsvBjri65NZwkjeo54fWqN7e5HUpIhIFL4Q+Mcf7+DQkcVAvnJLPpIIsDX+IJKg1tYc5Z0L8rT0dSdIGtZmxcn4Za7VGtUjCaevqY/3elrhd2+NkSRvUEPxI1NMf4FWtUS2SUE6sPR3H3YhDJXVQXzijmPysNE3TE0kwa7Yejtu1pyNJ6qBOT01hxbwyfrdNa1SLJIqe/vfWnk5Jib+1pyNJ6qCG4BrVrV39VNVpjWqRRPDaziN09w8mzPg0KKjfW6NaizSJJIQ1tfG99nQkpw1qM5tmZi+bWa2ZbTWzL8aisFjJyUzj8tkl/HZrI4GA1qgWiWf9gwFe3NbElefG79rTkYzkv2QA+LJzbj5wMXCvmc2PblmxdeOSKRxs6+YlNb+IxLXnNzfQcryPG5dM9rqUMXXaoHbONTjnakK3O4FtwJRoFxZL1y6cyJTCcax+dY/XpYjIGXLOsXrtHmZPyOXDc+J7EaaTjeqzgZlNB5YA6yPct8rMqsysqrk5vuYlp6em8KnLZ/DW3hY27m/zuhwROQNv7D5KbUMHq66YmTCzPcJGHNRmlgs8A9znnPvAdaycc6udc5XOucrS0vi7ksJtF0wjLyuNh3VULRKXVq/dQ0luJjck2LAHjDCozSydYEg/4Zz7eXRL8kZuZhofv6iCX7/TQP1RXVBAJJ5sb+zg9+8288nLppOZlup1OWNuJLM+DHgE2OaceyD6JXnnk5dNJzXFePT1vV6XIiKj8PDavWRnpPLxi8q9LiUqRnJEfRnwp8BVZrYx9HVdlOvyRFl+FjcsnsJTb++nrUvrVIvEg8b2Hp7bdJBbK6dRmJ3hdTlRMZJZH68558w5d75zbnHo61exKM4Ld18xk+7+QX7yZp3XpYjICDz2xl4GA45PXz7D61KiJnFmhI+Rcyfm8aE5pTz+Rh09/Vr+VMTPOnv6+Y8367n2vElMK872upyoUVBHcM/ymRw51ssvNh70uhQROYWn3t5PZ+8A9yyf6XUpUaWgjuCSWeNZMDmfh1/dq7ZyEZ/qHwzw2Ov7uGhGMedPLfS6nKhSUEdgZqxaPpNdTcd4eYfaykX86FfvNHCwrZtVCX40DQrqYV133iQmF2Sxeq0aYET8JtwuPqs0hyvPTax28UgU1MMIt5Wv39vCJrWVi/jKut1H2Xqog1XLE69dPBIF9SncfmG52spFfGj1q6F28cUJtT7csBTUp5CbmcafXFTOr95pYH+L2spF/GBHYyev7GjmrksryEpPvHbxSBTUp/HJS2eQmmI88praykX84OFX9zAuPZWPX1ThdSkxo6A+jYkFWVy/aApPV6mtXMRrhzt6+MXGg9x2wTSKchKzXTwSBfUI3L18Bl19gzyxvt7rUkSS2mOv72Mw4PjUZYnbLh6JgnoE5k7MZ/mcUh57fR+9A2orF/HCsd4Bnlhfx7ULJ1E+PnHbxSNRUI/QqitCbeUbDnldikhSeurt/XT2DHB3EjS4nExBPUKXzR7P/En5rH51j9rKRWJsYDDAo6/t5cIZxSyeVuh1OTGnoB6hoW3lr7yrtnKRWHo+3C5+RfIdTYOCelQ+er7aykVizTnHw68G28Wvmpv47eKRKKhHIdxW/uaeFjYfaPO6HJGksG7PUbYc7ODuBLy6+EgpqEfptgumkZeZpqNqkRgJXl08gxuXJEe7eCQK6lHKy0pXW7lIjITbxT9xyfSkaRePREF9Bu66bDoppquVi0Tbv4Xaxe+8OHnaxSNRUJ+BSQXjuH7xZJ56ez/tXf1elyOSkJo6evivjQe5tXJqUrWLR6KgPkN3XzGTrr5BfrJeVysXiYbH3gi1iyfw1cVHSkF9huZNyueKc0p4/A21lYuMtWO9AzzxZh3XLJxIxfgcr8vxnIL6LKxaPpPmzl5+sVFt5SJj6em399PRM8DdSdrgcjIF9Vm4fHYJ8ybl8/BatZWLjJWBwQCPvLaXC6cXs6S8yOtyfEFBfRbMjHuWz2Rn0zGeqtrvdTkiCeHxN/ZxsK07KRdfGo6C+ixdv2gyl84az7d/WUvd0eNelyMS13Y0dvJPv9nB1fPL+Mi85GwXj0RBfZZSUozv3LKI1BTjL57ayMBgwOuSROJS78Ag9z21kfxxafzDzedhlpzt4pEoqMfA5MJx/K8bF1JT38ZDv9/tdTkicemBF95lW0MH93/sfEpyM70ux1cU1GPkhsVT+KNFk/mXF3fyzoF2r8sRiSvr9xxl9do93HFhOSvmlXldju+cNqjN7FEzazKzLbEoKJ59+4YFlORmct9TG+jp19xqkZHo7OnnS09vorw4m7/+6Dyvy/GlkRxRPw5cE+U6EkJhdgbfuWURu5uP84+/3u51OSJx4ZvP1dLQ3s0Dty4mJzPN63J86bRB7ZxbC7TEoJaEcPk5JXzysuk8/sY+1r7b7HU5Ir7263caeKbmAJ+/cjbLKjRnejhjNkZtZqvMrMrMqpqbkzug/sc1czlnQi5/9bNNtHX1eV2OiC81dfTw9Wff4fypBXxhxTlel+NrYxbUzrnVzrlK51xlaWnpWP3auJSVnsr3bltMy/E+vvFfW3BOXYsiQznn+Mozm+nuH+R7ty0mPVXzGk5FeydKFk4p4L6PzOH5zQ1aC0TkJD9ZX88rO5r5+nXzmFWa63U5vqegjqLPfmgWlRVF/M9fbOFgW7fX5Yj4wp7mY/z987Usn1PKnyb5BQFGaiTT834KrAPONbMDZvbp6JeVGFJTjAduXUwg4PjLpzdp4SZJev2DAf7iqY1kpafyz398vroPR2gksz7ucM5Ncs6lO+emOuceiUVhiaJ8fDZ/+0cLWLfnqC7dJUnv+y/tYtOBdv73TedRlp/ldTlxQ0MfMXBL5VRWzi/jn36zgx2NnV6XI+KJDfWtfP/lXdy8ZArXnTfJ63LiioI6BsyMf7j5PPLHpfHFJzfoijCSdLr6BvjS05uYmJ/FN29Y4HU5cUdBHSPjczO5/2Pns72xkwdeeNfrckRi6u+f38a+o8f57q2LyM9K97qcuKOgjqEV88q448JyVq/dw/o9R70uRyQmXt7exBPr67n7iplcPHO81+XEJQV1jP31R+dRUZzNl57eREdPv9fliETV0WO9/NXPNjN3Yh5fXjnH63LiloI6xnIy03jgtsU0dvTwredqvS5HJGqcc3zt5+/Q0d3P925bTGZaqtclxS0FtQeWlhdx75WzeabmAL9+p8HrckSi4mfVB1hTe5i//IM5zJuU73U5cU1B7ZEvXDWb86cW8PVn36GhXV2Lkljqjh7nW/9dy8Uzi/nM5bpI7dlSUHskPTWF7922mN6BAH/84Dp2NWl+tSSGLQfbueWhdaQYfOeWRaSkqPvwbCmoPTSrNJcnV11M70CAm3/4But2ayaIxLeXtzdx6/9bR1qK8bM/v5SpRdlel5QQFNQeO39qIc9+7lIm5GfxZ4+u5782HPS6JJEz8h/r6/nMj6uYUZLDs/dexpyyPK9LShgKah+YVpzNM5+9lGUVRdz31Ea+/9JOrWEtcSMQcNz/m+18/dl3WH5OCU/fc4nW8RhjCmqfKMhO50efupCblkzhO2ve5Ws/f4f+wYDXZYmcUu/AIF98aiMPvrKbP7monIf/rFLXPYwC7VEfyUxL5YFbFzG1aBz/96VdHGrv4Qd/soQ8tdyKD7V19bHqx9W8ta+Fr147l3uWz9SypVGiI2qfMTO+vPJc7v/Yeby+6wi3PLSOxvYer8sSeZ/6o13c/OAbbNzfxr/esYTPfmiWQjqKFNQ+ddsF5Tx21wUcaO3mph++zraGDq9LEgFg4/42bn7wdY4e6+Mnn7mI6xdN9rqkhKeg9rHlc0p5+p5LcA5ueWgda99N7qu7i/fWbG3k9tXrGJeRys8/dykXzij2uqSkoKD2ufmT83n23kuZWjSOTz3+Nk+/vd/rkiRJPfb6Xu75STXnTszn2c9dpovSxpCCOg5MKhjHf372Ei6ZNZ6vPLOZB9bs0PQ9iZlAwPHtX9byrf+u5ep5ZTx598WU5GZ6XVZSUVDHibysdB696wJurZzKv760iy8/vYm+AU3fk+jq6R/kc0/U8Mhre7nr0uk8eOcyxmVoFbxY0/S8OJKemsL9HzufaUXZfPeFd2lo7+GhO5dRkK3pezL2jhzr5e4fV7Fxfxt/84fz+dTlM7wuKWkpqOOMmfGFFecwtXgcX/nZZj70nZf55KUz+MSlFRRmZ3hdniSAo8d6efT1vfz4jTr6BgM8+PFlXLNwotdlJTUFdZy6aclUzpmQx7+8+C7fe/FdVq/dzZ2XVPDpy2cwIU/tuzJ6De3drF67h5++VU/vQIBrF07kiyvmcO5ErdnhNYvGSanKykpXVVU15r9XItvW0MEPX9nN85sPkZ6awm0XTGPV8plauUxGZN+R4zz0+908U3OAgIMbF0/hzz88k9kTFNCxZGbVzrnKiPcpqBPH3iPHeeiV3fx8wwGcgxuXTOHPPzxL06gkoh2NnfzwlV3896ZDpKWmcFtl8A/8tGL9gfeCgjrJHGp77yNs32CA686bxOc+PIsFkwu8Lk18YNP+Nr7/8i5eqD1MdkYqd15cwWcun8EErXjnKQV1kjpyrJdHXtvLv6+r41jvAFfNncC9V85mWUWR16VJjDnneHNPCz94eRev7TpCwbh07rp0OnddOp2iHJ2E9gMFdZJr7+rnR+v28ejre2nr6ufimcV8/spzuGz2eC2kk+Ccc7y8o4kfvLyb6rpWSnIz+cwVM7jz4gpytRypryioBYDjvQP89K16Vq/dQ1NnL7NKc7hwxniWVRRRWVFExfhsBXecc86x58hxqve1Ul3Xyvq9R9l3tIspheO450MzubVyGlnpaljxIwW1vE/vwCDPVB/kt1sbqalvpbNnAIDxORksrSg6EdwLpxTof2qf6+4bZNOBNqrrWqmpa6W6vpW2rn4ACsals6yiiGsWTuSmJVNIT1Ujsp8pqGVYgYBjZ9MxquuCR2A19a3sPXIcgPRUY+GUApaVB8N7WUWRTjh5rLG9h6q6lhPBvPVQBwOB4P/Ds0pzTrxOyyqKmFmSqyuAx5GzDmozuwb4P0Aq8G/OuX881eMV1PHtyLHeE0dnNXWtbDrQfmJdkWnF41hWXsR5UwuZkJfJ+NwMxucEvxdlZ5CqYDgrA4MBWrv6OXq8l5ZjfRw53kdTRw+bDrRTU9fKwbZuALLSU1g0tTD46Wd6EUumFemkYJw7q6A2s1TgXeBq4ADwNnCHc652uJ9RUCeWvoEAWw+1nzjqrqprpbmz9wOPM4Oi7AzG52RQnJNBSW4wwItzMhifm8n4nPfuy0xLJS3VSEs10lNSgt9TU0hLMVJTLO7Gyp1zDAQcA4OO/kCAgUHHwGCA/kDwe+9AgJbjfRw91kfL8V6OHOsL/nvo7WO9tHX3E+l/yUkFWe87Wp43KV9DGQnmVEE9ktO+FwK7nHN7Qr/sSeAGYNiglsSSkZbCkvIilpQX8ZkrgqEUDJlg8Bw93hv6Hgybo6Hg2dbYQcvxvhNjpqORnmqknRTg6anBf6cMCfH3xblFvDls6A89SHlfNrqINxkMvD983xfKgdEPIRZlp5/4IzanLJfimcWMz8mkJDeD4tCnlJLQJxYdLSe3kQT1FGDoavUHgItOfpCZrQJWAZSXl49JceJPZhY8Qs7NhLLTP75/MEDrkGBv6eqjbyAwfOgN2d4/6BgIbQ/fDufr0GgcWeg6jJNCewThbie2QVpKSvCPSOgPSfB2Cukpwe9DPyEM3Z6RlhL8RBH6hFGcnUGajohlhMZsIqVzbjWwGoJDH2P1eyX+paemMCE/SyciRc7QSP6kHwSmDfn31NA2ERGJgZEE9dvAOWY2w8wygNuB56JbloiIhJ126MM5N2Bmnwd+S3B63qPOua1Rr0xERIARjlE7534F/CrKtYiISAQ67Swi4nMKahERn1NQi4j4nIJaRMTnorJ6npk1A3Vn+OMlwJExLGesqK7RUV2jo7pGJxHrqnDOlUa6IypBfTbMrGq4hUm8pLpGR3WNjuoanWSrS0MfIiI+p6AWEfE5Pwb1aq8LGIbqGh3VNTqqa3SSqi7fjVGLiMj7+fGIWkREhlBQi4j4nGdBbWa3mNlWMwuYWeVJ933NzHaZ2Q4z+4Mh268JbdtlZl+NQY1PmdnG0Nc+M9sY2j7dzLqH3PdQtGs5qa5vmtnBIc9/3ZD7Iu67GNX1z2a23cw2m9mzZlYY2u7p/grVENP3zinqmGZmL5tZbej9/8XQ9mFf0xjXt8/M3gnVUBXaVmxmL5jZztD3ohjXdO6Q/bLRzDrM7D4v9pmZPWpmTWa2Zci2iPvHgv419J7bbGZLz/iJnXOefAHzgHOBV4DKIdvnA5uATGAGsJvg8qqpodszgYzQY+bHsN7vAn8Tuj0d2OLhvvsm8JcRtkfcdzGsayWQFrp9P3C/T/aXp++dk2qZBCwN3c4jeOHo+cO9ph7Utw8oOWnbPwFfDd3+avh19fC1bAQqvNhnwHJg6dD383D7B7gO+DXBq7ldDKw/0+f17IjaObfNObcjwl03AE8653qdc3uBXQQvsHviIrvOuT4gfJHdqLPgBfRuBX4ai+c7C8Ptu5hwzq1xzg2E/vkmwasB+YFn752TOecanHM1odudwDaC1yX1sxuAH4Vu/wi40btSWAHsds6daefzWXHOrQVaTto83P65AfixC3oTKDSzSWfyvH4co450Md0pp9geC1cAh51zO4dsm2FmG8zs92Z2RYzqGOrzoY9Tjw75KOrlPjrZpwgeTYR5ub/8tF9OMLPpwBJgfWhTpNc01hywxsyqLXjBaoAy51xD6HYjI7qkcdTczvsPmPywz4bbP2P2votqUJvZi2a2JcKXJ0czkYywxjt4/5ujASh3zi0BvgT8h5nlx7CuB4FZwOJQLd8dy+c+i7rCj/kGMAA8EdoU9f0Vb8wsF3gGuM8514GHr+lJLnfOLQWuBe41s+VD73TBz/SezOm14KUArwf+M7TJL/vshGjtnzG7CnkkzrmPnMGPnepiumN+kd3T1WhmacDNwLIhP9ML9IZuV5vZbmAOUHW29Yy0riH1PQz8MvTPqF+IeAT76y7gD4EVoTdtTPbXafjqAs1mlk4wpJ9wzv0cwDl3eMj9Q1/TmHLOHQx9bzKzZwkOGx02s0nOuYbQR/cmL2oj+MejJryv/LLPGH7/jNn7zo9DH88Bt5tZppnNAM4B3sK7i+x+BNjunDsQ3mBmpWaWGro9M1TjnhjUEn7+oeNcNwHhM9DD7btY1XUN8BXgeudc15Dtnu4vfHSB5tD5jkeAbc65B4ZsH+41jWVtOWaWF75N8OTwFoL76hOhh30C+EWsawt53ydbP+yzkOH2z3PAn4Vmf1wMtA8ZIhmdWJ+1HXKm9CaCYza9wGHgt0Pu+wbBs/Q7gGuHbL+O4Fny3cA3YlTn48BnT9r2MWArsBGoAf4oxvvu34F3gM2hN8Ok0+27GNW1i+CY3MbQ10N+2F9evXeGqeNygh+NNw/ZT9ed6jWNYW0zCc6I2RR6vb4R2j4e+B2wE3gRKPagthzgKFAwZFvM9xnBPxQNQH8ovz493P4hONvjB6H33DsMmd022i+1kIuI+Jwfhz5ERGQIBbWIiM8pqEVEfE5BLSLicwpqERGfU1CLiPicglpExOf+PyIeIhP4pOrsAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(initial_set, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-100.,  -90.,  -80.,  -70.,  -60.,  -50.,  -40.,  -30.,  -20.,\n",
       "        -10.,    0.,   10.,   20.,   30.,   40.,   50.,   60.,   70.,\n",
       "         80.,   90.,  100.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-4.6875\n",
      "-0.5\n"
     ]
    }
   ],
   "source": [
    "second_list= np.linspace(-10,10,41)\n",
    "\n",
    "y= []\n",
    "\n",
    "for i in range (len(second_list)):\n",
    "\n",
    "    value = function(second_list[i])\n",
    "\n",
    "    y.append(value)\n",
    "\n",
    "    if (value)<=(temp):\n",
    "\n",
    "        temp = value\n",
    "\n",
    "        j = second_list[i] \n",
    "\n",
    "    else:\n",
    "\n",
    "        continue\n",
    "\n",
    "print(temp)\n",
    "\n",
    "print(j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc6e285b8b0>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(second_list, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-10. ,  -9.5,  -9. ,  -8.5,  -8. ,  -7.5,  -7. ,  -6.5,  -6. ,\n",
       "        -5.5,  -5. ,  -4.5,  -4. ,  -3.5,  -3. ,  -2.5,  -2. ,  -1.5,\n",
       "        -1. ,  -0.5,   0. ,   0.5,   1. ,   1.5,   2. ,   2.5,   3. ,\n",
       "         3.5,   4. ,   4.5,   5. ,   5.5,   6. ,   6.5,   7. ,   7.5,\n",
       "         8. ,   8.5,   9. ,   9.5,  10. ])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "second_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-5.171091796875\n",
      "-0.725\n"
     ]
    }
   ],
   "source": [
    "third_list = np.linspace(-1,0,41)\n",
    "\n",
    "y=[]\n",
    "\n",
    "for i in range (len(third_list)):\n",
    "\n",
    "    value = function(third_list[i])\n",
    "\n",
    "    y.append(value)\n",
    "\n",
    "    if (value)<=(temp):\n",
    "\n",
    "        temp = value\n",
    "\n",
    "        j = third_list[i] \n",
    "\n",
    "    else:\n",
    "\n",
    "        continue\n",
    "\n",
    "print(temp)\n",
    "\n",
    "print(j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc6e2b95040>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(third_list, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.   , -0.975, -0.95 , -0.925, -0.9  , -0.875, -0.85 , -0.825,\n",
       "       -0.8  , -0.775, -0.75 , -0.725, -0.7  , -0.675, -0.65 , -0.625,\n",
       "       -0.6  , -0.575, -0.55 , -0.525, -0.5  , -0.475, -0.45 , -0.425,\n",
       "       -0.4  , -0.375, -0.35 , -0.325, -0.3  , -0.275, -0.25 , -0.225,\n",
       "       -0.2  , -0.175, -0.15 , -0.125, -0.1  , -0.075, -0.05 , -0.025,\n",
       "        0.   ])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "third_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-5.171761952929687\n",
      "-0.7324999999999999\n"
     ]
    }
   ],
   "source": [
    "fourth_list = np.linspace(-0.75,-0.70,41)\n",
    "\n",
    "y=[]\n",
    "\n",
    "for i in range (len(fourth_list)):\n",
    "\n",
    "    value = function(fourth_list[i])\n",
    "\n",
    "    y.append(value)\n",
    "\n",
    "    if (value)<=(temp):\n",
    "\n",
    "        temp = value\n",
    "\n",
    "        j = fourth_list[i] \n",
    "\n",
    "    else:\n",
    "\n",
    "        continue\n",
    "\n",
    "print(temp)\n",
    "\n",
    "print(j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc6e29a0a00>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fourth_list, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.75   , -0.74875, -0.7475 , -0.74625, -0.745  , -0.74375,\n",
       "       -0.7425 , -0.74125, -0.74   , -0.73875, -0.7375 , -0.73625,\n",
       "       -0.735  , -0.73375, -0.7325 , -0.73125, -0.73   , -0.72875,\n",
       "       -0.7275 , -0.72625, -0.725  , -0.72375, -0.7225 , -0.72125,\n",
       "       -0.72   , -0.71875, -0.7175 , -0.71625, -0.715  , -0.71375,\n",
       "       -0.7125 , -0.71125, -0.71   , -0.70875, -0.7075 , -0.70625,\n",
       "       -0.705  , -0.70375, -0.7025 , -0.70125, -0.7    ])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fourth_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-5.171761952929687\n",
      "-0.7324999999999999\n"
     ]
    }
   ],
   "source": [
    "fifth_list = np.linspace(-0.73375,-0.73125,41)\n",
    "\n",
    "y=[]\n",
    "\n",
    "for i in range (len(fifth_list)):\n",
    "\n",
    "    value = function(fifth_list[i])\n",
    "\n",
    "    y.append(value)\n",
    "\n",
    "    if (value)<=(temp):\n",
    "\n",
    "        temp = value\n",
    "\n",
    "        j = fifth_list[i] \n",
    "\n",
    "    else:\n",
    "\n",
    "        continue\n",
    "\n",
    "print(temp)\n",
    "\n",
    "print(j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc6e29153a0>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fifth_list, y)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
  },
  "kernelspec": {
   "display_name": "Python 3.8.10 64-bit",
   "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": "3.8.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}