{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"toc": "true"
},
"source": [
"Table of Contents
\n",
""
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-exp(-x) + 4*exp(-2*x)\n"
]
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"from sympy import *\n",
"\n",
"x = symbols('x')\n",
"\n",
"\n",
"def func(x):\n",
" return exp(-x)-2*exp(-2*x)\n",
"\n",
"def df(x):\n",
" return diff(func(x), x)\n",
"\n",
"print(df(x))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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HIGXOsWPHGDx4MJs2beL+++8nJiaGa64p9ARzEfGCCoCUKW+//TbDhw/np59+YuHChQwZ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\nVPVwXlQAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def func(x):\n",
" return np.exp(-x)-2*np.exp(-2*x)\n",
"\n",
"def df(x):\n",
" return -np.exp(-x) + 4*np.exp(-2*x)\n",
"\n",
"x0=0.0\n",
"x1=1.0\n",
"x = np.linspace(x0, x1, 100)\n",
"y = func(x)\n",
"plt.plot(x, y, color = 'k')\n",
"plt.plot([x0,x1],[0,0])\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.69314718056\n"
]
}
],
"source": [
"from scipy.optimize import fsolve\n",
"x0 = fsolve(func, 0.0)[0]\n",
"pprint(x0)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" x1 x2 f1 f2\n",
"0.000 1.000 -1.000 0.097 \n",
" +0.5000000000 +1.0000000000 -0.1292282226 +0.0972088747\n",
" +0.5000000000 +0.7500000000 -0.1292282226 +0.0261062324\n",
" +0.6250000000 +0.7500000000 -0.0377481652 +0.0261062324\n",
" +0.6875000000 +0.7500000000 -0.0028476136 +0.0261062324\n",
" +0.6875000000 +0.7187500000 -0.0028476136 +0.0123194385\n",
" +0.6875000000 +0.7031250000 -0.0028476136 +0.0049148184\n",
" +0.6875000000 +0.6953125000 -0.0028476136 +0.0010791492\n",
" +0.6914062500 +0.6953125000 -0.0008727415 +0.0010791492\n",
" +0.6914062500 +0.6933593750 -0.0008727415 +0.0001060635\n",
" +0.6923828125 +0.6933593750 -0.0003826225 +0.0001060635\n",
" +0.6928710938 +0.6933593750 -0.0001381006 +0.0001060635\n",
" +0.6931152344 +0.6933593750 -0.0000159739 +0.0001060635\n",
" +0.6931152344 +0.6932373047 -0.0000159739 +0.0000450560\n",
" +0.6931152344 +0.6931762695 -0.0000159739 +0.0000145439\n",
" +0.6931457520 +0.6931762695 -0.0000007143 +0.0000145439\n",
" +0.6931457520 +0.6931610107 -0.0000007143 +0.0000069149\n",
" +0.6931457520 +0.6931533813 -0.0000007143 +0.0000031004\n",
" +0.6931457520 +0.6931495667 -0.0000007143 +0.0000011930\n",
" +0.6931457520 +0.6931476593 -0.0000007143 +0.0000002394\n",
" +0.6931467056 +0.6931476593 -0.0000002375 +0.0000002394\n",
"[[0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19], [0.69314718055994529, 0.19314718055994529, 0.056852819440054714, 0.068147180559945286, 0.0056471805599452862, 0.025602819440054714, 0.0099778194400547138, 0.0021653194400547138, 0.0017409305599452862, 0.00021219444005471377, 0.00076436805994528623, 0.00027608680994528623, 3.1946184945286227e-05, 9.0124127554713773e-05, 2.9088971304713773e-05, 1.4286068202862268e-06, 1.3830182242213773e-05, 6.2007877109637732e-06, 2.3860904453387732e-06, 4.7874181252627324e-07, 4.7493250387997676e-07]]\n"
]
}
],
"source": [
"x1, x2 = 0.0, 1.0\n",
"f1, f2 = func(x1), func(x2)\n",
"print('%+15s %+15s %+15s %+15s' % ('x1','x2','f1','f2'))\n",
"print('%-6.3f %-6.3f %-6.3f %-6.3f' % (x1,x2,f1,f2))\n",
"#x0 = 0.7034674224983916520498186018599021303429\n",
"list_bisec = [[0],[abs(x1-x0)]]\n",
"for i in range(0, 20):\n",
" x = (x1 + x2)/2\n",
" f = func(x)\n",
" if (f*f1>=0.0):\n",
" x1, f1 = x, f\n",
" list_bisec[0].append(i)\n",
" list_bisec[1].append(abs(x1-x0))\n",
" else:\n",
" x2, f2 = x, f\n",
" list_bisec[0].append(i)\n",
" list_bisec[1].append(abs(x2-x0))\n",
"\n",
" print('%+15.10f %+15.10f %+15.10f %+15.10f' % (x1,x2,f1,f2))\n",
"\n",
"print(list_bisec)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.0000000000 +0.0972088746982169293886500\n",
"0.4395945658 -0.1859411764694577362533323\n",
"0.6225751275 -0.0392343494594583619416994\n",
"0.6863677429 -0.0034243718517862342665126\n",
"0.6930789080 -0.0000341397924060737878449\n",
"[[0, 0, 1, 2, 3], [1.0, 0.25355261477134106, 0.070572053060396933, 0.0067794376968084435, 6.8272592720464154e-05]]\n"
]
}
],
"source": [
"x1 = 1.0\n",
"f1 = func(x1)\n",
"list_newton = [[0],[x1]]\n",
"print('%-15.10f %+24.25f' % (x1,f1))\n",
"for i in range(0, 4):\n",
" x1 = x1 - f1 / df(x1)\n",
" f1 =func(x1)\n",
" print('%-15.10f %+24.25f' % (x1,f1))\n",
" list_newton[0].append(i)\n",
" list_newton[1].append(abs(x1-x0))\n",
"\n",
"print(list_newton)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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EsAJoISKpIhIBDAdmuyOooJsLqKK4BpDQUhNAgMtKrcMV6fWZvGD7edcxdsae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\nRK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"X = list_bisec[0]\n",
"Y = list_bisec[1]\n",
"plt.plot(X, Y)\n",
"\n",
"X = list_newton[0]\n",
"Y = list_newton[1]\n",
"plt.plot(X, Y)\n",
"\n",
"\n",
"plt.yscale(\"log\") # y軸を対数目盛に\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.8.3"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
"autocomplete": true,
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 1,
"hotkeys": {
"equation": "Ctrl-E",
"itemize": "Ctrl-I"
},
"labels_anchors": false,
"latex_user_defs": false,
"report_style_numbering": false,
"user_envs_cfg": false
},
"toc": {
"base_numbering": 1,
"nav_menu": {
"height": "12px",
"width": "252px"
},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": true,
"toc_position": {},
"toc_section_display": "block",
"toc_window_display": true
}
},
"nbformat": 4,
"nbformat_minor": 2
}