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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"다음 조건을 만족시키는 두 자연수 $a$, $b$의 모든 순서쌍 $(a, b)$의 개수를 구하여라.\n", | |
"\n", | |
"- $1 \\le a \\le 10$, $1 \\le b \\le 100$\n", | |
"- 곡선 $y=2^x$이 원 $(x-a)^2 + (y-b)^2 = 1$과 만나지 않는다\n", | |
"- 곡선 $y=2^x$이 원 $(x-a)^2 + (y-b)^2 = 4$와 적어도 한 점에서 만난다" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## Solution\n", | |
"\n", | |
"먼저 임의의 점 $(p, q)$와 $y=2^x$와의 거리를 측정해보도록 하자. 이는 $y=2^x$라는 constraint가 존재할 때 $(x-p)^2 + (y-q)^2$를 최소화시키는 문제와 같다.\n", | |
"\n", | |
"따라서 라그랑지안 $\\mathcal{L}$은 다음과 같이 정의된다\n", | |
"\n", | |
"$$\n", | |
"\\mathcal{L}(x, y, \\lambda) = (x-p)^2 + (y-q)^2 + \\lambda(xlog2 - logy)\n", | |
"$$\n", | |
"\n", | |
"$\\nabla\\mathcal{L} = 0$이어야 하므로 다음 식이 성립한다\n", | |
"\n", | |
"$$\n", | |
"\\begin{align}\n", | |
"\\nabla_{x}\\mathcal{L} &= 2x - 2p + \\lambda log2 = 0 \\\\\n", | |
"\\nabla_{y}\\mathcal{L} &= 2y - 2q - \\frac{\\lambda}{y} = 0 \\\\\n", | |
"\\nabla_{\\lambda}\\mathcal{L} &= xlog2 - logy = 0\n", | |
"\\end{align}\n", | |
"$$\n", | |
"\n", | |
"1, 2번식을 정리하면 $x$와 $y$를 $\\lambda$에 대해서 표현할 수 있다.\n", | |
"\n", | |
"$$\n", | |
"\\begin{align}\n", | |
"x &= p - \\frac{\\lambda log2}{2} \\\\\n", | |
"y &= \\frac{q + \\sqrt{q^2 + 2\\lambda}}{2}(\\because y \\gt 0)\n", | |
"\\end{align}\n", | |
"$$\n", | |
"\n", | |
"이 식을 3번에 대입하면 $\\lambda$에 대한 식으로 바뀌고, 이는 newton raphson method로 해를 구할 수 있다. 이렇게 구한 좌표 $(x, y)$와 $(p, q)$간의 거리가 $y=2^x$와 $(p, q)$ 사이의 거리가 된다." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"import math\n", | |
"from scipy import optimize\n", | |
"from functools import partial\n", | |
"log2 = math.log(2)\n", | |
"\n", | |
"def x(p, l):\n", | |
" return p - l*log2/2\n", | |
" \n", | |
"def y(q, l):\n", | |
" return (q + math.sqrt(q**2 + 2*l))/2\n", | |
"\n", | |
"def f(p, q, l):\n", | |
" return x(p, l)*log2 - math.log(y(q, l))\n", | |
"\n", | |
"def dist(p, q):\n", | |
" \"\"\"Get Euclidean distance between y=2^x and (p, q)\"\"\"\n", | |
" l = optimize.newton(lambda l: f(p, q, l), 1.0)\n", | |
" x_ = x(p, l)\n", | |
" y_ = 2**x_\n", | |
" return math.sqrt((x_-p)**2 + (y_-q)**2)\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"2.7012892057857038e-15\n" | |
] | |
} | |
], | |
"source": [ | |
"# test\n", | |
"print(dist(3, 8))" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"196" | |
] | |
}, | |
"execution_count": 3, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"points = []\n", | |
"for a in range(1, 11):\n", | |
" for b in range(1, 101):\n", | |
" if 1 < dist(a, b) <= 2:\n", | |
" points.append((a, b))\n", | |
"len(points)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"IOPub data rate exceeded.\n", | |
"The notebook server will temporarily stop sending output\n", | |
"to the client in order to avoid crashing it.\n", | |
"To change this limit, set the config variable\n", | |
"`--NotebookApp.iopub_data_rate_limit`.\n" | |
] | |
} | |
], | |
"source": [ | |
"from plotly.offline import init_notebook_mode\n", | |
"init_notebook_mode()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
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84.0, \"y1\": 88.0, \"line\": {\"color\": \"rgba(50, 171, 96, 0.3)\"}}, {\"type\": \"circle\", \"xref\": \"x\", \"yref\": \"y\", \"x0\": 6.0, \"x1\": 10.0, \"y0\": 85.0, \"y1\": 89.0, \"line\": {\"color\": \"rgba(50, 171, 96, 0.3)\"}}, {\"type\": \"circle\", \"xref\": \"x\", \"yref\": \"y\", \"x0\": 6.0, \"x1\": 10.0, \"y0\": 86.0, \"y1\": 90.0, \"line\": {\"color\": \"rgba(50, 171, 96, 0.3)\"}}, {\"type\": \"circle\", \"xref\": \"x\", \"yref\": \"y\", \"x0\": 6.0, \"x1\": 10.0, \"y0\": 87.0, \"y1\": 91.0, \"line\": {\"color\": \"rgba(50, 171, 96, 0.3)\"}}, {\"type\": \"circle\", \"xref\": \"x\", \"yref\": \"y\", \"x0\": 6.0, \"x1\": 10.0, \"y0\": 88.0, \"y1\": 92.0, \"line\": {\"color\": \"rgba(50, 171, 96, 0.3)\"}}, {\"type\": \"circle\", \"xref\": \"x\", \"yref\": \"y\", \"x0\": 6.0, \"x1\": 10.0, \"y0\": 89.0, \"y1\": 93.0, \"line\": {\"color\": \"rgba(50, 171, 96, 0.3)\"}}, {\"type\": \"circle\", \"xref\": \"x\", \"yref\": \"y\", \"x0\": 6.0, \"x1\": 10.0, \"y0\": 90.0, \"y1\": 94.0, \"line\": {\"color\": \"rgba(50, 171, 96, 0.3)\"}}, {\"type\": \"circle\", \"xref\": \"x\", \"yref\": \"y\", \"x0\": 6.0, \"x1\": 10.0, \"y0\": 91.0, \"y1\": 95.0, \"line\": {\"color\": \"rgba(50, 171, 96, 0.3)\"}}, {\"type\": \"circle\", \"xref\": \"x\", \"yref\": \"y\", \"x0\": 6.0, \"x1\": 10.0, \"y0\": 92.0, \"y1\": 96.0, \"line\": {\"color\": \"rgba(50, 171, 96, 0.3)\"}}, {\"type\": \"circle\", \"xref\": \"x\", \"yref\": \"y\", \"x0\": 6.0, \"x1\": 10.0, \"y0\": 93.0, \"y1\": 97.0, \"line\": {\"color\": \"rgba(50, 171, 96, 0.3)\"}}, {\"type\": \"circle\", \"xref\": \"x\", \"yref\": \"y\", \"x0\": 6.0, \"x1\": 10.0, \"y0\": 94.0, \"y1\": 98.0, \"line\": {\"color\": \"rgba(50, 171, 96, 0.3)\"}}, {\"type\": \"circle\", \"xref\": \"x\", \"yref\": \"y\", \"x0\": 6.0, \"x1\": 10.0, \"y0\": 95.0, \"y1\": 99.0, \"line\": {\"color\": \"rgba(50, 171, 96, 0.3)\"}}, {\"type\": \"circle\", \"xref\": \"x\", \"yref\": \"y\", \"x0\": 6.0, \"x1\": 10.0, \"y0\": 96.0, \"y1\": 100.0, \"line\": {\"color\": \"rgba(50, 171, 96, 0.3)\"}}, {\"type\": \"circle\", \"xref\": \"x\", \"yref\": \"y\", \"x0\": 6.0, \"x1\": 10.0, \"y0\": 97.0, \"y1\": 101.0, \"line\": {\"color\": \"rgba(50, 171, 96, 0.3)\"}}, {\"type\": \"circle\", \"xref\": \"x\", \"yref\": \"y\", \"x0\": 6.0, \"x1\": 10.0, \"y0\": 98.0, \"y1\": 102.0, \"line\": {\"color\": \"rgba(50, 171, 96, 0.3)\"}}]}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# Plot them\n", | |
"import plotly.offline as py\n", | |
"import plotly.graph_objs as go\n", | |
"import numpy as np\n", | |
"\n", | |
"circle_r1 = [{\n", | |
" \"type\": \"circle\",\n", | |
" \"xref\": \"x\",\n", | |
" \"yref\": \"y\",\n", | |
" \"x0\": px - 1.0,\n", | |
" \"x1\": px + 1.0,\n", | |
" \"y0\": py - 1.0,\n", | |
" \"y1\": py + 1.0,\n", | |
" \"line\": {\"color\": \"rgba(50, 171, 96, 0.3)\"}\n", | |
"} for px, py in points]\n", | |
"\n", | |
"circle_r2 = [{\n", | |
" \"type\": \"circle\",\n", | |
" \"xref\": \"x\",\n", | |
" \"yref\": \"y\",\n", | |
" \"x0\": px - 2.0,\n", | |
" \"x1\": px + 2.0,\n", | |
" \"y0\": py - 2.0,\n", | |
" \"y1\": py + 2.0,\n", | |
" \"line\": {\"color\": \"rgba(50, 171, 96, 0.3)\"}\n", | |
"} for px, py in points]\n", | |
"\n", | |
"curve_x = np.linspace(-1, 11, 200)\n", | |
"curve_y = np.power(2, curve_x)\n", | |
"\n", | |
"data = [go.Scatter(x=curve_x, y=curve_y)]\n", | |
"layout = {\n", | |
" \"height\": 3000,\n", | |
" \"xaxis\": {\n", | |
" \"range\": [-1, 11]\n", | |
" },\n", | |
" \"yaxis\": {\n", | |
" \"scaleanchor\": \"x\",\n", | |
" \"range\": [-1, 101]\n", | |
" },\n", | |
" \"shapes\": [\n", | |
" *circle_r1,\n", | |
" *circle_r2\n", | |
" ]\n", | |
"}\n", | |
"\n", | |
"py.iplot({\n", | |
" \"data\": data,\n", | |
" \"layout\": layout\n", | |
"}, filename=\"result\")" | |
] | |
}, | |
{ | |
"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": "3.6.1" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 2 | |
} |
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