{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br />\n",
    "\n",
    "<div style=\"text-align: center;\">\n",
    "<font size=\"7\">数値計算試験問題</font>\n",
    "</div>\n",
    "<br />\n",
    "<div style=\"text-align: right;\">\n",
    "<font size=\"4\">2023/07/12 実施</font>\n",
    "<br />\n",
    "<font size=\"4\">cc by Shigeto R. Nishitani 2023 </font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # 1 簡単な行列計算:25点 \n",
    " \n",
    " 次の行列\n",
    "  $\n",
    "  A = \\left(\\begin{array}{ccc}\n",
    "    4 & 5 & 5 \\\\\n",
    "    -4 & -5 & -7 \\\\\n",
    "    4 & 4 & 6\n",
    "  \\end{array}\n",
    "  \\right)\n",
    "  $\n",
    "  の固有値と固有ベクトルを求めよ．\n",
    "  \n",
    "  また，固有ベクトルで構成される対角化行列$P$ を用いて，ドット演算 により$P^{-1}.A.P$が対角化されることを確かめよ．\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 4  5  5]\n",
      " [-4 -5 -7]\n",
      " [ 4  4  6]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.set_printoptions(formatter={'float': '{: 0.3f}'.format}) \n",
    "\n",
    "aa = np.array([[4,5,5],[-4,-5,-7],[4,4,6]])\n",
    "print(aa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 4.+0.j  2.+0.j -1.+0.j]\n",
      "[[-0.333  0.000 -0.707]\n",
      " [ 0.667  0.707  0.707]\n",
      " [-0.667 -0.707  0.000]]\n"
     ]
    }
   ],
   "source": [
    "import scipy.linalg as linalg\n",
    "l, PP = linalg.eig(aa)\n",
    "print(l)\n",
    "print(PP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4.000,  0.000,  0.000],\n",
       "       [ 0.000,  2.000, -0.000],\n",
       "       [ 0.000,  0.000, -1.000]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linalg.inv(PP).dot(aa).dot(PP)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 ニュートンの差分商補間:25点\n",
    "\n",
    "三次関数$x^3$の$x=0.75$における値$F(0.75)$をニュートンの差分商補間を用いて求める．\n",
    "ニュートンの内挿公式は，\n",
    "$$\n",
    "\\begin{array}{rc}\n",
    "F (x )&=F (x _{0})+\n",
    "(x -x _{0})f _{1}\\lfloor x_0,x_1\\rfloor+\n",
    "(x -x _{0})(x -x _{1})\n",
    "f _{2}\\lfloor x_0,x_1,x_2\\rfloor + \\\\\n",
    "& \\cdots +  \\prod_{i=0}^{n-1} (x-x_i) \\, \n",
    "f_n \\lfloor x_0,x_1,\\cdots,x_n \\rfloor\n",
    "\\end{array}\n",
    "$$\n",
    "である．ここで$f_i \\lfloor\\, \\rfloor$ は次のような関数を意味していて，\n",
    "$$\n",
    "\\begin{array}{rc}\n",
    "f _{1}\\lfloor x_0,x_1\\rfloor &=&  \\frac{y_1-y_0}{x_1-x_0} \\\\\n",
    "f _{2}\\lfloor x_0,x_1,x_2\\rfloor &=&  \\frac{f _{1}\\lfloor x_1,x_2\\rfloor-\n",
    "f _{1}\\lfloor x_0,x_1\\rfloor}{x_2-x_0} \\\\\n",
    "\\vdots & \\\\\n",
    "f _{n}\\lfloor x_0,x_1,\\cdots,x_n\\rfloor &=&  \\frac{f_{n-1}\\lfloor x_1,x_2\\cdots,x_{n}\\rfloor-\n",
    "f _{n-1}\\lfloor x_0,x_1,\\cdots,x_{n-1}\\rfloor}{x_n-x_0} \n",
    "\\end{array}\n",
    "$$\n",
    "差分商と呼ばれる．$x_k=-1,0,1,2$をそれぞれ選ぶと，差分商補間のそれぞれの項は以下の通りとなる．\n",
    "\n",
    "$$\n",
    "\\begin{array}{ccl|lll}\n",
    "\\hline\n",
    "k  &  x_k & y_k=F_0( x_k) &f_1\\lfloor x_k,x_{k+1}\\rfloor & f_2\\lfloor x_k,x_{k+1},x_{k+2}\\rfloor &  f_3\\lfloor x_k,x_{k+1},x_{k+2},x_{k+3}\\rfloor \\\\\n",
    "\\hline\n",
    "0  &   -1.0  &  -1.0  &          &              &\\\\\n",
    "&      &     &     1.0  &              &\\\\ \n",
    "1  &   0.0  &  0.0  &           &    [ XXX ]     &\\\\\n",
    "&      &     &     1.0    &              & 1.0 \\\\\n",
    "2  &  1.0  &  1.0  &           &     3.0   &\\\\ \n",
    "&      &     &     7.0   &              &\\\\ \n",
    "3  &  2.0  & 8.0 &           &              &\\\\ \n",
    "\\hline\n",
    "\\end{array}\n",
    "$$\n",
    "それぞれの項は，例えば，\n",
    "\n",
    "$$\n",
    "f_1\\lfloor x_0,x_1 \\rfloor =\\frac{0.0-(-1.0)}{0.0-(-1.0)}=1.0\n",
    "$$\n",
    "で求められる．ニュートンの差分商の一次多項式の値はx=0.75で\n",
    "\n",
    "\\begin{align}\n",
    "F(x)&=F_0(-1.0)+(x-x_0)f_1\\lfloor x_0,x_1\\rfloor  \\\\\n",
    "& =-1.0+\\left(0.75-(-1.0)\\right)\\times(1.0)\\\\\n",
    "& =0.75\n",
    "\\end{align}\n",
    "となる．\n",
    "\n",
    "## (1) 差分商補間の表中の開いている箇所[ XXX ]を埋めよ．\n",
    "## (2)  ニュートンの二次多項式\n",
    "\n",
    "$$\n",
    "F (x )=F (x _{0})+(x -x _{0})f _{1}\\lfloor x_0,x_1\\rfloor+(x -x _{0})(x -x _{1})\n",
    "f _{2}\\lfloor x_0,x_1,x_2\\rfloor\n",
    "$$\n",
    "の値を求めよ．\n",
    "## (3) ニュートンの三次多項式の値を求めよ．\n",
    "(児玉鹿三著「理工系基礎数学解析-I」(槙書店,　1967), p.294)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.0 0.0 1.0 8.0\n",
      "1.0 1.0\n",
      "f1: 0.75\n",
      "f2_012: 0.0\n",
      "f2:  0.75\n",
      "f3: 0.421875\n",
      "0.421875\n"
     ]
    }
   ],
   "source": [
    "def func(x):\n",
    "    return x**3\n",
    "\n",
    "x0,x1,x2,x3=-1.0,0.0,1.0,2.0\n",
    "y0,y1,y2,y3=func(x0),func(x1),func(x2),func(x3)\n",
    "print(y0,y1,y2,y3)\n",
    "f1_12=(y2-y1)/(x2-x1)\n",
    "f1_01=(y1-y0)/(x1-x0)\n",
    "f1_23=(y3-y2)/(x3-x2)\n",
    "print(f1_12,f1_01)\n",
    "\n",
    "#(0) f1 = f0+(x-x0)*f1_01\n",
    "x=0.75\n",
    "f1 = y0+(x-x0)*f1_01\n",
    "print('f1:', f1)\n",
    "# (1) f2_012 = (f1_12-f1_01)/(x2-x0)\n",
    "f2_012=(f1_12-f1_01)/(x2-x0)\n",
    "print('f2_012:',f2_012)\n",
    "                    \n",
    "# (2) \n",
    "f0=y0\n",
    "\n",
    "f2 = f0+(x-x0)*f1_01+(x-x0)*(x-x1)*f2_012\n",
    "print('f2: ',f2)\n",
    "# (3)\n",
    "f3_0123 =1.0\n",
    "f3 = f0+(x-x0)*f1_01+(x-x0)*(x-x1)*f2_012 + +(x-x0)*(x-x1)*(x-x2)*f3_0123\n",
    "print('f3:' , f3)\n",
    "print(func(0.75))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 Gauss-Seidelの収束性:25点\n",
    "\n",
    "初期値を$[0,0,0]^{t}$として，\n",
    "$A(tt)x=b$ \n",
    "にガウス・ザイデルによる連立一次方程式の反復解法プログラムを適用する．\n",
    "ただし，\n",
    "\\begin{equation}\n",
    "A(tt)=\n",
    "\\left(\n",
    "\\begin{array}{ccc}\n",
    "1&tt&tt \\\\\n",
    "tt&1&tt \\\\\n",
    "tt&tt&1\n",
    "\\end{array}\n",
    "\\right)\n",
    ", \\, \n",
    "b=\n",
    "\\left(\n",
    "\\begin{array}{c}\n",
    "2 \\\\\n",
    "2 \\\\\n",
    "2 \\\\\n",
    "\\end{array}\n",
    "\\right)\n",
    "\\end{equation}\n",
    "\n",
    "である．\n",
    "$tt=0.2,0.5,0.7$ に対して有効数字6桁の解を得るための反復回数を求めよ．\n",
    "\n",
    "(E.クライツィグ著「数値解析」(培風館,2003), p.89, 問題2.3-9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 [2.   1.6  1.28]\n",
      "1 [1.424   1.4592  1.42336]\n",
      "2 [1.423488 1.43063  1.429176]\n",
      "3 [1.428039 1.428557 1.428681]\n",
      "4 [1.428552 1.428553 1.428579]\n",
      "5 [1.428574 1.42857  1.428571]\n",
      "6 [1.428572 1.428571 1.428571]\n",
      "7 [1.428571 1.428571 1.428571]\n",
      "8 [1.428571 1.428571 1.428571]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.set_printoptions(precision=6, suppress=True)\n",
    "\n",
    "tt=0.2\n",
    "A=np.array([[1,tt,tt],[tt,1,tt],[tt,tt,1]])\n",
    "b=np.array([2,2,2])\n",
    "n=3\n",
    "x0=np.zeros(n)\n",
    "x1=np.zeros(n)\n",
    "\n",
    "for iter in range(0, 9):\n",
    "    for i in range(0, n):\n",
    "        x1[i]=b[i]\n",
    "        for j in range(0, n):\n",
    "            x1[i]=x1[i]-A[i][j]*x0[j]\n",
    "        x1[i]=x1[i]+A[i][i]*x0[i]\n",
    "        x1[i]=x1[i]/A[i][i]\n",
    "        x0[i]=x1[i]\n",
    "    print(iter,x0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 [2.  1.  0.5]\n",
      "1 [1.25   1.125  0.8125]\n",
      "2 [1.03125  1.078125 0.945312]\n",
      "3 [0.988281 1.033203 0.989258]\n",
      "4 [0.98877  1.010986 1.000122]\n",
      "5 [0.994446 1.002716 1.001419]\n",
      "6 [0.997932 1.000324 1.000872]\n",
      "7 [0.999402 0.999863 1.000367]\n",
      "8 [0.999885 0.999874 1.000121]\n",
      "9 [1.000003 0.999938 1.000029]\n",
      "10 [1.000016 0.999977 1.000003]\n",
      "11 [1.00001  0.999993 0.999998]\n",
      "12 [1.000004 0.999999 0.999999]\n",
      "13 [1.000001 1.       0.999999]\n",
      "14 [1. 1. 1.]\n",
      "15 [1. 1. 1.]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.set_printoptions(precision=6, suppress=True)\n",
    "\n",
    "tt=0.5\n",
    "A=np.array([[1,tt,tt],[tt,1,tt],[tt,tt,1]])\n",
    "b=np.array([2,2,2])\n",
    "n=3\n",
    "x0=np.zeros(n)\n",
    "x1=np.zeros(n)\n",
    "\n",
    "for iter in range(0, 16):\n",
    "    for i in range(0, n):\n",
    "        x1[i]=b[i]\n",
    "        for j in range(0, n):\n",
    "            x1[i]=x1[i]-A[i][j]*x0[j]\n",
    "        x1[i]=x1[i]+A[i][i]*x0[i]\n",
    "        x1[i]=x1[i]/A[i][i]\n",
    "        x0[i]=x1[i]\n",
    "    print(iter,x0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 [2.   0.6  0.18]\n",
      "1 [1.454   0.8562  0.38286]\n",
      "2 [1.132658 0.939137 0.549743]\n",
      "3 [0.957784 0.944731 0.66824 ]\n",
      "4 [0.87092  0.922588 0.744544]\n",
      "5 [0.833008 0.895714 0.789895]\n",
      "6 [0.820074 0.873022 0.814833]\n",
      "7 [0.818502 0.856666 0.827383]\n",
      "8 [0.821166 0.846016 0.832973]\n",
      "9 [0.824708 0.839623 0.834968]\n",
      "10 [0.827786 0.836072 0.835299]\n",
      "11 [0.83004  0.834263 0.834988]\n",
      "12 [0.831524 0.833441 0.834524]\n",
      "13 [0.832424 0.833136 0.834108]\n",
      "14 [0.832929 0.833074 0.833798]\n",
      "15 [0.83319  0.833109 0.833591]\n",
      "16 [0.83331  0.833169 0.833464]\n",
      "17 [0.833356 0.833225 0.833393]\n",
      "18 [0.833367 0.833268 0.833355]\n",
      "19 [0.833364 0.833297 0.833338]\n",
      "20 [0.833356 0.833314 0.833331]\n",
      "21 [0.833348 0.833325 0.833329]\n",
      "22 [0.833343 0.83333  0.833329]\n",
      "23 [0.833339 0.833333 0.83333 ]\n",
      "24 [0.833336 0.833334 0.833331]\n",
      "25 [0.833335 0.833334 0.833332]\n",
      "26 [0.833334 0.833334 0.833333]\n",
      "27 [0.833333 0.833334 0.833333]\n",
      "28 [0.833333 0.833334 0.833333]\n",
      "29 [0.833333 0.833334 0.833333]\n",
      "30 [0.833333 0.833333 0.833333]\n",
      "31 [0.833333 0.833333 0.833333]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.set_printoptions(precision=6, suppress=True)\n",
    "\n",
    "tt=0.7\n",
    "A=np.array([[1,tt,tt],[tt,1,tt],[tt,tt,1]])\n",
    "b=np.array([2,2,2])\n",
    "n=3\n",
    "x0=np.zeros(n)\n",
    "x1=np.zeros(n)\n",
    "\n",
    "for iter in range(0, 32):\n",
    "    for i in range(0, n):\n",
    "        x1[i]=b[i]\n",
    "        for j in range(0, n):\n",
    "            x1[i]=x1[i]-A[i][j]*x0[j]\n",
    "        x1[i]=x1[i]+A[i][i]*x0[i]\n",
    "        x1[i]=x1[i]/A[i][i]\n",
    "        x0[i]=x1[i]\n",
    "    print(iter,x0)"
   ]
  },
  {
   "attachments": {
    "Link3.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 ページランク:25点\n",
    "\n",
    "次のようなリンクが張られたページ群のページランクを求めよ．\n",
    "\n",
    "![Link3.png](attachment:Link3.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.000  0.500  0.000  0.000  0.500]\n",
      " [ 0.333  0.000  0.000  0.000  0.500]\n",
      " [ 0.333  0.500  0.000  0.500  0.000]\n",
      " [ 0.333  0.000  1.000  0.000  0.000]\n",
      " [ 0.000  0.000  0.000  0.500  0.000]]\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "from numpy import array, zeros, diagflat, dot, transpose, set_printoptions\n",
    "from scipy.linalg import eig\n",
    "\n",
    "A = array([[0,1,1,1,0],\n",
    "           [1,0,1,0,0],\n",
    "           [0,0,0,1,0],\n",
    "           [0,0,1,0,1],\n",
    "           [1,1,0,0,0]])\n",
    "\n",
    "n = 5\n",
    "diag = []\n",
    "for i in range(0,n):\n",
    "    tmp = 0.0\n",
    "    for j in range(0,n):\n",
    "        tmp += A[i,j]\n",
    "    diag.append(1.0/tmp)\n",
    "\n",
    "D = diagflat(diag)\n",
    "tA = dot(transpose(A),D)\n",
    "\n",
    "set_printoptions(formatter={'float': '{: 0.3f}'.format}) \n",
    "print(tA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "初期ベクトルを等分の値にして，3度ほどホップさせた結果．"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array([ 0.125,  0.111,  0.269,  0.328,  0.167])\n"
     ]
    }
   ],
   "source": [
    "x = array([1/5,1/5,1/5,1/5,1/5])\n",
    "pprint(dot(tA,dot(tA,dot(tA,x))))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "固有値を求める．固有値がソートされているか自信がないので，表示させてみた．"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array([ 1.      +0.j      ,  0.102423+0.501154j,  0.102423-0.501154j,\n",
      "       -0.813177+0.j      , -0.391669+0.j      ])\n"
     ]
    }
   ],
   "source": [
    "l, V = eig(tA)\n",
    "pprint(l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "l[0]に対応する最大固有値のベクトルを取り出す．\n",
    "さらに，初期ベクトルからのホップと比較するために値を揃えている．\n",
    "順序は一致しているが，ホップ数が少ないので数値の一致はそれほど高くない．"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array([-0.294489+0.j, -0.261768+0.j, -0.556257+0.j, -0.65442 +0.j,\n",
      "       -0.32721 +0.j])\n",
      "array([0.125   +0.j, 0.111111+0.j, 0.236111+0.j, 0.277778+0.j,\n",
      "       0.138889+0.j])\n"
     ]
    }
   ],
   "source": [
    "v0 = V[:,0]\n",
    "pprint(v0)\n",
    "pprint(v0/v0[0]*0.125)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "id=４(３番目）が一番ランクが高いので，それで規格化すると数字が読みやすい．"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array([0.45-0.j, 0.4 -0.j, 0.85-0.j, 1.  -0.j, 0.5 -0.j])\n"
     ]
    }
   ],
   "source": [
    "pprint(v0/v0[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "これより，ページランクは，\n",
    "[4, 5, 2, 1, 3]\n",
    "の順になる．"
   ]
  }
 ],
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