chapter01.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第一讲：方程组的几何解释\n",
    "\n",
    "我们从求解线性方程组来开始这门课，从一个普通的例子讲起：方程组有$2$个未知数，一共有$2$个方程，分别来看方程组的“行图像”和“列图像”。\n",
    "\n",
    "有方程组$\\begin{cases}2x&-y&=0\\\\-x&+2y&=3\\end{cases}$，写作矩阵形式有$\\begin{bmatrix}2&-1\\\\-1&2\\end{bmatrix}\\begin{bmatrix}x\\\\y\\end{bmatrix}=\\begin{bmatrix}0\\\\3\\end{bmatrix}$，通常我们把第一个矩阵称为系数矩阵$A$，将第二个矩阵称为向量$x$，将第三个矩阵称为向量$b$，于是线性方程组可以表示为$Ax=b$。\n",
    "\n",
    "我们来看行图像，即直角坐标系中的图像："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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soQU66+SfvExvXYjlpr0bjGCw/ZckL62vORzRy+s/09t/3a1gMKB53zpfc24Y6+lKTS/t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\nOMj/B7jePUnkGW/XAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104d91a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "x = [-2, 2, -2, 2]\n",
    "y = [-4, 4, 0.5, 2.5]\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.axhline(y=0, c='black')\n",
    "plt.axvline(x=0, c='black')\n",
    "\n",
    "plt.plot(x[:2], y[:2], x[2:], y[2:])\n",
    "\n",
    "plt.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上图是我们都很熟悉的直角坐标系中两直线相交的情况，接下来我们按列观察方程组$x\\begin{bmatrix}2\\\\-1\\end{bmatrix}+y\\begin{bmatrix}-1\\\\2\\end{bmatrix}=\\begin{bmatrix}0\\\\3\\end{bmatrix}$（我们把第一个向量称作$col_1$，第二个向量称作$col_2$，以表示第一列向量和第二列向量），要使得式子成立，需要第一个向量加上两倍的第二个向量，即$1\\begin{bmatrix}2\\\\-1\\end{bmatrix}+2\\begin{bmatrix}-1\\\\2\\end{bmatrix}=\\begin{bmatrix}0\\\\3\\end{bmatrix}$。\n",
    "\n",
    "现在来看列图像，在二维平面上画出上面的列向量："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uGCw5pxmNExIZsOdLSvVmnwAuF6xY4cVmg2HDXHg8Vk8ksU7xlbhjNwyebJxLbmIiAwp3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\n8K2eAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11909a320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from functools import partial\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.axhline(y=0, c='black')\n",
    "plt.axvline(x=0, c='black')\n",
    "ax = plt.gca()\n",
    "ax.set_xlim(-2.5, 2.5)\n",
    "ax.set_ylim(-3, 4)\n",
    "\n",
    "arrow_vector = partial(plt.arrow, width=0.01, head_width=0.1, head_length=0.2, length_includes_head=True)\n",
    "\n",
    "arrow_vector(0, 0, 2, -1, color='g')\n",
    "arrow_vector(0, 0, -1, 2, color='c')\n",
    "arrow_vector(2, -1, -2, 4, color='b')\n",
    "arrow_vector(0, 0, 0, 3, width=0.05, color='r')\n",
    "\n",
    "plt.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
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    "如图，绿向量$col_1$与蓝向量（两倍的蓝绿向量$col_2$）合成红向量$b$。\n",
    "\n",
    "接着，我们继续观察$x\\begin{bmatrix}2\\\\-1\\end{bmatrix}+y\\begin{bmatrix}-1\\\\2\\end{bmatrix}=\\begin{bmatrix}0\\\\3\\end{bmatrix}$，$col_1,col_2$的某种线性组合得到了向量$b$，那么$col_1,col_2$的所有线性组合能够得到什么结果？它们将铺满整个平面。\n",
    "\n",
    "下面进入三个未知数的方程组：$\\begin{cases}2x&-y&&=0\\\\-x&+2y&-z&=-1\\\\&-3y&+4z&=4\\end{cases}$，写作矩阵形式$A=\\begin{bmatrix}2&-1&0\\\\-1&2&-1\\\\0&-3&4\\end{bmatrix},\\ b=\\begin{bmatrix}0\\\\-1\\\\4\\end{bmatrix}$。\n",
    "\n",
    "在三维直角坐标系中，每一个方程将确定一个平面，而例子中的三个平面会相交于一点，这个点就是方程组的解。\n",
    "\n",
    "同样的，将方程组写成列向量的线性组合，观察列图像：$x\\begin{bmatrix}2\\\\-1\\\\0\\end{bmatrix}+y\\begin{bmatrix}-1\\\\2\\\\-3\\end{bmatrix}+z\\begin{bmatrix}0\\\\-1\\\\4\\end{bmatrix}=\\begin{bmatrix}0\\\\-1\\\\4\\end{bmatrix}$。易知教授特意安排的例子中最后一个列向量恰巧等于等式右边的$b$向量，所以我们需要的线性组合为$x=0,y=0,z=1$。假设我们令$b=\\begin{bmatrix}1\\\\1\\\\-3\\end{bmatrix}$，则需要的线性组合为$x=1,y=1,z=0$。\n",
    "\n",
    "我们并不能总是这么轻易的求出正确的线性组合，所以下一讲将介绍消元法——一种线性方程组的系统性解法。\n",
    "\n",
    "现在，我们需要考虑，对于任意的$b$，是否都能求解$Ax=b$？用列向量线性组合的观点阐述就是，列向量的线性组合能否覆盖整个三维向量空间？对上面这个例子，答案是肯定的，这个例子中的$A$是我们喜欢的矩阵类型，但是对另一些矩阵，答案是否定的。那么在什么情况下，三个向量的线性组合得不到$b$？\n",
    "\n",
    "——如果三个向量在同一个平面上，问题就出现了——那么他们的线性组合也一定都在这个平面上。举个例子，比如$col_3=col_1+col_2$，那么不管怎么组合，这三个向量的结果都逃不出这个平面，因此当$b$在平面内，方程组有解，而当$b$不在平面内，这三个列向量就无法构造出$b$。在后面的课程中，我们会了解到这种情形称为**奇异**、**矩阵不可逆**。\n",
    "\n",
    "下面我们推广到九维空间，每个方程有九个未知数，共九个方程，此时已经无法从坐标图像中描述问题了，但是我们依然可以从求九维列向量线性组合的角度解决问题，仍然是上面的问题，是否总能得到$b$？当然这仍取决于这九个向量，如果我们取一些并不相互独立的向量，则答案是否定的，比如取了九列但其实只相当于八列，有一列毫无贡献（这一列是前面列的某种线性组合），则会有一部分$b$无法求得。\n",
    "\n",
    "接下来介绍方程的矩阵形式$Ax=b$，这是一种乘法运算，举个例子，取$A=\\begin{bmatrix}2&5\\\\1&3\\end{bmatrix},\\ x=\\begin{bmatrix}1\\\\2\\end{bmatrix}$，来看如何计算矩阵乘以向量：\n",
    "\n",
    "* 我们依然使用列向量线性组合的方式，一次计算一列，$\\begin{bmatrix}2&5\\\\1&3\\end{bmatrix}\\begin{bmatrix}1\\\\2\\end{bmatrix}=1\\begin{bmatrix}2\\\\1\\end{bmatrix}+2\\begin{bmatrix}5\\\\3\\end{bmatrix}=\\begin{bmatrix}12\\\\7\\end{bmatrix}$\n",
    "* 另一种方法，使用向量内积，矩阵第一行向量点乘$x$向量$\\begin{bmatrix}2&5\\end{bmatrix}\\cdot\\begin{bmatrix}1&2\\end{bmatrix}^T=12,\\ \\begin{bmatrix}1&3\\end{bmatrix}\\cdot\\begin{bmatrix}1&2\\end{bmatrix}^T=7$。\n",
    "\n",
    "教授建议使用第一种方法，将$Ax$看做$A$列向量的线性组合。"
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