{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Convergence of Newton's Method"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as pt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "def f(x):\n",
        "    return np.exp(x) - 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f44dd2925c0>]"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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JhEWL/J106uvhJz/RJWFzoeuBJ0zb/LfSqRZBudcik4G6OrjyShgzpuPmXe61KDY1cBEp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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f44dd292588>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "xgrid = np.linspace(-2, 3, 1000)\n",
        "pt.grid()\n",
        "pt.plot(xgrid, f(xgrid))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "What's the true solution of $f(x)=0$?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.69314718056\n",
            "0.0\n"
          ]
        }
      ],
      "source": [
        "xtrue = np.log(2)\n",
        "print(xtrue)\n",
        "print(f(xtrue))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now let's run Newton's method and keep track of the errors:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "errors = []\n",
        "x = 2\n",
        "xbefore = 3"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "At each iteration, print the current guess and the error."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "nan\n",
            "nan\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "-c:1: RuntimeWarning: invalid value encountered in double_scalars\n"
          ]
        }
      ],
      "source": [
        "slope = (f(x)-f(xbefore))/(x-xbefore)\n",
        "\n",
        "xbefore = x\n",
        "x = x - f(x)/slope\n",
        "print(x)\n",
        "errors.append(abs(x-xtrue))\n",
        "print(errors[-1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.882400077493\n",
            "0.411823511031\n",
            "0.147482044876\n",
            "0.0276859623403\n",
            "0.00198268429064\n",
            "2.73106724006e-05\n",
            "2.70651508982e-08\n",
            "3.69593244898e-13\n",
            "1.11022302463e-16\n",
            "1.11022302463e-16\n",
            "nan\n"
          ]
        }
      ],
      "source": [
        "for err in errors:\n",
        "    print(err)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "* Do you have a hypothesis about the order of convergence?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Does not quite double the number of digits each round--unclear."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "------------\n",
        "Let's check:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.504224909965\n",
            "0.619635842142\n",
            "0.612688428557\n",
            "0.657169643929\n",
            "0.644727394358\n",
            "0.655276572424\n",
            "0.648759771781\n",
            "14482.1405299\n",
            "7243300082.99\n",
            "nan\n"
          ]
        }
      ],
      "source": [
        "for i in range(len(errors)-1):\n",
        "    print(errors[i+1]/errors[i]**1.618)"
      ]
    }
  ],
  "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 0
}