{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Set $f(x) = 54 x^6 + 45 x^5 − 102 x^4 − 69 x^3 + 35 x^2 + 16 x − 4$. \n", "\n", "Plot the function on the interval [−2,2], and use the Secant Method to find all five roots in the interval.\n", "\n", "To which of the roots is the convergence linear, and to which is it superlinear?" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from math import pow as Pow\n", "def y(x):\n", " return 54 * Pow(x, 6) + 45 * Pow(x, 5) - 102 * Pow(x, 4) - 69 * Pow(x, 3) + 35 * Pow(x, 2) + 16 * x - 4" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "x = np.arange(-2, 2, 0.001)\n", "y = [y(a) for a in x]\n", "\n", "plt.plot(x,y)\n", "plt.ylim([-0.5,0.5])\n", "plt.xlim([-2, 2])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "matrix([[ 29, 34, 39],\n", " [ 62, 76, 90],\n", " [ 95, 118, 141]])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m = np.matrix(np.arange(1, 10).reshape(3,3))\n", "m * m - m" ] } ], "metadata": { "kernelspec": { "display_name": "base", "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": "3.9.13" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }