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cc/D1r8PYsbBkCUyfHq5S9R//EZ5PQtFP2ny2RTmjlZacxVCPXyLT0BCOsHnt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\nAElFTkSuQmCC\n", "output_type": "display_data", "metadata": {}, "text": [""]}], "collapsed": false, "input": ["a = 2\n", "b = 6\n", "\n", "x = np.linspace(a, b)\n", "\n", "def f(x):\n", " return 1e-2 * np.exp(x) - 2\n", "\n", "pt.grid()\n", "pt.plot(x, f(x))"], "prompt_number": 2, "metadata": {}}, {"cell_type": "markdown", "metadata": {}, "source": ["Write code for the bisection method and run it in-place many times: (Ctrl-Enter)"]}, {"language": "python", "cell_type": "code", "outputs": [{"output_type": "stream", "text": ["5.298316955566406 5.2983174324035645\n"], "stream": "stdout"}], "collapsed": false, "input": ["m = (a+b)/2\n", "\n", "if np.sign(f(a)) == np.sign(f(m)):\n", " a = m\n", "else:\n", " b = m\n", " \n", "print(a, b)"], "prompt_number": 25, "metadata": {}}]}], "nbformat": 3, "nbformat_minor": 0, "metadata": {"signature": "sha256:82b1b00a4eb81202819ba452817bf1331088ef27c1cb8c9ca835896491b0308d", "name": ""}}