Tarea17.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# n = variables\n",
    "# m = restricciones\n",
    "def barreraLog(x0, A, b, mu, tol=1e-6):\n",
    "    k = 0\n",
    "    xk = x0\n",
    "    T = 1e-1\n",
    "    print(\"k, x, mu\")\n",
    "    while k < 1000:\n",
    "        c, c1, c2 = calcC(A, xk, b)\n",
    "        # print(f\"c: {c}\")\n",
    "        grad = g(xk) - A.T.dot(c1)*mu\n",
    "        # print(grad)\n",
    "        hess = H(xk) + A.T.dot(c2).dot(A)*mu\n",
    "        # print(hess)\n",
    "        xk = xk - np.dot(grad, np.linalg.inv(hess))\n",
    "        print(f\"{k}, {xk}, {round(mu,6)}\")\n",
    "        k = k + 1\n",
    "        if (np.linalg.norm(grad) < T):\n",
    "            mu /= 1.5\n",
    "        if(mu < tol):\n",
    "            break\n",
    "    return xk\n",
    "            \n",
    "\n",
    "\n",
    "def calcC(A, x, b):\n",
    "    m = A.shape[0]\n",
    "    n = x.shape[0]\n",
    "    C = A.dot(x)-b\n",
    "    C1 = np.zeros(m)\n",
    "    C2 = np.zeros((m, m))\n",
    "    for i in range(0, m):\n",
    "        val = 1/C[i]\n",
    "        C1[i] = val\n",
    "        C2[i, i] = val*val\n",
    "    return C, C1, C2\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EJ 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def f(x):\n",
    "#     return x\n",
    "\n",
    "\n",
    "# def g(x):\n",
    "#     return np.array([\n",
    "#         1\n",
    "#     ])\n",
    "\n",
    "\n",
    "# def H(x):\n",
    "#     return np.array([\n",
    "#         0\n",
    "#     ])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A = np.array([\n",
    "#     [1],\n",
    "#     [-1]\n",
    "# ])\n",
    "# b = np.array([0, -1])\n",
    "# x = np.array([0.9])\n",
    "# mu = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# barreraLog(x,A, b, mu = mu, tol=1e-20)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EJ 2 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def f(x):\n",
    "#     return (x[0]-2)**2 + (x[1]-2)**2\n",
    "\n",
    "\n",
    "# def g(x):\n",
    "#     return np.array([\n",
    "#         2*(x[0]-2),\n",
    "#         2*(x[1]-2),\n",
    "#     ])\n",
    "\n",
    "\n",
    "# def H(x):\n",
    "#     return np.array([\n",
    "#         [2, 0],\n",
    "#         [0, 2]\n",
    "#     ])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A = np.array([\n",
    "#     [1, 1],\n",
    "#     [2, -1]\n",
    "# ])\n",
    "# b = np.array([4, 0])\n",
    "# x = np.array([2, 0])\n",
    "\n",
    "\n",
    "# barreraLog(x,A, b, mu = 100, tol=1e-20)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ej3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k, x, mu\n",
      "0, [0.999998 0.999998 0.999998 0.999998], 100\n",
      "1, [0.999996 0.999996 0.999996 0.999996], 100\n",
      "2, [0.999992 0.999992 0.999992 0.999992], 100\n",
      "3, [0.999984 0.999984 0.999984 0.999984], 100\n",
      "4, [0.999968 0.999968 0.999968 0.999968], 100\n",
      "5, [0.999936 0.999936 0.999936 0.999936], 100\n",
      "6, [0.99987201 0.999872   0.999872   0.999872  ], 100\n",
      "7, [0.99974402 0.99974402 0.999744   0.999744  ], 100\n",
      "8, [0.99948809 0.99948806 0.99948798 0.99948801], 100\n",
      "9, [0.99897634 0.99897625 0.99897593 0.99897602], 100\n",
      "10, [0.99795337 0.99795301 0.99795171 0.99795209], 100\n",
      "11, [0.99590948 0.99590806 0.99590288 0.99590438], 100\n",
      "12, [0.99183003 0.99182436 0.99180371 0.99180971], 100\n",
      "13, [0.98370497 0.98368245 0.9836005  0.98362434], 100\n",
      "14, [0.96759482 0.9675059  0.96718334 0.96727734], 100\n",
      "15, [0.93596998 0.93562411 0.93437773 0.93474201], 100\n",
      "16, [0.87537692 0.87407608 0.86947052 0.87082102], 100\n",
      "17, [0.76675648 0.76226748 0.74718924 0.75159325], 100\n",
      "18, [0.60893103 0.59687835 0.56266029 0.57218134], 100\n",
      "19, [0.49472636 0.47826165 0.44741377 0.45471501], 100\n",
      "20, [0.48294187 0.4684532  0.44410787 0.44991629], 100\n",
      "21, [0.48295641 0.46848064 0.44411392 0.44992761], 100\n",
      "22, [0.47552922 0.45422239 0.42175467 0.42885224], 66.666667\n",
      "23, [0.47553271 0.45430013 0.42206357 0.42911355], 66.666667\n",
      "24, [0.47553271 0.45430013 0.42206364 0.4291136 ], 66.666667\n",
      "25, [0.46548187 0.43459098 0.39390938 0.40164114], 44.444444\n",
      "26, [0.46548444 0.4347953  0.39453034 0.40222704], 44.444444\n",
      "27, [0.46548443 0.43479532 0.39453072 0.40222738], 44.444444\n",
      "28, [0.45237458 0.40852352 0.3613042  0.36846198], 29.62963\n",
      "29, [0.4523664  0.40901054 0.36234973 0.36958295], 29.62963\n",
      "30, [0.4523663  0.4090107  0.36235105 0.36958459], 29.62963\n",
      "31, [0.43599085 0.37563886 0.3258401  0.33093176], 19.753086\n",
      "32, [0.43595007 0.37666478 0.32730366 0.33271642], 19.753086\n",
      "33, [0.43594961 0.37666585 0.32730661 0.33272165], 19.753086\n",
      "34, [0.41646039 0.33673342 0.289871   0.29172044], 13.168724\n",
      "35, [0.41635458 0.33859819 0.29160352 0.29407245], 13.168724\n",
      "36, [0.41635313 0.33860328 0.29160808 0.29408344], 13.168724\n",
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      "39, [0.39413123 0.29684646 0.25725425 0.25621786], 8.77915\n",
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      "157, [2.78880222e-01 4.78595284e-06 8.36142058e-02 1.69555065e-02], 2e-05\n",
      "158, [2.78886621e-01 2.39335838e-06 8.36148930e-02 1.69470975e-02], 1.4e-05\n",
      "159, [2.78886427e-01 2.99150146e-06 8.36149510e-02 1.69469380e-02], 1.4e-05\n",
      "160, [2.78886367e-01 3.17834482e-06 8.36149691e-02 1.69468882e-02], 1.4e-05\n",
      "161, [2.78886363e-01 3.19074337e-06 8.36149703e-02 1.69468849e-02], 1.4e-05\n",
      "162, [2.78890633e-01 1.59556585e-06 8.36154295e-02 1.69412734e-02], 9e-06\n",
      "163, [2.78890503e-01 1.99435777e-06 8.36154682e-02 1.69411670e-02], 9e-06\n",
      "164, [2.78890463e-01 2.11894173e-06 8.36154802e-02 1.69411338e-02], 9e-06\n",
      "165, [2.78890460e-01 2.12721034e-06 8.36154810e-02 1.69411316e-02], 9e-06\n",
      "166, [2.78893309e-01 1.06370771e-06 8.36157876e-02 1.69373882e-02], 6e-06\n",
      "167, [2.78893223e-01 1.32958227e-06 8.36158134e-02 1.69373172e-02], 6e-06\n",
      "168, [2.78893196e-01 1.41264791e-06 8.36158214e-02 1.69372951e-02], 6e-06\n",
      "169, [2.78893194e-01 1.41816161e-06 8.36158220e-02 1.69372936e-02], 6e-06\n",
      "170, [2.78895094e-01 7.09137198e-07 8.36160265e-02 1.69347969e-02], 4e-06\n",
      "171, [2.78895036e-01 8.86392816e-07 8.36160437e-02 1.69347496e-02], 4e-06\n",
      "172, [2.78895018e-01 9.41774207e-07 8.36160491e-02 1.69347348e-02], 4e-06\n",
      "173, [2.78895017e-01 9.45450583e-07 8.36160494e-02 1.69347338e-02], 4e-06\n",
      "174, [2.78896284e-01 4.72757564e-07 8.36161859e-02 1.69330689e-02], 3e-06\n",
      "175, [2.78896245e-01 5.90930603e-07 8.36161974e-02 1.69330373e-02], 3e-06\n",
      "176, [2.78896233e-01 6.27853442e-07 8.36162009e-02 1.69330275e-02], 3e-06\n",
      "177, [2.78896233e-01 6.30304615e-07 8.36162012e-02 1.69330268e-02], 3e-06\n",
      "178, [2.78897077e-01 3.15171457e-07 8.36162922e-02 1.69319166e-02], 2e-06\n",
      "179, [2.78897052e-01 3.93954651e-07 8.36162998e-02 1.69318956e-02], 2e-06\n",
      "180, [2.78897044e-01 4.18570726e-07 8.36163022e-02 1.69318890e-02], 2e-06\n",
      "181, [2.78897043e-01 4.20204956e-07 8.36163024e-02 1.69318886e-02], 2e-06\n",
      "182, [2.78897607e-01 2.10114192e-07 8.36163631e-02 1.69311484e-02], 1e-06\n",
      "183, [2.78897590e-01 2.62636841e-07 8.36163682e-02 1.69311344e-02], 1e-06\n",
      "184, [2.78897584e-01 2.79047935e-07 8.36163697e-02 1.69311300e-02], 1e-06\n",
      "185, [2.78897584e-01 2.80137472e-07 8.36163699e-02 1.69311297e-02], 1e-06\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2.78897584e-01, 2.80137472e-07, 8.36163699e-02, 1.69311297e-02])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# def f(x):\n",
    "#     return x\n",
    "\n",
    "\n",
    "def g(x):\n",
    "    d = np.array([\n",
    "        5.0, -2., 10, 6\n",
    "    ])\n",
    "    h = H(x)\n",
    "    return h.dot(x) - d\n",
    "\n",
    "\n",
    "def H(x):\n",
    "    return np.array([\n",
    "        [16.0, 8.0, 4.0, 12.0],\n",
    "        [8.0, 29.0, -3.0, 16.0],\n",
    "        [4.0, -3.0, 102.0, 21.0],\n",
    "        [12.0, 16.0, 21.0,  53.],\n",
    "    ])\n",
    "\n",
    "\n",
    "A = np.array([\n",
    "    [1., 0., 0., 0.],\n",
    "    [0., 1., 0., 0.],\n",
    "    [0., 0., 1., 0.],\n",
    "    [0., 0., 0., 1.],\n",
    "    [-1., 0., 0., 0.],\n",
    "    [0., -1., 0., 0.],\n",
    "    [0., 0., -1., 0.],\n",
    "    [0., 0., 0., -1.]\n",
    "])\n",
    "b = np.array(\n",
    "    [0, 0, 0, 0, -1, -1, -1, -1]\n",
    ")\n",
    "x = np.ones(4)*.9\n",
    "mu = 100\n",
    "barreraLog(x, A, b, mu=mu, tol=1e-6)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.9, 0.9, 0.9, 0.9])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones(4)*.9\n"
   ]
  }
 ],
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   "name": "python3"
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