Selected project scipy

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import linprog\n",
    "from scipy.optimize import Bounds\n",
    "from scipy.optimize import minimize\n",
    "import numpy as np \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     con: array([], dtype=float64)\n",
      "     fun: -37.0\n",
      " message: 'Optimization terminated successfully.'\n",
      "     nit: 3\n",
      "   slack: array([0., 0., 3.])\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([3., 5.])\n"
     ]
    }
   ],
   "source": [
    "c = [-4,-5]\n",
    "a = [[1,1],[1,3],[2,1]]\n",
    "b = [8,18,14]\n",
    "\n",
    "res = linprog(c,A_ub=a,b_ub=b, method='simplex') \n",
    "print (res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                   ================ Results when initial guess is  (-3.0, 3.0) ================\n",
      "     fun: -3.2235359612692725e-05\n",
      "     jac: array([-0.000199  ,  0.00013173])\n",
      " message: 'Optimization terminated successfully.'\n",
      "    nfev: 4\n",
      "     nit: 1\n",
      "    njev: 1\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([-3.,  3.])\n",
      "                                   ================ Results when initial guess is  (-1, 1) ================\n",
      "     fun: -8.106213317674746\n",
      "     jac: array([-0.00058389, -0.00409687])\n",
      " message: 'Optimization terminated successfully.'\n",
      "    nfev: 45\n",
      "     nit: 10\n",
      "    njev: 10\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([-0.00935726,  1.58124096])\n",
      "                                   ================ Results when initial guess is  (2, -1) ================\n",
      "     fun: -3.592489910983635\n",
      "     jac: array([-0.00011516, -0.00030747])\n",
      " message: 'Optimization terminated successfully.'\n",
      "    nfev: 30\n",
      "     nit: 7\n",
      "    njev: 7\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 1.2856778 , -0.00489063])\n",
      "                                   ================ Results when initial guess is  (3, 1) ================\n",
      "     fun: -3.7765807176898147\n",
      "     jac: array([ 0.00097468, -0.00355253])\n",
      " message: 'Optimization terminated successfully.'\n",
      "    nfev: 39\n",
      "     nit: 8\n",
      "    njev: 8\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([-0.4600389 , -0.62935856])\n"
     ]
    }
   ],
   "source": [
    "def f(x):\n",
    "    return -1*(3*( (1-x[0])**2 ) * np.exp( -(x[0]**2)-(x[1]+1)**2 ) - 10*(x[0]/5 - (x[0]**3) -(x[1]**5)) * np.exp(-(x[0]**2) - (x[1]**2)) - (1/3)*np.exp(-((x[0]+1)**2) -(x[1]**2)))\n",
    "\n",
    "x0 = np.array([1,2], dtype=np.float64)\n",
    "for i in [(-3.0,3.0),(-1,1),(2,-1),(3,1)]:\n",
    "    \n",
    "    x0[0]=i[0]\n",
    "    x0[1]=i[1]\n",
    "    res = minimize(f,x0,method='SLSQP')\n",
    "    print(\"                                   ================ Results when initial guess is \",i,\"================\")\n",
    "    print(str(res))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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