NumberLink.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e8e0ec5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pulp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98013cb5",
   "metadata": {},
   "source": [
    "http://yetanothermathprogrammingconsultant.blogspot.com/2017/09/numberlink-models.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e685faaa",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c34e7d77",
   "metadata": {},
   "outputs": [],
   "source": [
    "T = [\n",
    "    [0,0,0,4,0,0,0],\n",
    "    [0,3,0,0,2,5,0],\n",
    "    [0,0,0,3,1,0,0],\n",
    "    [0,0,0,5,0,0,0],\n",
    "    [0,0,0,0,0,0,0],\n",
    "    [0,0,1,0,0,0,0],\n",
    "    [2,0,0,0,4,0,0],\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "763ba2f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0, 0, 0, 4, 0, 0, 0],\n",
       " [0, 3, 0, 0, 2, 5, 0],\n",
       " [0, 0, 0, 3, 1, 0, 0],\n",
       " [0, 0, 0, 5, 0, 0, 0],\n",
       " [0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 1, 0, 0, 0, 0],\n",
       " [2, 0, 0, 0, 4, 0, 0]]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1be6c2c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "cells = [(i,j) for i in range(len(T)) for j in range(len(T))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "47075e9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cells[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "15236c13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T[cells[0][0]][cells[0][1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "447590be",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for (i,j) in cells:\n",
    "    #print(T[i][j],)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1bad6754",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(0, 0): [(0, 1), (1, 0)],\n",
       " (0, 1): [(0, 0), (0, 2), (1, 1)],\n",
       " (0, 2): [(0, 1), (0, 3), (1, 2)],\n",
       " (0, 3): [(0, 2), (0, 4), (1, 3)],\n",
       " (0, 4): [(0, 3), (0, 5), (1, 4)],\n",
       " (0, 5): [(0, 4), (0, 6), (1, 5)],\n",
       " (0, 6): [(0, 5), (1, 6)],\n",
       " (1, 0): [(0, 0), (1, 1), (2, 0)],\n",
       " (1, 1): [(0, 1), (1, 0), (1, 2), (2, 1)],\n",
       " (1, 2): [(0, 2), (1, 1), (1, 3), (2, 2)],\n",
       " (1, 3): [(0, 3), (1, 2), (1, 4), (2, 3)],\n",
       " (1, 4): [(0, 4), (1, 3), (1, 5), (2, 4)],\n",
       " (1, 5): [(0, 5), (1, 4), (1, 6), (2, 5)],\n",
       " (1, 6): [(0, 6), (1, 5), (2, 6)],\n",
       " (2, 0): [(1, 0), (2, 1), (3, 0)],\n",
       " (2, 1): [(1, 1), (2, 0), (2, 2), (3, 1)],\n",
       " (2, 2): [(1, 2), (2, 1), (2, 3), (3, 2)],\n",
       " (2, 3): [(1, 3), (2, 2), (2, 4), (3, 3)],\n",
       " (2, 4): [(1, 4), (2, 3), (2, 5), (3, 4)],\n",
       " (2, 5): [(1, 5), (2, 4), (2, 6), (3, 5)],\n",
       " (2, 6): [(1, 6), (2, 5), (3, 6)],\n",
       " (3, 0): [(2, 0), (3, 1), (4, 0)],\n",
       " (3, 1): [(2, 1), (3, 0), (3, 2), (4, 1)],\n",
       " (3, 2): [(2, 2), (3, 1), (3, 3), (4, 2)],\n",
       " (3, 3): [(2, 3), (3, 2), (3, 4), (4, 3)],\n",
       " (3, 4): [(2, 4), (3, 3), (3, 5), (4, 4)],\n",
       " (3, 5): [(2, 5), (3, 4), (3, 6), (4, 5)],\n",
       " (3, 6): [(2, 6), (3, 5), (4, 6)],\n",
       " (4, 0): [(3, 0), (4, 1), (5, 0)],\n",
       " (4, 1): [(3, 1), (4, 0), (4, 2), (5, 1)],\n",
       " (4, 2): [(3, 2), (4, 1), (4, 3), (5, 2)],\n",
       " (4, 3): [(3, 3), (4, 2), (4, 4), (5, 3)],\n",
       " (4, 4): [(3, 4), (4, 3), (4, 5), (5, 4)],\n",
       " (4, 5): [(3, 5), (4, 4), (4, 6), (5, 5)],\n",
       " (4, 6): [(3, 6), (4, 5), (5, 6)],\n",
       " (5, 0): [(4, 0), (5, 1), (6, 0)],\n",
       " (5, 1): [(4, 1), (5, 0), (5, 2), (6, 1)],\n",
       " (5, 2): [(4, 2), (5, 1), (5, 3), (6, 2)],\n",
       " (5, 3): [(4, 3), (5, 2), (5, 4), (6, 3)],\n",
       " (5, 4): [(4, 4), (5, 3), (5, 5), (6, 4)],\n",
       " (5, 5): [(4, 5), (5, 4), (5, 6), (6, 5)],\n",
       " (5, 6): [(4, 6), (5, 5), (6, 6)],\n",
       " (6, 0): [(5, 0), (6, 1)],\n",
       " (6, 1): [(5, 1), (6, 0), (6, 2)],\n",
       " (6, 2): [(5, 2), (6, 1), (6, 3)],\n",
       " (6, 3): [(5, 3), (6, 2), (6, 4)],\n",
       " (6, 4): [(5, 4), (6, 3), (6, 5)],\n",
       " (6, 5): [(5, 5), (6, 4), (6, 6)],\n",
       " (6, 6): [(5, 6), (6, 5)]}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighbours = {}\n",
    "for (i,j) in cells:\n",
    "    #neighbours[(i,j)] = [(i-1,j),(i+1,j),(i,j-1),(i,j+1)]\n",
    "    neighbours[(i,j)] = [(i+k,j+h) for k in [-1,0,1] for h in [-1,0,1] if k+h!=0 and k*h!=1 and i+k>=0 and i+k<=6 and j+h>=0 and j+h<=6 ]\n",
    "neighbours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7197f27b",
   "metadata": {},
   "outputs": [],
   "source": [
    "paths = [p for p in range(1,6)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59e3f6b2",
   "metadata": {},
   "source": [
    "# MIP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "408b1fd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create problem\n",
    "prob = pulp.LpProblem(\"numberlink\", pulp.LpMaximize)\n",
    "\n",
    "# define variables\n",
    "x = pulp.LpVariable.dicts(\"x\",(cells,paths),cat=pulp.LpBinary)\n",
    "\n",
    "# objective function\n",
    "prob += 0\n",
    "\n",
    "# each cell has a unique number\n",
    "for (i,j) in cells:\n",
    "    prob += pulp.lpSum(x[(i,j)][k] for k in paths) == 1\n",
    "    \n",
    "# enforce endpoints\n",
    "for (i,j) in cells:\n",
    "    val = T[i][j]\n",
    "    if val > 0:\n",
    "        prob += x[(i,j)][val] == 1\n",
    "        \n",
    "# if cell is an endpoint, then one neighbor must have same value\n",
    "for (i,j) in cells:\n",
    "    val = T[i][j]\n",
    "    if val > 0:\n",
    "        prob += pulp.lpSum(x[(u,v)][val] for (u,v) in neighbours[(i,j)]) == 1\n",
    "        \n",
    "# if cell is not an end point, then 2 neighbours must have same value\n",
    "for (i,j) in cells:\n",
    "    val = T[i][j]\n",
    "    if val == 0:\n",
    "        for k in paths:\n",
    "            prob += pulp.lpSum(x[(u,v)][k] for (u,v) in neighbours[(i,j)]) >= 2*x[(i,j)][k]\n",
    "            prob += pulp.lpSum(x[(u,v)][k] for (u,v) in neighbours[(i,j)]) <= 2*x[(i,j)][k]+4*(1-x[(i,j)][k])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "91980c48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numberlink:\n",
       "MAXIMIZE\n",
       "0\n",
       "SUBJECT TO\n",
       "_C1: x_(0,_0)_1 + x_(0,_0)_2 + x_(0,_0)_3 + x_(0,_0)_4 + x_(0,_0)_5 = 1\n",
       "\n",
       "_C2: x_(0,_1)_1 + x_(0,_1)_2 + x_(0,_1)_3 + x_(0,_1)_4 + x_(0,_1)_5 = 1\n",
       "\n",
       "_C3: x_(0,_2)_1 + x_(0,_2)_2 + x_(0,_2)_3 + x_(0,_2)_4 + x_(0,_2)_5 = 1\n",
       "\n",
       "_C4: x_(0,_3)_1 + x_(0,_3)_2 + x_(0,_3)_3 + x_(0,_3)_4 + x_(0,_3)_5 = 1\n",
       "\n",
       "_C5: x_(0,_4)_1 + x_(0,_4)_2 + x_(0,_4)_3 + x_(0,_4)_4 + x_(0,_4)_5 = 1\n",
       "\n",
       "_C6: x_(0,_5)_1 + x_(0,_5)_2 + x_(0,_5)_3 + x_(0,_5)_4 + x_(0,_5)_5 = 1\n",
       "\n",
       "_C7: x_(0,_6)_1 + x_(0,_6)_2 + x_(0,_6)_3 + x_(0,_6)_4 + x_(0,_6)_5 = 1\n",
       "\n",
       "_C8: x_(1,_0)_1 + x_(1,_0)_2 + x_(1,_0)_3 + x_(1,_0)_4 + x_(1,_0)_5 = 1\n",
       "\n",
       "_C9: x_(1,_1)_1 + x_(1,_1)_2 + x_(1,_1)_3 + x_(1,_1)_4 + x_(1,_1)_5 = 1\n",
       "\n",
       "_C10: x_(1,_2)_1 + x_(1,_2)_2 + x_(1,_2)_3 + x_(1,_2)_4 + x_(1,_2)_5 = 1\n",
       "\n",
       "_C11: x_(1,_3)_1 + x_(1,_3)_2 + x_(1,_3)_3 + x_(1,_3)_4 + x_(1,_3)_5 = 1\n",
       "\n",
       "_C12: x_(1,_4)_1 + x_(1,_4)_2 + x_(1,_4)_3 + x_(1,_4)_4 + x_(1,_4)_5 = 1\n",
       "\n",
       "_C13: x_(1,_5)_1 + x_(1,_5)_2 + x_(1,_5)_3 + x_(1,_5)_4 + x_(1,_5)_5 = 1\n",
       "\n",
       "_C14: x_(1,_6)_1 + x_(1,_6)_2 + x_(1,_6)_3 + x_(1,_6)_4 + x_(1,_6)_5 = 1\n",
       "\n",
       "_C15: x_(2,_0)_1 + x_(2,_0)_2 + x_(2,_0)_3 + x_(2,_0)_4 + x_(2,_0)_5 = 1\n",
       "\n",
       "_C16: x_(2,_1)_1 + x_(2,_1)_2 + x_(2,_1)_3 + x_(2,_1)_4 + x_(2,_1)_5 = 1\n",
       "\n",
       "_C17: x_(2,_2)_1 + x_(2,_2)_2 + x_(2,_2)_3 + x_(2,_2)_4 + x_(2,_2)_5 = 1\n",
       "\n",
       "_C18: x_(2,_3)_1 + x_(2,_3)_2 + x_(2,_3)_3 + x_(2,_3)_4 + x_(2,_3)_5 = 1\n",
       "\n",
       "_C19: x_(2,_4)_1 + x_(2,_4)_2 + x_(2,_4)_3 + x_(2,_4)_4 + x_(2,_4)_5 = 1\n",
       "\n",
       "_C20: x_(2,_5)_1 + x_(2,_5)_2 + x_(2,_5)_3 + x_(2,_5)_4 + x_(2,_5)_5 = 1\n",
       "\n",
       "_C21: x_(2,_6)_1 + x_(2,_6)_2 + x_(2,_6)_3 + x_(2,_6)_4 + x_(2,_6)_5 = 1\n",
       "\n",
       "_C22: x_(3,_0)_1 + x_(3,_0)_2 + x_(3,_0)_3 + x_(3,_0)_4 + x_(3,_0)_5 = 1\n",
       "\n",
       "_C23: x_(3,_1)_1 + x_(3,_1)_2 + x_(3,_1)_3 + x_(3,_1)_4 + x_(3,_1)_5 = 1\n",
       "\n",
       "_C24: x_(3,_2)_1 + x_(3,_2)_2 + x_(3,_2)_3 + x_(3,_2)_4 + x_(3,_2)_5 = 1\n",
       "\n",
       "_C25: x_(3,_3)_1 + x_(3,_3)_2 + x_(3,_3)_3 + x_(3,_3)_4 + x_(3,_3)_5 = 1\n",
       "\n",
       "_C26: x_(3,_4)_1 + x_(3,_4)_2 + x_(3,_4)_3 + x_(3,_4)_4 + x_(3,_4)_5 = 1\n",
       "\n",
       "_C27: x_(3,_5)_1 + x_(3,_5)_2 + x_(3,_5)_3 + x_(3,_5)_4 + x_(3,_5)_5 = 1\n",
       "\n",
       "_C28: x_(3,_6)_1 + x_(3,_6)_2 + x_(3,_6)_3 + x_(3,_6)_4 + x_(3,_6)_5 = 1\n",
       "\n",
       "_C29: x_(4,_0)_1 + x_(4,_0)_2 + x_(4,_0)_3 + x_(4,_0)_4 + x_(4,_0)_5 = 1\n",
       "\n",
       "_C30: x_(4,_1)_1 + x_(4,_1)_2 + x_(4,_1)_3 + x_(4,_1)_4 + x_(4,_1)_5 = 1\n",
       "\n",
       "_C31: x_(4,_2)_1 + x_(4,_2)_2 + x_(4,_2)_3 + x_(4,_2)_4 + x_(4,_2)_5 = 1\n",
       "\n",
       "_C32: x_(4,_3)_1 + x_(4,_3)_2 + x_(4,_3)_3 + x_(4,_3)_4 + x_(4,_3)_5 = 1\n",
       "\n",
       "_C33: x_(4,_4)_1 + x_(4,_4)_2 + x_(4,_4)_3 + x_(4,_4)_4 + x_(4,_4)_5 = 1\n",
       "\n",
       "_C34: x_(4,_5)_1 + x_(4,_5)_2 + x_(4,_5)_3 + x_(4,_5)_4 + x_(4,_5)_5 = 1\n",
       "\n",
       "_C35: x_(4,_6)_1 + x_(4,_6)_2 + x_(4,_6)_3 + x_(4,_6)_4 + x_(4,_6)_5 = 1\n",
       "\n",
       "_C36: x_(5,_0)_1 + x_(5,_0)_2 + x_(5,_0)_3 + x_(5,_0)_4 + x_(5,_0)_5 = 1\n",
       "\n",
       "_C37: x_(5,_1)_1 + x_(5,_1)_2 + x_(5,_1)_3 + x_(5,_1)_4 + x_(5,_1)_5 = 1\n",
       "\n",
       "_C38: x_(5,_2)_1 + x_(5,_2)_2 + x_(5,_2)_3 + x_(5,_2)_4 + x_(5,_2)_5 = 1\n",
       "\n",
       "_C39: x_(5,_3)_1 + x_(5,_3)_2 + x_(5,_3)_3 + x_(5,_3)_4 + x_(5,_3)_5 = 1\n",
       "\n",
       "_C40: x_(5,_4)_1 + x_(5,_4)_2 + x_(5,_4)_3 + x_(5,_4)_4 + x_(5,_4)_5 = 1\n",
       "\n",
       "_C41: x_(5,_5)_1 + x_(5,_5)_2 + x_(5,_5)_3 + x_(5,_5)_4 + x_(5,_5)_5 = 1\n",
       "\n",
       "_C42: x_(5,_6)_1 + x_(5,_6)_2 + x_(5,_6)_3 + x_(5,_6)_4 + x_(5,_6)_5 = 1\n",
       "\n",
       "_C43: x_(6,_0)_1 + x_(6,_0)_2 + x_(6,_0)_3 + x_(6,_0)_4 + x_(6,_0)_5 = 1\n",
       "\n",
       "_C44: x_(6,_1)_1 + x_(6,_1)_2 + x_(6,_1)_3 + x_(6,_1)_4 + x_(6,_1)_5 = 1\n",
       "\n",
       "_C45: x_(6,_2)_1 + x_(6,_2)_2 + x_(6,_2)_3 + x_(6,_2)_4 + x_(6,_2)_5 = 1\n",
       "\n",
       "_C46: x_(6,_3)_1 + x_(6,_3)_2 + x_(6,_3)_3 + x_(6,_3)_4 + x_(6,_3)_5 = 1\n",
       "\n",
       "_C47: x_(6,_4)_1 + x_(6,_4)_2 + x_(6,_4)_3 + x_(6,_4)_4 + x_(6,_4)_5 = 1\n",
       "\n",
       "_C48: x_(6,_5)_1 + x_(6,_5)_2 + x_(6,_5)_3 + x_(6,_5)_4 + x_(6,_5)_5 = 1\n",
       "\n",
       "_C49: x_(6,_6)_1 + x_(6,_6)_2 + x_(6,_6)_3 + x_(6,_6)_4 + x_(6,_6)_5 = 1\n",
       "\n",
       "_C50: x_(0,_3)_4 = 1\n",
       "\n",
       "_C51: x_(1,_1)_3 = 1\n",
       "\n",
       "_C52: x_(1,_4)_2 = 1\n",
       "\n",
       "_C53: x_(1,_5)_5 = 1\n",
       "\n",
       "_C54: x_(2,_3)_3 = 1\n",
       "\n",
       "_C55: x_(2,_4)_1 = 1\n",
       "\n",
       "_C56: x_(3,_3)_5 = 1\n",
       "\n",
       "_C57: x_(5,_2)_1 = 1\n",
       "\n",
       "_C58: x_(6,_0)_2 = 1\n",
       "\n",
       "_C59: x_(6,_4)_4 = 1\n",
       "\n",
       "_C60: x_(0,_2)_4 + x_(0,_4)_4 + x_(1,_3)_4 = 1\n",
       "\n",
       "_C61: x_(0,_1)_3 + x_(1,_0)_3 + x_(1,_2)_3 + x_(2,_1)_3 = 1\n",
       "\n",
       "_C62: x_(0,_4)_2 + x_(1,_3)_2 + x_(1,_5)_2 + x_(2,_4)_2 = 1\n",
       "\n",
       "_C63: x_(0,_5)_5 + x_(1,_4)_5 + x_(1,_6)_5 + x_(2,_5)_5 = 1\n",
       "\n",
       "_C64: x_(1,_3)_3 + x_(2,_2)_3 + x_(2,_4)_3 + x_(3,_3)_3 = 1\n",
       "\n",
       "_C65: x_(1,_4)_1 + x_(2,_3)_1 + x_(2,_5)_1 + x_(3,_4)_1 = 1\n",
       "\n",
       "_C66: x_(2,_3)_5 + x_(3,_2)_5 + x_(3,_4)_5 + x_(4,_3)_5 = 1\n",
       "\n",
       "_C67: x_(4,_2)_1 + x_(5,_1)_1 + x_(5,_3)_1 + x_(6,_2)_1 = 1\n",
       "\n",
       "_C68: x_(5,_0)_2 + x_(6,_1)_2 = 1\n",
       "\n",
       "_C69: x_(5,_4)_4 + x_(6,_3)_4 + x_(6,_5)_4 = 1\n",
       "\n",
       "_C70: - 2 x_(0,_0)_1 + x_(0,_1)_1 + x_(1,_0)_1 >= 0\n",
       "\n",
       "_C71: 2 x_(0,_0)_1 + x_(0,_1)_1 + x_(1,_0)_1 <= 4\n",
       "\n",
       "_C72: - 2 x_(0,_0)_2 + x_(0,_1)_2 + x_(1,_0)_2 >= 0\n",
       "\n",
       "_C73: 2 x_(0,_0)_2 + x_(0,_1)_2 + x_(1,_0)_2 <= 4\n",
       "\n",
       "_C74: - 2 x_(0,_0)_3 + x_(0,_1)_3 + x_(1,_0)_3 >= 0\n",
       "\n",
       "_C75: 2 x_(0,_0)_3 + x_(0,_1)_3 + x_(1,_0)_3 <= 4\n",
       "\n",
       "_C76: - 2 x_(0,_0)_4 + x_(0,_1)_4 + x_(1,_0)_4 >= 0\n",
       "\n",
       "_C77: 2 x_(0,_0)_4 + x_(0,_1)_4 + x_(1,_0)_4 <= 4\n",
       "\n",
       "_C78: - 2 x_(0,_0)_5 + x_(0,_1)_5 + x_(1,_0)_5 >= 0\n",
       "\n",
       "_C79: 2 x_(0,_0)_5 + x_(0,_1)_5 + x_(1,_0)_5 <= 4\n",
       "\n",
       "_C80: x_(0,_0)_1 - 2 x_(0,_1)_1 + x_(0,_2)_1 + x_(1,_1)_1 >= 0\n",
       "\n",
       "_C81: x_(0,_0)_1 + 2 x_(0,_1)_1 + x_(0,_2)_1 + x_(1,_1)_1 <= 4\n",
       "\n",
       "_C82: x_(0,_0)_2 - 2 x_(0,_1)_2 + x_(0,_2)_2 + x_(1,_1)_2 >= 0\n",
       "\n",
       "_C83: x_(0,_0)_2 + 2 x_(0,_1)_2 + x_(0,_2)_2 + x_(1,_1)_2 <= 4\n",
       "\n",
       "_C84: x_(0,_0)_3 - 2 x_(0,_1)_3 + x_(0,_2)_3 + x_(1,_1)_3 >= 0\n",
       "\n",
       "_C85: x_(0,_0)_3 + 2 x_(0,_1)_3 + x_(0,_2)_3 + x_(1,_1)_3 <= 4\n",
       "\n",
       "_C86: x_(0,_0)_4 - 2 x_(0,_1)_4 + x_(0,_2)_4 + x_(1,_1)_4 >= 0\n",
       "\n",
       "_C87: x_(0,_0)_4 + 2 x_(0,_1)_4 + x_(0,_2)_4 + x_(1,_1)_4 <= 4\n",
       "\n",
       "_C88: x_(0,_0)_5 - 2 x_(0,_1)_5 + x_(0,_2)_5 + x_(1,_1)_5 >= 0\n",
       "\n",
       "_C89: x_(0,_0)_5 + 2 x_(0,_1)_5 + x_(0,_2)_5 + x_(1,_1)_5 <= 4\n",
       "\n",
       "_C90: x_(0,_1)_1 - 2 x_(0,_2)_1 + x_(0,_3)_1 + x_(1,_2)_1 >= 0\n",
       "\n",
       "_C91: x_(0,_1)_1 + 2 x_(0,_2)_1 + x_(0,_3)_1 + x_(1,_2)_1 <= 4\n",
       "\n",
       "_C92: x_(0,_1)_2 - 2 x_(0,_2)_2 + x_(0,_3)_2 + x_(1,_2)_2 >= 0\n",
       "\n",
       "_C93: x_(0,_1)_2 + 2 x_(0,_2)_2 + x_(0,_3)_2 + x_(1,_2)_2 <= 4\n",
       "\n",
       "_C94: x_(0,_1)_3 - 2 x_(0,_2)_3 + x_(0,_3)_3 + x_(1,_2)_3 >= 0\n",
       "\n",
       "_C95: x_(0,_1)_3 + 2 x_(0,_2)_3 + x_(0,_3)_3 + x_(1,_2)_3 <= 4\n",
       "\n",
       "_C96: x_(0,_1)_4 - 2 x_(0,_2)_4 + x_(0,_3)_4 + x_(1,_2)_4 >= 0\n",
       "\n",
       "_C97: x_(0,_1)_4 + 2 x_(0,_2)_4 + x_(0,_3)_4 + x_(1,_2)_4 <= 4\n",
       "\n",
       "_C98: x_(0,_1)_5 - 2 x_(0,_2)_5 + x_(0,_3)_5 + x_(1,_2)_5 >= 0\n",
       "\n",
       "_C99: x_(0,_1)_5 + 2 x_(0,_2)_5 + x_(0,_3)_5 + x_(1,_2)_5 <= 4\n",
       "\n",
       "_C100: x_(0,_3)_1 - 2 x_(0,_4)_1 + x_(0,_5)_1 + x_(1,_4)_1 >= 0\n",
       "\n",
       "_C101: x_(0,_3)_1 + 2 x_(0,_4)_1 + x_(0,_5)_1 + x_(1,_4)_1 <= 4\n",
       "\n",
       "_C102: x_(0,_3)_2 - 2 x_(0,_4)_2 + x_(0,_5)_2 + x_(1,_4)_2 >= 0\n",
       "\n",
       "_C103: x_(0,_3)_2 + 2 x_(0,_4)_2 + x_(0,_5)_2 + x_(1,_4)_2 <= 4\n",
       "\n",
       "_C104: x_(0,_3)_3 - 2 x_(0,_4)_3 + x_(0,_5)_3 + x_(1,_4)_3 >= 0\n",
       "\n",
       "_C105: x_(0,_3)_3 + 2 x_(0,_4)_3 + x_(0,_5)_3 + x_(1,_4)_3 <= 4\n",
       "\n",
       "_C106: x_(0,_3)_4 - 2 x_(0,_4)_4 + x_(0,_5)_4 + x_(1,_4)_4 >= 0\n",
       "\n",
       "_C107: x_(0,_3)_4 + 2 x_(0,_4)_4 + x_(0,_5)_4 + x_(1,_4)_4 <= 4\n",
       "\n",
       "_C108: x_(0,_3)_5 - 2 x_(0,_4)_5 + x_(0,_5)_5 + x_(1,_4)_5 >= 0\n",
       "\n",
       "_C109: x_(0,_3)_5 + 2 x_(0,_4)_5 + x_(0,_5)_5 + x_(1,_4)_5 <= 4\n",
       "\n",
       "_C110: x_(0,_4)_1 - 2 x_(0,_5)_1 + x_(0,_6)_1 + x_(1,_5)_1 >= 0\n",
       "\n",
       "_C111: x_(0,_4)_1 + 2 x_(0,_5)_1 + x_(0,_6)_1 + x_(1,_5)_1 <= 4\n",
       "\n",
       "_C112: x_(0,_4)_2 - 2 x_(0,_5)_2 + x_(0,_6)_2 + x_(1,_5)_2 >= 0\n",
       "\n",
       "_C113: x_(0,_4)_2 + 2 x_(0,_5)_2 + x_(0,_6)_2 + x_(1,_5)_2 <= 4\n",
       "\n",
       "_C114: x_(0,_4)_3 - 2 x_(0,_5)_3 + x_(0,_6)_3 + x_(1,_5)_3 >= 0\n",
       "\n",
       "_C115: x_(0,_4)_3 + 2 x_(0,_5)_3 + x_(0,_6)_3 + x_(1,_5)_3 <= 4\n",
       "\n",
       "_C116: x_(0,_4)_4 - 2 x_(0,_5)_4 + x_(0,_6)_4 + x_(1,_5)_4 >= 0\n",
       "\n",
       "_C117: x_(0,_4)_4 + 2 x_(0,_5)_4 + x_(0,_6)_4 + x_(1,_5)_4 <= 4\n",
       "\n",
       "_C118: x_(0,_4)_5 - 2 x_(0,_5)_5 + x_(0,_6)_5 + x_(1,_5)_5 >= 0\n",
       "\n",
       "_C119: x_(0,_4)_5 + 2 x_(0,_5)_5 + x_(0,_6)_5 + x_(1,_5)_5 <= 4\n",
       "\n",
       "_C120: x_(0,_5)_1 - 2 x_(0,_6)_1 + x_(1,_6)_1 >= 0\n",
       "\n",
       "_C121: x_(0,_5)_1 + 2 x_(0,_6)_1 + x_(1,_6)_1 <= 4\n",
       "\n",
       "_C122: x_(0,_5)_2 - 2 x_(0,_6)_2 + x_(1,_6)_2 >= 0\n",
       "\n",
       "_C123: x_(0,_5)_2 + 2 x_(0,_6)_2 + x_(1,_6)_2 <= 4\n",
       "\n",
       "_C124: x_(0,_5)_3 - 2 x_(0,_6)_3 + x_(1,_6)_3 >= 0\n",
       "\n",
       "_C125: x_(0,_5)_3 + 2 x_(0,_6)_3 + x_(1,_6)_3 <= 4\n",
       "\n",
       "_C126: x_(0,_5)_4 - 2 x_(0,_6)_4 + x_(1,_6)_4 >= 0\n",
       "\n",
       "_C127: x_(0,_5)_4 + 2 x_(0,_6)_4 + x_(1,_6)_4 <= 4\n",
       "\n",
       "_C128: x_(0,_5)_5 - 2 x_(0,_6)_5 + x_(1,_6)_5 >= 0\n",
       "\n",
       "_C129: x_(0,_5)_5 + 2 x_(0,_6)_5 + x_(1,_6)_5 <= 4\n",
       "\n",
       "_C130: x_(0,_0)_1 - 2 x_(1,_0)_1 + x_(1,_1)_1 + x_(2,_0)_1 >= 0\n",
       "\n",
       "_C131: x_(0,_0)_1 + 2 x_(1,_0)_1 + x_(1,_1)_1 + x_(2,_0)_1 <= 4\n",
       "\n",
       "_C132: x_(0,_0)_2 - 2 x_(1,_0)_2 + x_(1,_1)_2 + x_(2,_0)_2 >= 0\n",
       "\n",
       "_C133: x_(0,_0)_2 + 2 x_(1,_0)_2 + x_(1,_1)_2 + x_(2,_0)_2 <= 4\n",
       "\n",
       "_C134: x_(0,_0)_3 - 2 x_(1,_0)_3 + x_(1,_1)_3 + x_(2,_0)_3 >= 0\n",
       "\n",
       "_C135: x_(0,_0)_3 + 2 x_(1,_0)_3 + x_(1,_1)_3 + x_(2,_0)_3 <= 4\n",
       "\n",
       "_C136: x_(0,_0)_4 - 2 x_(1,_0)_4 + x_(1,_1)_4 + x_(2,_0)_4 >= 0\n",
       "\n",
       "_C137: x_(0,_0)_4 + 2 x_(1,_0)_4 + x_(1,_1)_4 + x_(2,_0)_4 <= 4\n",
       "\n",
       "_C138: x_(0,_0)_5 - 2 x_(1,_0)_5 + x_(1,_1)_5 + x_(2,_0)_5 >= 0\n",
       "\n",
       "_C139: x_(0,_0)_5 + 2 x_(1,_0)_5 + x_(1,_1)_5 + x_(2,_0)_5 <= 4\n",
       "\n",
       "_C140: x_(0,_2)_1 + x_(1,_1)_1 - 2 x_(1,_2)_1 + x_(1,_3)_1 + x_(2,_2)_1 >= 0\n",
       "\n",
       "_C141: x_(0,_2)_1 + x_(1,_1)_1 + 2 x_(1,_2)_1 + x_(1,_3)_1 + x_(2,_2)_1 <= 4\n",
       "\n",
       "_C142: x_(0,_2)_2 + x_(1,_1)_2 - 2 x_(1,_2)_2 + x_(1,_3)_2 + x_(2,_2)_2 >= 0\n",
       "\n",
       "_C143: x_(0,_2)_2 + x_(1,_1)_2 + 2 x_(1,_2)_2 + x_(1,_3)_2 + x_(2,_2)_2 <= 4\n",
       "\n",
       "_C144: x_(0,_2)_3 + x_(1,_1)_3 - 2 x_(1,_2)_3 + x_(1,_3)_3 + x_(2,_2)_3 >= 0\n",
       "\n",
       "_C145: x_(0,_2)_3 + x_(1,_1)_3 + 2 x_(1,_2)_3 + x_(1,_3)_3 + x_(2,_2)_3 <= 4\n",
       "\n",
       "_C146: x_(0,_2)_4 + x_(1,_1)_4 - 2 x_(1,_2)_4 + x_(1,_3)_4 + x_(2,_2)_4 >= 0\n",
       "\n",
       "_C147: x_(0,_2)_4 + x_(1,_1)_4 + 2 x_(1,_2)_4 + x_(1,_3)_4 + x_(2,_2)_4 <= 4\n",
       "\n",
       "_C148: x_(0,_2)_5 + x_(1,_1)_5 - 2 x_(1,_2)_5 + x_(1,_3)_5 + x_(2,_2)_5 >= 0\n",
       "\n",
       "_C149: x_(0,_2)_5 + x_(1,_1)_5 + 2 x_(1,_2)_5 + x_(1,_3)_5 + x_(2,_2)_5 <= 4\n",
       "\n",
       "_C150: x_(0,_3)_1 + x_(1,_2)_1 - 2 x_(1,_3)_1 + x_(1,_4)_1 + x_(2,_3)_1 >= 0\n",
       "\n",
       "_C151: x_(0,_3)_1 + x_(1,_2)_1 + 2 x_(1,_3)_1 + x_(1,_4)_1 + x_(2,_3)_1 <= 4\n",
       "\n",
       "_C152: x_(0,_3)_2 + x_(1,_2)_2 - 2 x_(1,_3)_2 + x_(1,_4)_2 + x_(2,_3)_2 >= 0\n",
       "\n",
       "_C153: x_(0,_3)_2 + x_(1,_2)_2 + 2 x_(1,_3)_2 + x_(1,_4)_2 + x_(2,_3)_2 <= 4\n",
       "\n",
       "_C154: x_(0,_3)_3 + x_(1,_2)_3 - 2 x_(1,_3)_3 + x_(1,_4)_3 + x_(2,_3)_3 >= 0\n",
       "\n",
       "_C155: x_(0,_3)_3 + x_(1,_2)_3 + 2 x_(1,_3)_3 + x_(1,_4)_3 + x_(2,_3)_3 <= 4\n",
       "\n",
       "_C156: x_(0,_3)_4 + x_(1,_2)_4 - 2 x_(1,_3)_4 + x_(1,_4)_4 + x_(2,_3)_4 >= 0\n",
       "\n",
       "_C157: x_(0,_3)_4 + x_(1,_2)_4 + 2 x_(1,_3)_4 + x_(1,_4)_4 + x_(2,_3)_4 <= 4\n",
       "\n",
       "_C158: x_(0,_3)_5 + x_(1,_2)_5 - 2 x_(1,_3)_5 + x_(1,_4)_5 + x_(2,_3)_5 >= 0\n",
       "\n",
       "_C159: x_(0,_3)_5 + x_(1,_2)_5 + 2 x_(1,_3)_5 + x_(1,_4)_5 + x_(2,_3)_5 <= 4\n",
       "\n",
       "_C160: x_(0,_6)_1 + x_(1,_5)_1 - 2 x_(1,_6)_1 + x_(2,_6)_1 >= 0\n",
       "\n",
       "_C161: x_(0,_6)_1 + x_(1,_5)_1 + 2 x_(1,_6)_1 + x_(2,_6)_1 <= 4\n",
       "\n",
       "_C162: x_(0,_6)_2 + x_(1,_5)_2 - 2 x_(1,_6)_2 + x_(2,_6)_2 >= 0\n",
       "\n",
       "_C163: x_(0,_6)_2 + x_(1,_5)_2 + 2 x_(1,_6)_2 + x_(2,_6)_2 <= 4\n",
       "\n",
       "_C164: x_(0,_6)_3 + x_(1,_5)_3 - 2 x_(1,_6)_3 + x_(2,_6)_3 >= 0\n",
       "\n",
       "_C165: x_(0,_6)_3 + x_(1,_5)_3 + 2 x_(1,_6)_3 + x_(2,_6)_3 <= 4\n",
       "\n",
       "_C166: x_(0,_6)_4 + x_(1,_5)_4 - 2 x_(1,_6)_4 + x_(2,_6)_4 >= 0\n",
       "\n",
       "_C167: x_(0,_6)_4 + x_(1,_5)_4 + 2 x_(1,_6)_4 + x_(2,_6)_4 <= 4\n",
       "\n",
       "_C168: x_(0,_6)_5 + x_(1,_5)_5 - 2 x_(1,_6)_5 + x_(2,_6)_5 >= 0\n",
       "\n",
       "_C169: x_(0,_6)_5 + x_(1,_5)_5 + 2 x_(1,_6)_5 + x_(2,_6)_5 <= 4\n",
       "\n",
       "_C170: x_(1,_0)_1 - 2 x_(2,_0)_1 + x_(2,_1)_1 + x_(3,_0)_1 >= 0\n",
       "\n",
       "_C171: x_(1,_0)_1 + 2 x_(2,_0)_1 + x_(2,_1)_1 + x_(3,_0)_1 <= 4\n",
       "\n",
       "_C172: x_(1,_0)_2 - 2 x_(2,_0)_2 + x_(2,_1)_2 + x_(3,_0)_2 >= 0\n",
       "\n",
       "_C173: x_(1,_0)_2 + 2 x_(2,_0)_2 + x_(2,_1)_2 + x_(3,_0)_2 <= 4\n",
       "\n",
       "_C174: x_(1,_0)_3 - 2 x_(2,_0)_3 + x_(2,_1)_3 + x_(3,_0)_3 >= 0\n",
       "\n",
       "_C175: x_(1,_0)_3 + 2 x_(2,_0)_3 + x_(2,_1)_3 + x_(3,_0)_3 <= 4\n",
       "\n",
       "_C176: x_(1,_0)_4 - 2 x_(2,_0)_4 + x_(2,_1)_4 + x_(3,_0)_4 >= 0\n",
       "\n",
       "_C177: x_(1,_0)_4 + 2 x_(2,_0)_4 + x_(2,_1)_4 + x_(3,_0)_4 <= 4\n",
       "\n",
       "_C178: x_(1,_0)_5 - 2 x_(2,_0)_5 + x_(2,_1)_5 + x_(3,_0)_5 >= 0\n",
       "\n",
       "_C179: x_(1,_0)_5 + 2 x_(2,_0)_5 + x_(2,_1)_5 + x_(3,_0)_5 <= 4\n",
       "\n",
       "_C180: x_(1,_1)_1 + x_(2,_0)_1 - 2 x_(2,_1)_1 + x_(2,_2)_1 + x_(3,_1)_1 >= 0\n",
       "\n",
       "_C181: x_(1,_1)_1 + x_(2,_0)_1 + 2 x_(2,_1)_1 + x_(2,_2)_1 + x_(3,_1)_1 <= 4\n",
       "\n",
       "_C182: x_(1,_1)_2 + x_(2,_0)_2 - 2 x_(2,_1)_2 + x_(2,_2)_2 + x_(3,_1)_2 >= 0\n",
       "\n",
       "_C183: x_(1,_1)_2 + x_(2,_0)_2 + 2 x_(2,_1)_2 + x_(2,_2)_2 + x_(3,_1)_2 <= 4\n",
       "\n",
       "_C184: x_(1,_1)_3 + x_(2,_0)_3 - 2 x_(2,_1)_3 + x_(2,_2)_3 + x_(3,_1)_3 >= 0\n",
       "\n",
       "_C185: x_(1,_1)_3 + x_(2,_0)_3 + 2 x_(2,_1)_3 + x_(2,_2)_3 + x_(3,_1)_3 <= 4\n",
       "\n",
       "_C186: x_(1,_1)_4 + x_(2,_0)_4 - 2 x_(2,_1)_4 + x_(2,_2)_4 + x_(3,_1)_4 >= 0\n",
       "\n",
       "_C187: x_(1,_1)_4 + x_(2,_0)_4 + 2 x_(2,_1)_4 + x_(2,_2)_4 + x_(3,_1)_4 <= 4\n",
       "\n",
       "_C188: x_(1,_1)_5 + x_(2,_0)_5 - 2 x_(2,_1)_5 + x_(2,_2)_5 + x_(3,_1)_5 >= 0\n",
       "\n",
       "_C189: x_(1,_1)_5 + x_(2,_0)_5 + 2 x_(2,_1)_5 + x_(2,_2)_5 + x_(3,_1)_5 <= 4\n",
       "\n",
       "_C190: x_(1,_2)_1 + x_(2,_1)_1 - 2 x_(2,_2)_1 + x_(2,_3)_1 + x_(3,_2)_1 >= 0\n",
       "\n",
       "_C191: x_(1,_2)_1 + x_(2,_1)_1 + 2 x_(2,_2)_1 + x_(2,_3)_1 + x_(3,_2)_1 <= 4\n",
       "\n",
       "_C192: x_(1,_2)_2 + x_(2,_1)_2 - 2 x_(2,_2)_2 + x_(2,_3)_2 + x_(3,_2)_2 >= 0\n",
       "\n",
       "_C193: x_(1,_2)_2 + x_(2,_1)_2 + 2 x_(2,_2)_2 + x_(2,_3)_2 + x_(3,_2)_2 <= 4\n",
       "\n",
       "_C194: x_(1,_2)_3 + x_(2,_1)_3 - 2 x_(2,_2)_3 + x_(2,_3)_3 + x_(3,_2)_3 >= 0\n",
       "\n",
       "_C195: x_(1,_2)_3 + x_(2,_1)_3 + 2 x_(2,_2)_3 + x_(2,_3)_3 + x_(3,_2)_3 <= 4\n",
       "\n",
       "_C196: x_(1,_2)_4 + x_(2,_1)_4 - 2 x_(2,_2)_4 + x_(2,_3)_4 + x_(3,_2)_4 >= 0\n",
       "\n",
       "_C197: x_(1,_2)_4 + x_(2,_1)_4 + 2 x_(2,_2)_4 + x_(2,_3)_4 + x_(3,_2)_4 <= 4\n",
       "\n",
       "_C198: x_(1,_2)_5 + x_(2,_1)_5 - 2 x_(2,_2)_5 + x_(2,_3)_5 + x_(3,_2)_5 >= 0\n",
       "\n",
       "_C199: x_(1,_2)_5 + x_(2,_1)_5 + 2 x_(2,_2)_5 + x_(2,_3)_5 + x_(3,_2)_5 <= 4\n",
       "\n",
       "_C200: x_(1,_5)_1 + x_(2,_4)_1 - 2 x_(2,_5)_1 + x_(2,_6)_1 + x_(3,_5)_1 >= 0\n",
       "\n",
       "_C201: x_(1,_5)_1 + x_(2,_4)_1 + 2 x_(2,_5)_1 + x_(2,_6)_1 + x_(3,_5)_1 <= 4\n",
       "\n",
       "_C202: x_(1,_5)_2 + x_(2,_4)_2 - 2 x_(2,_5)_2 + x_(2,_6)_2 + x_(3,_5)_2 >= 0\n",
       "\n",
       "_C203: x_(1,_5)_2 + x_(2,_4)_2 + 2 x_(2,_5)_2 + x_(2,_6)_2 + x_(3,_5)_2 <= 4\n",
       "\n",
       "_C204: x_(1,_5)_3 + x_(2,_4)_3 - 2 x_(2,_5)_3 + x_(2,_6)_3 + x_(3,_5)_3 >= 0\n",
       "\n",
       "_C205: x_(1,_5)_3 + x_(2,_4)_3 + 2 x_(2,_5)_3 + x_(2,_6)_3 + x_(3,_5)_3 <= 4\n",
       "\n",
       "_C206: x_(1,_5)_4 + x_(2,_4)_4 - 2 x_(2,_5)_4 + x_(2,_6)_4 + x_(3,_5)_4 >= 0\n",
       "\n",
       "_C207: x_(1,_5)_4 + x_(2,_4)_4 + 2 x_(2,_5)_4 + x_(2,_6)_4 + x_(3,_5)_4 <= 4\n",
       "\n",
       "_C208: x_(1,_5)_5 + x_(2,_4)_5 - 2 x_(2,_5)_5 + x_(2,_6)_5 + x_(3,_5)_5 >= 0\n",
       "\n",
       "_C209: x_(1,_5)_5 + x_(2,_4)_5 + 2 x_(2,_5)_5 + x_(2,_6)_5 + x_(3,_5)_5 <= 4\n",
       "\n",
       "_C210: x_(1,_6)_1 + x_(2,_5)_1 - 2 x_(2,_6)_1 + x_(3,_6)_1 >= 0\n",
       "\n",
       "_C211: x_(1,_6)_1 + x_(2,_5)_1 + 2 x_(2,_6)_1 + x_(3,_6)_1 <= 4\n",
       "\n",
       "_C212: x_(1,_6)_2 + x_(2,_5)_2 - 2 x_(2,_6)_2 + x_(3,_6)_2 >= 0\n",
       "\n",
       "_C213: x_(1,_6)_2 + x_(2,_5)_2 + 2 x_(2,_6)_2 + x_(3,_6)_2 <= 4\n",
       "\n",
       "_C214: x_(1,_6)_3 + x_(2,_5)_3 - 2 x_(2,_6)_3 + x_(3,_6)_3 >= 0\n",
       "\n",
       "_C215: x_(1,_6)_3 + x_(2,_5)_3 + 2 x_(2,_6)_3 + x_(3,_6)_3 <= 4\n",
       "\n",
       "_C216: x_(1,_6)_4 + x_(2,_5)_4 - 2 x_(2,_6)_4 + x_(3,_6)_4 >= 0\n",
       "\n",
       "_C217: x_(1,_6)_4 + x_(2,_5)_4 + 2 x_(2,_6)_4 + x_(3,_6)_4 <= 4\n",
       "\n",
       "_C218: x_(1,_6)_5 + x_(2,_5)_5 - 2 x_(2,_6)_5 + x_(3,_6)_5 >= 0\n",
       "\n",
       "_C219: x_(1,_6)_5 + x_(2,_5)_5 + 2 x_(2,_6)_5 + x_(3,_6)_5 <= 4\n",
       "\n",
       "_C220: x_(2,_0)_1 - 2 x_(3,_0)_1 + x_(3,_1)_1 + x_(4,_0)_1 >= 0\n",
       "\n",
       "_C221: x_(2,_0)_1 + 2 x_(3,_0)_1 + x_(3,_1)_1 + x_(4,_0)_1 <= 4\n",
       "\n",
       "_C222: x_(2,_0)_2 - 2 x_(3,_0)_2 + x_(3,_1)_2 + x_(4,_0)_2 >= 0\n",
       "\n",
       "_C223: x_(2,_0)_2 + 2 x_(3,_0)_2 + x_(3,_1)_2 + x_(4,_0)_2 <= 4\n",
       "\n",
       "_C224: x_(2,_0)_3 - 2 x_(3,_0)_3 + x_(3,_1)_3 + x_(4,_0)_3 >= 0\n",
       "\n",
       "_C225: x_(2,_0)_3 + 2 x_(3,_0)_3 + x_(3,_1)_3 + x_(4,_0)_3 <= 4\n",
       "\n",
       "_C226: x_(2,_0)_4 - 2 x_(3,_0)_4 + x_(3,_1)_4 + x_(4,_0)_4 >= 0\n",
       "\n",
       "_C227: x_(2,_0)_4 + 2 x_(3,_0)_4 + x_(3,_1)_4 + x_(4,_0)_4 <= 4\n",
       "\n",
       "_C228: x_(2,_0)_5 - 2 x_(3,_0)_5 + x_(3,_1)_5 + x_(4,_0)_5 >= 0\n",
       "\n",
       "_C229: x_(2,_0)_5 + 2 x_(3,_0)_5 + x_(3,_1)_5 + x_(4,_0)_5 <= 4\n",
       "\n",
       "_C230: x_(2,_1)_1 + x_(3,_0)_1 - 2 x_(3,_1)_1 + x_(3,_2)_1 + x_(4,_1)_1 >= 0\n",
       "\n",
       "_C231: x_(2,_1)_1 + x_(3,_0)_1 + 2 x_(3,_1)_1 + x_(3,_2)_1 + x_(4,_1)_1 <= 4\n",
       "\n",
       "_C232: x_(2,_1)_2 + x_(3,_0)_2 - 2 x_(3,_1)_2 + x_(3,_2)_2 + x_(4,_1)_2 >= 0\n",
       "\n",
       "_C233: x_(2,_1)_2 + x_(3,_0)_2 + 2 x_(3,_1)_2 + x_(3,_2)_2 + x_(4,_1)_2 <= 4\n",
       "\n",
       "_C234: x_(2,_1)_3 + x_(3,_0)_3 - 2 x_(3,_1)_3 + x_(3,_2)_3 + x_(4,_1)_3 >= 0\n",
       "\n",
       "_C235: x_(2,_1)_3 + x_(3,_0)_3 + 2 x_(3,_1)_3 + x_(3,_2)_3 + x_(4,_1)_3 <= 4\n",
       "\n",
       "_C236: x_(2,_1)_4 + x_(3,_0)_4 - 2 x_(3,_1)_4 + x_(3,_2)_4 + x_(4,_1)_4 >= 0\n",
       "\n",
       "_C237: x_(2,_1)_4 + x_(3,_0)_4 + 2 x_(3,_1)_4 + x_(3,_2)_4 + x_(4,_1)_4 <= 4\n",
       "\n",
       "_C238: x_(2,_1)_5 + x_(3,_0)_5 - 2 x_(3,_1)_5 + x_(3,_2)_5 + x_(4,_1)_5 >= 0\n",
       "\n",
       "_C239: x_(2,_1)_5 + x_(3,_0)_5 + 2 x_(3,_1)_5 + x_(3,_2)_5 + x_(4,_1)_5 <= 4\n",
       "\n",
       "_C240: x_(2,_2)_1 + x_(3,_1)_1 - 2 x_(3,_2)_1 + x_(3,_3)_1 + x_(4,_2)_1 >= 0\n",
       "\n",
       "_C241: x_(2,_2)_1 + x_(3,_1)_1 + 2 x_(3,_2)_1 + x_(3,_3)_1 + x_(4,_2)_1 <= 4\n",
       "\n",
       "_C242: x_(2,_2)_2 + x_(3,_1)_2 - 2 x_(3,_2)_2 + x_(3,_3)_2 + x_(4,_2)_2 >= 0\n",
       "\n",
       "_C243: x_(2,_2)_2 + x_(3,_1)_2 + 2 x_(3,_2)_2 + x_(3,_3)_2 + x_(4,_2)_2 <= 4\n",
       "\n",
       "_C244: x_(2,_2)_3 + x_(3,_1)_3 - 2 x_(3,_2)_3 + x_(3,_3)_3 + x_(4,_2)_3 >= 0\n",
       "\n",
       "_C245: x_(2,_2)_3 + x_(3,_1)_3 + 2 x_(3,_2)_3 + x_(3,_3)_3 + x_(4,_2)_3 <= 4\n",
       "\n",
       "_C246: x_(2,_2)_4 + x_(3,_1)_4 - 2 x_(3,_2)_4 + x_(3,_3)_4 + x_(4,_2)_4 >= 0\n",
       "\n",
       "_C247: x_(2,_2)_4 + x_(3,_1)_4 + 2 x_(3,_2)_4 + x_(3,_3)_4 + x_(4,_2)_4 <= 4\n",
       "\n",
       "_C248: x_(2,_2)_5 + x_(3,_1)_5 - 2 x_(3,_2)_5 + x_(3,_3)_5 + x_(4,_2)_5 >= 0\n",
       "\n",
       "_C249: x_(2,_2)_5 + x_(3,_1)_5 + 2 x_(3,_2)_5 + x_(3,_3)_5 + x_(4,_2)_5 <= 4\n",
       "\n",
       "_C250: x_(2,_4)_1 + x_(3,_3)_1 - 2 x_(3,_4)_1 + x_(3,_5)_1 + x_(4,_4)_1 >= 0\n",
       "\n",
       "_C251: x_(2,_4)_1 + x_(3,_3)_1 + 2 x_(3,_4)_1 + x_(3,_5)_1 + x_(4,_4)_1 <= 4\n",
       "\n",
       "_C252: x_(2,_4)_2 + x_(3,_3)_2 - 2 x_(3,_4)_2 + x_(3,_5)_2 + x_(4,_4)_2 >= 0\n",
       "\n",
       "_C253: x_(2,_4)_2 + x_(3,_3)_2 + 2 x_(3,_4)_2 + x_(3,_5)_2 + x_(4,_4)_2 <= 4\n",
       "\n",
       "_C254: x_(2,_4)_3 + x_(3,_3)_3 - 2 x_(3,_4)_3 + x_(3,_5)_3 + x_(4,_4)_3 >= 0\n",
       "\n",
       "_C255: x_(2,_4)_3 + x_(3,_3)_3 + 2 x_(3,_4)_3 + x_(3,_5)_3 + x_(4,_4)_3 <= 4\n",
       "\n",
       "_C256: x_(2,_4)_4 + x_(3,_3)_4 - 2 x_(3,_4)_4 + x_(3,_5)_4 + x_(4,_4)_4 >= 0\n",
       "\n",
       "_C257: x_(2,_4)_4 + x_(3,_3)_4 + 2 x_(3,_4)_4 + x_(3,_5)_4 + x_(4,_4)_4 <= 4\n",
       "\n",
       "_C258: x_(2,_4)_5 + x_(3,_3)_5 - 2 x_(3,_4)_5 + x_(3,_5)_5 + x_(4,_4)_5 >= 0\n",
       "\n",
       "_C259: x_(2,_4)_5 + x_(3,_3)_5 + 2 x_(3,_4)_5 + x_(3,_5)_5 + x_(4,_4)_5 <= 4\n",
       "\n",
       "_C260: x_(2,_5)_1 + x_(3,_4)_1 - 2 x_(3,_5)_1 + x_(3,_6)_1 + x_(4,_5)_1 >= 0\n",
       "\n",
       "_C261: x_(2,_5)_1 + x_(3,_4)_1 + 2 x_(3,_5)_1 + x_(3,_6)_1 + x_(4,_5)_1 <= 4\n",
       "\n",
       "_C262: x_(2,_5)_2 + x_(3,_4)_2 - 2 x_(3,_5)_2 + x_(3,_6)_2 + x_(4,_5)_2 >= 0\n",
       "\n",
       "_C263: x_(2,_5)_2 + x_(3,_4)_2 + 2 x_(3,_5)_2 + x_(3,_6)_2 + x_(4,_5)_2 <= 4\n",
       "\n",
       "_C264: x_(2,_5)_3 + x_(3,_4)_3 - 2 x_(3,_5)_3 + x_(3,_6)_3 + x_(4,_5)_3 >= 0\n",
       "\n",
       "_C265: x_(2,_5)_3 + x_(3,_4)_3 + 2 x_(3,_5)_3 + x_(3,_6)_3 + x_(4,_5)_3 <= 4\n",
       "\n",
       "_C266: x_(2,_5)_4 + x_(3,_4)_4 - 2 x_(3,_5)_4 + x_(3,_6)_4 + x_(4,_5)_4 >= 0\n",
       "\n",
       "_C267: x_(2,_5)_4 + x_(3,_4)_4 + 2 x_(3,_5)_4 + x_(3,_6)_4 + x_(4,_5)_4 <= 4\n",
       "\n",
       "_C268: x_(2,_5)_5 + x_(3,_4)_5 - 2 x_(3,_5)_5 + x_(3,_6)_5 + x_(4,_5)_5 >= 0\n",
       "\n",
       "_C269: x_(2,_5)_5 + x_(3,_4)_5 + 2 x_(3,_5)_5 + x_(3,_6)_5 + x_(4,_5)_5 <= 4\n",
       "\n",
       "_C270: x_(2,_6)_1 + x_(3,_5)_1 - 2 x_(3,_6)_1 + x_(4,_6)_1 >= 0\n",
       "\n",
       "_C271: x_(2,_6)_1 + x_(3,_5)_1 + 2 x_(3,_6)_1 + x_(4,_6)_1 <= 4\n",
       "\n",
       "_C272: x_(2,_6)_2 + x_(3,_5)_2 - 2 x_(3,_6)_2 + x_(4,_6)_2 >= 0\n",
       "\n",
       "_C273: x_(2,_6)_2 + x_(3,_5)_2 + 2 x_(3,_6)_2 + x_(4,_6)_2 <= 4\n",
       "\n",
       "_C274: x_(2,_6)_3 + x_(3,_5)_3 - 2 x_(3,_6)_3 + x_(4,_6)_3 >= 0\n",
       "\n",
       "_C275: x_(2,_6)_3 + x_(3,_5)_3 + 2 x_(3,_6)_3 + x_(4,_6)_3 <= 4\n",
       "\n",
       "_C276: x_(2,_6)_4 + x_(3,_5)_4 - 2 x_(3,_6)_4 + x_(4,_6)_4 >= 0\n",
       "\n",
       "_C277: x_(2,_6)_4 + x_(3,_5)_4 + 2 x_(3,_6)_4 + x_(4,_6)_4 <= 4\n",
       "\n",
       "_C278: x_(2,_6)_5 + x_(3,_5)_5 - 2 x_(3,_6)_5 + x_(4,_6)_5 >= 0\n",
       "\n",
       "_C279: x_(2,_6)_5 + x_(3,_5)_5 + 2 x_(3,_6)_5 + x_(4,_6)_5 <= 4\n",
       "\n",
       "_C280: x_(3,_0)_1 - 2 x_(4,_0)_1 + x_(4,_1)_1 + x_(5,_0)_1 >= 0\n",
       "\n",
       "_C281: x_(3,_0)_1 + 2 x_(4,_0)_1 + x_(4,_1)_1 + x_(5,_0)_1 <= 4\n",
       "\n",
       "_C282: x_(3,_0)_2 - 2 x_(4,_0)_2 + x_(4,_1)_2 + x_(5,_0)_2 >= 0\n",
       "\n",
       "_C283: x_(3,_0)_2 + 2 x_(4,_0)_2 + x_(4,_1)_2 + x_(5,_0)_2 <= 4\n",
       "\n",
       "_C284: x_(3,_0)_3 - 2 x_(4,_0)_3 + x_(4,_1)_3 + x_(5,_0)_3 >= 0\n",
       "\n",
       "_C285: x_(3,_0)_3 + 2 x_(4,_0)_3 + x_(4,_1)_3 + x_(5,_0)_3 <= 4\n",
       "\n",
       "_C286: x_(3,_0)_4 - 2 x_(4,_0)_4 + x_(4,_1)_4 + x_(5,_0)_4 >= 0\n",
       "\n",
       "_C287: x_(3,_0)_4 + 2 x_(4,_0)_4 + x_(4,_1)_4 + x_(5,_0)_4 <= 4\n",
       "\n",
       "_C288: x_(3,_0)_5 - 2 x_(4,_0)_5 + x_(4,_1)_5 + x_(5,_0)_5 >= 0\n",
       "\n",
       "_C289: x_(3,_0)_5 + 2 x_(4,_0)_5 + x_(4,_1)_5 + x_(5,_0)_5 <= 4\n",
       "\n",
       "_C290: x_(3,_1)_1 + x_(4,_0)_1 - 2 x_(4,_1)_1 + x_(4,_2)_1 + x_(5,_1)_1 >= 0\n",
       "\n",
       "_C291: x_(3,_1)_1 + x_(4,_0)_1 + 2 x_(4,_1)_1 + x_(4,_2)_1 + x_(5,_1)_1 <= 4\n",
       "\n",
       "_C292: x_(3,_1)_2 + x_(4,_0)_2 - 2 x_(4,_1)_2 + x_(4,_2)_2 + x_(5,_1)_2 >= 0\n",
       "\n",
       "_C293: x_(3,_1)_2 + x_(4,_0)_2 + 2 x_(4,_1)_2 + x_(4,_2)_2 + x_(5,_1)_2 <= 4\n",
       "\n",
       "_C294: x_(3,_1)_3 + x_(4,_0)_3 - 2 x_(4,_1)_3 + x_(4,_2)_3 + x_(5,_1)_3 >= 0\n",
       "\n",
       "_C295: x_(3,_1)_3 + x_(4,_0)_3 + 2 x_(4,_1)_3 + x_(4,_2)_3 + x_(5,_1)_3 <= 4\n",
       "\n",
       "_C296: x_(3,_1)_4 + x_(4,_0)_4 - 2 x_(4,_1)_4 + x_(4,_2)_4 + x_(5,_1)_4 >= 0\n",
       "\n",
       "_C297: x_(3,_1)_4 + x_(4,_0)_4 + 2 x_(4,_1)_4 + x_(4,_2)_4 + x_(5,_1)_4 <= 4\n",
       "\n",
       "_C298: x_(3,_1)_5 + x_(4,_0)_5 - 2 x_(4,_1)_5 + x_(4,_2)_5 + x_(5,_1)_5 >= 0\n",
       "\n",
       "_C299: x_(3,_1)_5 + x_(4,_0)_5 + 2 x_(4,_1)_5 + x_(4,_2)_5 + x_(5,_1)_5 <= 4\n",
       "\n",
       "_C300: x_(3,_2)_1 + x_(4,_1)_1 - 2 x_(4,_2)_1 + x_(4,_3)_1 + x_(5,_2)_1 >= 0\n",
       "\n",
       "_C301: x_(3,_2)_1 + x_(4,_1)_1 + 2 x_(4,_2)_1 + x_(4,_3)_1 + x_(5,_2)_1 <= 4\n",
       "\n",
       "_C302: x_(3,_2)_2 + x_(4,_1)_2 - 2 x_(4,_2)_2 + x_(4,_3)_2 + x_(5,_2)_2 >= 0\n",
       "\n",
       "_C303: x_(3,_2)_2 + x_(4,_1)_2 + 2 x_(4,_2)_2 + x_(4,_3)_2 + x_(5,_2)_2 <= 4\n",
       "\n",
       "_C304: x_(3,_2)_3 + x_(4,_1)_3 - 2 x_(4,_2)_3 + x_(4,_3)_3 + x_(5,_2)_3 >= 0\n",
       "\n",
       "_C305: x_(3,_2)_3 + x_(4,_1)_3 + 2 x_(4,_2)_3 + x_(4,_3)_3 + x_(5,_2)_3 <= 4\n",
       "\n",
       "_C306: x_(3,_2)_4 + x_(4,_1)_4 - 2 x_(4,_2)_4 + x_(4,_3)_4 + x_(5,_2)_4 >= 0\n",
       "\n",
       "_C307: x_(3,_2)_4 + x_(4,_1)_4 + 2 x_(4,_2)_4 + x_(4,_3)_4 + x_(5,_2)_4 <= 4\n",
       "\n",
       "_C308: x_(3,_2)_5 + x_(4,_1)_5 - 2 x_(4,_2)_5 + x_(4,_3)_5 + x_(5,_2)_5 >= 0\n",
       "\n",
       "_C309: x_(3,_2)_5 + x_(4,_1)_5 + 2 x_(4,_2)_5 + x_(4,_3)_5 + x_(5,_2)_5 <= 4\n",
       "\n",
       "_C310: x_(3,_3)_1 + x_(4,_2)_1 - 2 x_(4,_3)_1 + x_(4,_4)_1 + x_(5,_3)_1 >= 0\n",
       "\n",
       "_C311: x_(3,_3)_1 + x_(4,_2)_1 + 2 x_(4,_3)_1 + x_(4,_4)_1 + x_(5,_3)_1 <= 4\n",
       "\n",
       "_C312: x_(3,_3)_2 + x_(4,_2)_2 - 2 x_(4,_3)_2 + x_(4,_4)_2 + x_(5,_3)_2 >= 0\n",
       "\n",
       "_C313: x_(3,_3)_2 + x_(4,_2)_2 + 2 x_(4,_3)_2 + x_(4,_4)_2 + x_(5,_3)_2 <= 4\n",
       "\n",
       "_C314: x_(3,_3)_3 + x_(4,_2)_3 - 2 x_(4,_3)_3 + x_(4,_4)_3 + x_(5,_3)_3 >= 0\n",
       "\n",
       "_C315: x_(3,_3)_3 + x_(4,_2)_3 + 2 x_(4,_3)_3 + x_(4,_4)_3 + x_(5,_3)_3 <= 4\n",
       "\n",
       "_C316: x_(3,_3)_4 + x_(4,_2)_4 - 2 x_(4,_3)_4 + x_(4,_4)_4 + x_(5,_3)_4 >= 0\n",
       "\n",
       "_C317: x_(3,_3)_4 + x_(4,_2)_4 + 2 x_(4,_3)_4 + x_(4,_4)_4 + x_(5,_3)_4 <= 4\n",
       "\n",
       "_C318: x_(3,_3)_5 + x_(4,_2)_5 - 2 x_(4,_3)_5 + x_(4,_4)_5 + x_(5,_3)_5 >= 0\n",
       "\n",
       "_C319: x_(3,_3)_5 + x_(4,_2)_5 + 2 x_(4,_3)_5 + x_(4,_4)_5 + x_(5,_3)_5 <= 4\n",
       "\n",
       "_C320: x_(3,_4)_1 + x_(4,_3)_1 - 2 x_(4,_4)_1 + x_(4,_5)_1 + x_(5,_4)_1 >= 0\n",
       "\n",
       "_C321: x_(3,_4)_1 + x_(4,_3)_1 + 2 x_(4,_4)_1 + x_(4,_5)_1 + x_(5,_4)_1 <= 4\n",
       "\n",
       "_C322: x_(3,_4)_2 + x_(4,_3)_2 - 2 x_(4,_4)_2 + x_(4,_5)_2 + x_(5,_4)_2 >= 0\n",
       "\n",
       "_C323: x_(3,_4)_2 + x_(4,_3)_2 + 2 x_(4,_4)_2 + x_(4,_5)_2 + x_(5,_4)_2 <= 4\n",
       "\n",
       "_C324: x_(3,_4)_3 + x_(4,_3)_3 - 2 x_(4,_4)_3 + x_(4,_5)_3 + x_(5,_4)_3 >= 0\n",
       "\n",
       "_C325: x_(3,_4)_3 + x_(4,_3)_3 + 2 x_(4,_4)_3 + x_(4,_5)_3 + x_(5,_4)_3 <= 4\n",
       "\n",
       "_C326: x_(3,_4)_4 + x_(4,_3)_4 - 2 x_(4,_4)_4 + x_(4,_5)_4 + x_(5,_4)_4 >= 0\n",
       "\n",
       "_C327: x_(3,_4)_4 + x_(4,_3)_4 + 2 x_(4,_4)_4 + x_(4,_5)_4 + x_(5,_4)_4 <= 4\n",
       "\n",
       "_C328: x_(3,_4)_5 + x_(4,_3)_5 - 2 x_(4,_4)_5 + x_(4,_5)_5 + x_(5,_4)_5 >= 0\n",
       "\n",
       "_C329: x_(3,_4)_5 + x_(4,_3)_5 + 2 x_(4,_4)_5 + x_(4,_5)_5 + x_(5,_4)_5 <= 4\n",
       "\n",
       "_C330: x_(3,_5)_1 + x_(4,_4)_1 - 2 x_(4,_5)_1 + x_(4,_6)_1 + x_(5,_5)_1 >= 0\n",
       "\n",
       "_C331: x_(3,_5)_1 + x_(4,_4)_1 + 2 x_(4,_5)_1 + x_(4,_6)_1 + x_(5,_5)_1 <= 4\n",
       "\n",
       "_C332: x_(3,_5)_2 + x_(4,_4)_2 - 2 x_(4,_5)_2 + x_(4,_6)_2 + x_(5,_5)_2 >= 0\n",
       "\n",
       "_C333: x_(3,_5)_2 + x_(4,_4)_2 + 2 x_(4,_5)_2 + x_(4,_6)_2 + x_(5,_5)_2 <= 4\n",
       "\n",
       "_C334: x_(3,_5)_3 + x_(4,_4)_3 - 2 x_(4,_5)_3 + x_(4,_6)_3 + x_(5,_5)_3 >= 0\n",
       "\n",
       "_C335: x_(3,_5)_3 + x_(4,_4)_3 + 2 x_(4,_5)_3 + x_(4,_6)_3 + x_(5,_5)_3 <= 4\n",
       "\n",
       "_C336: x_(3,_5)_4 + x_(4,_4)_4 - 2 x_(4,_5)_4 + x_(4,_6)_4 + x_(5,_5)_4 >= 0\n",
       "\n",
       "_C337: x_(3,_5)_4 + x_(4,_4)_4 + 2 x_(4,_5)_4 + x_(4,_6)_4 + x_(5,_5)_4 <= 4\n",
       "\n",
       "_C338: x_(3,_5)_5 + x_(4,_4)_5 - 2 x_(4,_5)_5 + x_(4,_6)_5 + x_(5,_5)_5 >= 0\n",
       "\n",
       "_C339: x_(3,_5)_5 + x_(4,_4)_5 + 2 x_(4,_5)_5 + x_(4,_6)_5 + x_(5,_5)_5 <= 4\n",
       "\n",
       "_C340: x_(3,_6)_1 + x_(4,_5)_1 - 2 x_(4,_6)_1 + x_(5,_6)_1 >= 0\n",
       "\n",
       "_C341: x_(3,_6)_1 + x_(4,_5)_1 + 2 x_(4,_6)_1 + x_(5,_6)_1 <= 4\n",
       "\n",
       "_C342: x_(3,_6)_2 + x_(4,_5)_2 - 2 x_(4,_6)_2 + x_(5,_6)_2 >= 0\n",
       "\n",
       "_C343: x_(3,_6)_2 + x_(4,_5)_2 + 2 x_(4,_6)_2 + x_(5,_6)_2 <= 4\n",
       "\n",
       "_C344: x_(3,_6)_3 + x_(4,_5)_3 - 2 x_(4,_6)_3 + x_(5,_6)_3 >= 0\n",
       "\n",
       "_C345: x_(3,_6)_3 + x_(4,_5)_3 + 2 x_(4,_6)_3 + x_(5,_6)_3 <= 4\n",
       "\n",
       "_C346: x_(3,_6)_4 + x_(4,_5)_4 - 2 x_(4,_6)_4 + x_(5,_6)_4 >= 0\n",
       "\n",
       "_C347: x_(3,_6)_4 + x_(4,_5)_4 + 2 x_(4,_6)_4 + x_(5,_6)_4 <= 4\n",
       "\n",
       "_C348: x_(3,_6)_5 + x_(4,_5)_5 - 2 x_(4,_6)_5 + x_(5,_6)_5 >= 0\n",
       "\n",
       "_C349: x_(3,_6)_5 + x_(4,_5)_5 + 2 x_(4,_6)_5 + x_(5,_6)_5 <= 4\n",
       "\n",
       "_C350: x_(4,_0)_1 - 2 x_(5,_0)_1 + x_(5,_1)_1 + x_(6,_0)_1 >= 0\n",
       "\n",
       "_C351: x_(4,_0)_1 + 2 x_(5,_0)_1 + x_(5,_1)_1 + x_(6,_0)_1 <= 4\n",
       "\n",
       "_C352: x_(4,_0)_2 - 2 x_(5,_0)_2 + x_(5,_1)_2 + x_(6,_0)_2 >= 0\n",
       "\n",
       "_C353: x_(4,_0)_2 + 2 x_(5,_0)_2 + x_(5,_1)_2 + x_(6,_0)_2 <= 4\n",
       "\n",
       "_C354: x_(4,_0)_3 - 2 x_(5,_0)_3 + x_(5,_1)_3 + x_(6,_0)_3 >= 0\n",
       "\n",
       "_C355: x_(4,_0)_3 + 2 x_(5,_0)_3 + x_(5,_1)_3 + x_(6,_0)_3 <= 4\n",
       "\n",
       "_C356: x_(4,_0)_4 - 2 x_(5,_0)_4 + x_(5,_1)_4 + x_(6,_0)_4 >= 0\n",
       "\n",
       "_C357: x_(4,_0)_4 + 2 x_(5,_0)_4 + x_(5,_1)_4 + x_(6,_0)_4 <= 4\n",
       "\n",
       "_C358: x_(4,_0)_5 - 2 x_(5,_0)_5 + x_(5,_1)_5 + x_(6,_0)_5 >= 0\n",
       "\n",
       "_C359: x_(4,_0)_5 + 2 x_(5,_0)_5 + x_(5,_1)_5 + x_(6,_0)_5 <= 4\n",
       "\n",
       "_C360: x_(4,_1)_1 + x_(5,_0)_1 - 2 x_(5,_1)_1 + x_(5,_2)_1 + x_(6,_1)_1 >= 0\n",
       "\n",
       "_C361: x_(4,_1)_1 + x_(5,_0)_1 + 2 x_(5,_1)_1 + x_(5,_2)_1 + x_(6,_1)_1 <= 4\n",
       "\n",
       "_C362: x_(4,_1)_2 + x_(5,_0)_2 - 2 x_(5,_1)_2 + x_(5,_2)_2 + x_(6,_1)_2 >= 0\n",
       "\n",
       "_C363: x_(4,_1)_2 + x_(5,_0)_2 + 2 x_(5,_1)_2 + x_(5,_2)_2 + x_(6,_1)_2 <= 4\n",
       "\n",
       "_C364: x_(4,_1)_3 + x_(5,_0)_3 - 2 x_(5,_1)_3 + x_(5,_2)_3 + x_(6,_1)_3 >= 0\n",
       "\n",
       "_C365: x_(4,_1)_3 + x_(5,_0)_3 + 2 x_(5,_1)_3 + x_(5,_2)_3 + x_(6,_1)_3 <= 4\n",
       "\n",
       "_C366: x_(4,_1)_4 + x_(5,_0)_4 - 2 x_(5,_1)_4 + x_(5,_2)_4 + x_(6,_1)_4 >= 0\n",
       "\n",
       "_C367: x_(4,_1)_4 + x_(5,_0)_4 + 2 x_(5,_1)_4 + x_(5,_2)_4 + x_(6,_1)_4 <= 4\n",
       "\n",
       "_C368: x_(4,_1)_5 + x_(5,_0)_5 - 2 x_(5,_1)_5 + x_(5,_2)_5 + x_(6,_1)_5 >= 0\n",
       "\n",
       "_C369: x_(4,_1)_5 + x_(5,_0)_5 + 2 x_(5,_1)_5 + x_(5,_2)_5 + x_(6,_1)_5 <= 4\n",
       "\n",
       "_C370: x_(4,_3)_1 + x_(5,_2)_1 - 2 x_(5,_3)_1 + x_(5,_4)_1 + x_(6,_3)_1 >= 0\n",
       "\n",
       "_C371: x_(4,_3)_1 + x_(5,_2)_1 + 2 x_(5,_3)_1 + x_(5,_4)_1 + x_(6,_3)_1 <= 4\n",
       "\n",
       "_C372: x_(4,_3)_2 + x_(5,_2)_2 - 2 x_(5,_3)_2 + x_(5,_4)_2 + x_(6,_3)_2 >= 0\n",
       "\n",
       "_C373: x_(4,_3)_2 + x_(5,_2)_2 + 2 x_(5,_3)_2 + x_(5,_4)_2 + x_(6,_3)_2 <= 4\n",
       "\n",
       "_C374: x_(4,_3)_3 + x_(5,_2)_3 - 2 x_(5,_3)_3 + x_(5,_4)_3 + x_(6,_3)_3 >= 0\n",
       "\n",
       "_C375: x_(4,_3)_3 + x_(5,_2)_3 + 2 x_(5,_3)_3 + x_(5,_4)_3 + x_(6,_3)_3 <= 4\n",
       "\n",
       "_C376: x_(4,_3)_4 + x_(5,_2)_4 - 2 x_(5,_3)_4 + x_(5,_4)_4 + x_(6,_3)_4 >= 0\n",
       "\n",
       "_C377: x_(4,_3)_4 + x_(5,_2)_4 + 2 x_(5,_3)_4 + x_(5,_4)_4 + x_(6,_3)_4 <= 4\n",
       "\n",
       "_C378: x_(4,_3)_5 + x_(5,_2)_5 - 2 x_(5,_3)_5 + x_(5,_4)_5 + x_(6,_3)_5 >= 0\n",
       "\n",
       "_C379: x_(4,_3)_5 + x_(5,_2)_5 + 2 x_(5,_3)_5 + x_(5,_4)_5 + x_(6,_3)_5 <= 4\n",
       "\n",
       "_C380: x_(4,_4)_1 + x_(5,_3)_1 - 2 x_(5,_4)_1 + x_(5,_5)_1 + x_(6,_4)_1 >= 0\n",
       "\n",
       "_C381: x_(4,_4)_1 + x_(5,_3)_1 + 2 x_(5,_4)_1 + x_(5,_5)_1 + x_(6,_4)_1 <= 4\n",
       "\n",
       "_C382: x_(4,_4)_2 + x_(5,_3)_2 - 2 x_(5,_4)_2 + x_(5,_5)_2 + x_(6,_4)_2 >= 0\n",
       "\n",
       "_C383: x_(4,_4)_2 + x_(5,_3)_2 + 2 x_(5,_4)_2 + x_(5,_5)_2 + x_(6,_4)_2 <= 4\n",
       "\n",
       "_C384: x_(4,_4)_3 + x_(5,_3)_3 - 2 x_(5,_4)_3 + x_(5,_5)_3 + x_(6,_4)_3 >= 0\n",
       "\n",
       "_C385: x_(4,_4)_3 + x_(5,_3)_3 + 2 x_(5,_4)_3 + x_(5,_5)_3 + x_(6,_4)_3 <= 4\n",
       "\n",
       "_C386: x_(4,_4)_4 + x_(5,_3)_4 - 2 x_(5,_4)_4 + x_(5,_5)_4 + x_(6,_4)_4 >= 0\n",
       "\n",
       "_C387: x_(4,_4)_4 + x_(5,_3)_4 + 2 x_(5,_4)_4 + x_(5,_5)_4 + x_(6,_4)_4 <= 4\n",
       "\n",
       "_C388: x_(4,_4)_5 + x_(5,_3)_5 - 2 x_(5,_4)_5 + x_(5,_5)_5 + x_(6,_4)_5 >= 0\n",
       "\n",
       "_C389: x_(4,_4)_5 + x_(5,_3)_5 + 2 x_(5,_4)_5 + x_(5,_5)_5 + x_(6,_4)_5 <= 4\n",
       "\n",
       "_C390: x_(4,_5)_1 + x_(5,_4)_1 - 2 x_(5,_5)_1 + x_(5,_6)_1 + x_(6,_5)_1 >= 0\n",
       "\n",
       "_C391: x_(4,_5)_1 + x_(5,_4)_1 + 2 x_(5,_5)_1 + x_(5,_6)_1 + x_(6,_5)_1 <= 4\n",
       "\n",
       "_C392: x_(4,_5)_2 + x_(5,_4)_2 - 2 x_(5,_5)_2 + x_(5,_6)_2 + x_(6,_5)_2 >= 0\n",
       "\n",
       "_C393: x_(4,_5)_2 + x_(5,_4)_2 + 2 x_(5,_5)_2 + x_(5,_6)_2 + x_(6,_5)_2 <= 4\n",
       "\n",
       "_C394: x_(4,_5)_3 + x_(5,_4)_3 - 2 x_(5,_5)_3 + x_(5,_6)_3 + x_(6,_5)_3 >= 0\n",
       "\n",
       "_C395: x_(4,_5)_3 + x_(5,_4)_3 + 2 x_(5,_5)_3 + x_(5,_6)_3 + x_(6,_5)_3 <= 4\n",
       "\n",
       "_C396: x_(4,_5)_4 + x_(5,_4)_4 - 2 x_(5,_5)_4 + x_(5,_6)_4 + x_(6,_5)_4 >= 0\n",
       "\n",
       "_C397: x_(4,_5)_4 + x_(5,_4)_4 + 2 x_(5,_5)_4 + x_(5,_6)_4 + x_(6,_5)_4 <= 4\n",
       "\n",
       "_C398: x_(4,_5)_5 + x_(5,_4)_5 - 2 x_(5,_5)_5 + x_(5,_6)_5 + x_(6,_5)_5 >= 0\n",
       "\n",
       "_C399: x_(4,_5)_5 + x_(5,_4)_5 + 2 x_(5,_5)_5 + x_(5,_6)_5 + x_(6,_5)_5 <= 4\n",
       "\n",
       "_C400: x_(4,_6)_1 + x_(5,_5)_1 - 2 x_(5,_6)_1 + x_(6,_6)_1 >= 0\n",
       "\n",
       "_C401: x_(4,_6)_1 + x_(5,_5)_1 + 2 x_(5,_6)_1 + x_(6,_6)_1 <= 4\n",
       "\n",
       "_C402: x_(4,_6)_2 + x_(5,_5)_2 - 2 x_(5,_6)_2 + x_(6,_6)_2 >= 0\n",
       "\n",
       "_C403: x_(4,_6)_2 + x_(5,_5)_2 + 2 x_(5,_6)_2 + x_(6,_6)_2 <= 4\n",
       "\n",
       "_C404: x_(4,_6)_3 + x_(5,_5)_3 - 2 x_(5,_6)_3 + x_(6,_6)_3 >= 0\n",
       "\n",
       "_C405: x_(4,_6)_3 + x_(5,_5)_3 + 2 x_(5,_6)_3 + x_(6,_6)_3 <= 4\n",
       "\n",
       "_C406: x_(4,_6)_4 + x_(5,_5)_4 - 2 x_(5,_6)_4 + x_(6,_6)_4 >= 0\n",
       "\n",
       "_C407: x_(4,_6)_4 + x_(5,_5)_4 + 2 x_(5,_6)_4 + x_(6,_6)_4 <= 4\n",
       "\n",
       "_C408: x_(4,_6)_5 + x_(5,_5)_5 - 2 x_(5,_6)_5 + x_(6,_6)_5 >= 0\n",
       "\n",
       "_C409: x_(4,_6)_5 + x_(5,_5)_5 + 2 x_(5,_6)_5 + x_(6,_6)_5 <= 4\n",
       "\n",
       "_C410: x_(5,_1)_1 + x_(6,_0)_1 - 2 x_(6,_1)_1 + x_(6,_2)_1 >= 0\n",
       "\n",
       "_C411: x_(5,_1)_1 + x_(6,_0)_1 + 2 x_(6,_1)_1 + x_(6,_2)_1 <= 4\n",
       "\n",
       "_C412: x_(5,_1)_2 + x_(6,_0)_2 - 2 x_(6,_1)_2 + x_(6,_2)_2 >= 0\n",
       "\n",
       "_C413: x_(5,_1)_2 + x_(6,_0)_2 + 2 x_(6,_1)_2 + x_(6,_2)_2 <= 4\n",
       "\n",
       "_C414: x_(5,_1)_3 + x_(6,_0)_3 - 2 x_(6,_1)_3 + x_(6,_2)_3 >= 0\n",
       "\n",
       "_C415: x_(5,_1)_3 + x_(6,_0)_3 + 2 x_(6,_1)_3 + x_(6,_2)_3 <= 4\n",
       "\n",
       "_C416: x_(5,_1)_4 + x_(6,_0)_4 - 2 x_(6,_1)_4 + x_(6,_2)_4 >= 0\n",
       "\n",
       "_C417: x_(5,_1)_4 + x_(6,_0)_4 + 2 x_(6,_1)_4 + x_(6,_2)_4 <= 4\n",
       "\n",
       "_C418: x_(5,_1)_5 + x_(6,_0)_5 - 2 x_(6,_1)_5 + x_(6,_2)_5 >= 0\n",
       "\n",
       "_C419: x_(5,_1)_5 + x_(6,_0)_5 + 2 x_(6,_1)_5 + x_(6,_2)_5 <= 4\n",
       "\n",
       "_C420: x_(5,_2)_1 + x_(6,_1)_1 - 2 x_(6,_2)_1 + x_(6,_3)_1 >= 0\n",
       "\n",
       "_C421: x_(5,_2)_1 + x_(6,_1)_1 + 2 x_(6,_2)_1 + x_(6,_3)_1 <= 4\n",
       "\n",
       "_C422: x_(5,_2)_2 + x_(6,_1)_2 - 2 x_(6,_2)_2 + x_(6,_3)_2 >= 0\n",
       "\n",
       "_C423: x_(5,_2)_2 + x_(6,_1)_2 + 2 x_(6,_2)_2 + x_(6,_3)_2 <= 4\n",
       "\n",
       "_C424: x_(5,_2)_3 + x_(6,_1)_3 - 2 x_(6,_2)_3 + x_(6,_3)_3 >= 0\n",
       "\n",
       "_C425: x_(5,_2)_3 + x_(6,_1)_3 + 2 x_(6,_2)_3 + x_(6,_3)_3 <= 4\n",
       "\n",
       "_C426: x_(5,_2)_4 + x_(6,_1)_4 - 2 x_(6,_2)_4 + x_(6,_3)_4 >= 0\n",
       "\n",
       "_C427: x_(5,_2)_4 + x_(6,_1)_4 + 2 x_(6,_2)_4 + x_(6,_3)_4 <= 4\n",
       "\n",
       "_C428: x_(5,_2)_5 + x_(6,_1)_5 - 2 x_(6,_2)_5 + x_(6,_3)_5 >= 0\n",
       "\n",
       "_C429: x_(5,_2)_5 + x_(6,_1)_5 + 2 x_(6,_2)_5 + x_(6,_3)_5 <= 4\n",
       "\n",
       "_C430: x_(5,_3)_1 + x_(6,_2)_1 - 2 x_(6,_3)_1 + x_(6,_4)_1 >= 0\n",
       "\n",
       "_C431: x_(5,_3)_1 + x_(6,_2)_1 + 2 x_(6,_3)_1 + x_(6,_4)_1 <= 4\n",
       "\n",
       "_C432: x_(5,_3)_2 + x_(6,_2)_2 - 2 x_(6,_3)_2 + x_(6,_4)_2 >= 0\n",
       "\n",
       "_C433: x_(5,_3)_2 + x_(6,_2)_2 + 2 x_(6,_3)_2 + x_(6,_4)_2 <= 4\n",
       "\n",
       "_C434: x_(5,_3)_3 + x_(6,_2)_3 - 2 x_(6,_3)_3 + x_(6,_4)_3 >= 0\n",
       "\n",
       "_C435: x_(5,_3)_3 + x_(6,_2)_3 + 2 x_(6,_3)_3 + x_(6,_4)_3 <= 4\n",
       "\n",
       "_C436: x_(5,_3)_4 + x_(6,_2)_4 - 2 x_(6,_3)_4 + x_(6,_4)_4 >= 0\n",
       "\n",
       "_C437: x_(5,_3)_4 + x_(6,_2)_4 + 2 x_(6,_3)_4 + x_(6,_4)_4 <= 4\n",
       "\n",
       "_C438: x_(5,_3)_5 + x_(6,_2)_5 - 2 x_(6,_3)_5 + x_(6,_4)_5 >= 0\n",
       "\n",
       "_C439: x_(5,_3)_5 + x_(6,_2)_5 + 2 x_(6,_3)_5 + x_(6,_4)_5 <= 4\n",
       "\n",
       "_C440: x_(5,_5)_1 + x_(6,_4)_1 - 2 x_(6,_5)_1 + x_(6,_6)_1 >= 0\n",
       "\n",
       "_C441: x_(5,_5)_1 + x_(6,_4)_1 + 2 x_(6,_5)_1 + x_(6,_6)_1 <= 4\n",
       "\n",
       "_C442: x_(5,_5)_2 + x_(6,_4)_2 - 2 x_(6,_5)_2 + x_(6,_6)_2 >= 0\n",
       "\n",
       "_C443: x_(5,_5)_2 + x_(6,_4)_2 + 2 x_(6,_5)_2 + x_(6,_6)_2 <= 4\n",
       "\n",
       "_C444: x_(5,_5)_3 + x_(6,_4)_3 - 2 x_(6,_5)_3 + x_(6,_6)_3 >= 0\n",
       "\n",
       "_C445: x_(5,_5)_3 + x_(6,_4)_3 + 2 x_(6,_5)_3 + x_(6,_6)_3 <= 4\n",
       "\n",
       "_C446: x_(5,_5)_4 + x_(6,_4)_4 - 2 x_(6,_5)_4 + x_(6,_6)_4 >= 0\n",
       "\n",
       "_C447: x_(5,_5)_4 + x_(6,_4)_4 + 2 x_(6,_5)_4 + x_(6,_6)_4 <= 4\n",
       "\n",
       "_C448: x_(5,_5)_5 + x_(6,_4)_5 - 2 x_(6,_5)_5 + x_(6,_6)_5 >= 0\n",
       "\n",
       "_C449: x_(5,_5)_5 + x_(6,_4)_5 + 2 x_(6,_5)_5 + x_(6,_6)_5 <= 4\n",
       "\n",
       "_C450: x_(5,_6)_1 + x_(6,_5)_1 - 2 x_(6,_6)_1 >= 0\n",
       "\n",
       "_C451: x_(5,_6)_1 + x_(6,_5)_1 + 2 x_(6,_6)_1 <= 4\n",
       "\n",
       "_C452: x_(5,_6)_2 + x_(6,_5)_2 - 2 x_(6,_6)_2 >= 0\n",
       "\n",
       "_C453: x_(5,_6)_2 + x_(6,_5)_2 + 2 x_(6,_6)_2 <= 4\n",
       "\n",
       "_C454: x_(5,_6)_3 + x_(6,_5)_3 - 2 x_(6,_6)_3 >= 0\n",
       "\n",
       "_C455: x_(5,_6)_3 + x_(6,_5)_3 + 2 x_(6,_6)_3 <= 4\n",
       "\n",
       "_C456: x_(5,_6)_4 + x_(6,_5)_4 - 2 x_(6,_6)_4 >= 0\n",
       "\n",
       "_C457: x_(5,_6)_4 + x_(6,_5)_4 + 2 x_(6,_6)_4 <= 4\n",
       "\n",
       "_C458: x_(5,_6)_5 + x_(6,_5)_5 - 2 x_(6,_6)_5 >= 0\n",
       "\n",
       "_C459: x_(5,_6)_5 + x_(6,_5)_5 + 2 x_(6,_6)_5 <= 4\n",
       "\n",
       "VARIABLES\n",
       "0 <= x_(0,_0)_1 <= 1 Integer\n",
       "0 <= x_(0,_0)_2 <= 1 Integer\n",
       "0 <= x_(0,_0)_3 <= 1 Integer\n",
       "0 <= x_(0,_0)_4 <= 1 Integer\n",
       "0 <= x_(0,_0)_5 <= 1 Integer\n",
       "0 <= x_(0,_1)_1 <= 1 Integer\n",
       "0 <= x_(0,_1)_2 <= 1 Integer\n",
       "0 <= x_(0,_1)_3 <= 1 Integer\n",
       "0 <= x_(0,_1)_4 <= 1 Integer\n",
       "0 <= x_(0,_1)_5 <= 1 Integer\n",
       "0 <= x_(0,_2)_1 <= 1 Integer\n",
       "0 <= x_(0,_2)_2 <= 1 Integer\n",
       "0 <= x_(0,_2)_3 <= 1 Integer\n",
       "0 <= x_(0,_2)_4 <= 1 Integer\n",
       "0 <= x_(0,_2)_5 <= 1 Integer\n",
       "0 <= x_(0,_3)_1 <= 1 Integer\n",
       "0 <= x_(0,_3)_2 <= 1 Integer\n",
       "0 <= x_(0,_3)_3 <= 1 Integer\n",
       "0 <= x_(0,_3)_4 <= 1 Integer\n",
       "0 <= x_(0,_3)_5 <= 1 Integer\n",
       "0 <= x_(0,_4)_1 <= 1 Integer\n",
       "0 <= x_(0,_4)_2 <= 1 Integer\n",
       "0 <= x_(0,_4)_3 <= 1 Integer\n",
       "0 <= x_(0,_4)_4 <= 1 Integer\n",
       "0 <= x_(0,_4)_5 <= 1 Integer\n",
       "0 <= x_(0,_5)_1 <= 1 Integer\n",
       "0 <= x_(0,_5)_2 <= 1 Integer\n",
       "0 <= x_(0,_5)_3 <= 1 Integer\n",
       "0 <= x_(0,_5)_4 <= 1 Integer\n",
       "0 <= x_(0,_5)_5 <= 1 Integer\n",
       "0 <= x_(0,_6)_1 <= 1 Integer\n",
       "0 <= x_(0,_6)_2 <= 1 Integer\n",
       "0 <= x_(0,_6)_3 <= 1 Integer\n",
       "0 <= x_(0,_6)_4 <= 1 Integer\n",
       "0 <= x_(0,_6)_5 <= 1 Integer\n",
       "0 <= x_(1,_0)_1 <= 1 Integer\n",
       "0 <= x_(1,_0)_2 <= 1 Integer\n",
       "0 <= x_(1,_0)_3 <= 1 Integer\n",
       "0 <= x_(1,_0)_4 <= 1 Integer\n",
       "0 <= x_(1,_0)_5 <= 1 Integer\n",
       "0 <= x_(1,_1)_1 <= 1 Integer\n",
       "0 <= x_(1,_1)_2 <= 1 Integer\n",
       "0 <= x_(1,_1)_3 <= 1 Integer\n",
       "0 <= x_(1,_1)_4 <= 1 Integer\n",
       "0 <= x_(1,_1)_5 <= 1 Integer\n",
       "0 <= x_(1,_2)_1 <= 1 Integer\n",
       "0 <= x_(1,_2)_2 <= 1 Integer\n",
       "0 <= x_(1,_2)_3 <= 1 Integer\n",
       "0 <= x_(1,_2)_4 <= 1 Integer\n",
       "0 <= x_(1,_2)_5 <= 1 Integer\n",
       "0 <= x_(1,_3)_1 <= 1 Integer\n",
       "0 <= x_(1,_3)_2 <= 1 Integer\n",
       "0 <= x_(1,_3)_3 <= 1 Integer\n",
       "0 <= x_(1,_3)_4 <= 1 Integer\n",
       "0 <= x_(1,_3)_5 <= 1 Integer\n",
       "0 <= x_(1,_4)_1 <= 1 Integer\n",
       "0 <= x_(1,_4)_2 <= 1 Integer\n",
       "0 <= x_(1,_4)_3 <= 1 Integer\n",
       "0 <= x_(1,_4)_4 <= 1 Integer\n",
       "0 <= x_(1,_4)_5 <= 1 Integer\n",
       "0 <= x_(1,_5)_1 <= 1 Integer\n",
       "0 <= x_(1,_5)_2 <= 1 Integer\n",
       "0 <= x_(1,_5)_3 <= 1 Integer\n",
       "0 <= x_(1,_5)_4 <= 1 Integer\n",
       "0 <= x_(1,_5)_5 <= 1 Integer\n",
       "0 <= x_(1,_6)_1 <= 1 Integer\n",
       "0 <= x_(1,_6)_2 <= 1 Integer\n",
       "0 <= x_(1,_6)_3 <= 1 Integer\n",
       "0 <= x_(1,_6)_4 <= 1 Integer\n",
       "0 <= x_(1,_6)_5 <= 1 Integer\n",
       "0 <= x_(2,_0)_1 <= 1 Integer\n",
       "0 <= x_(2,_0)_2 <= 1 Integer\n",
       "0 <= x_(2,_0)_3 <= 1 Integer\n",
       "0 <= x_(2,_0)_4 <= 1 Integer\n",
       "0 <= x_(2,_0)_5 <= 1 Integer\n",
       "0 <= x_(2,_1)_1 <= 1 Integer\n",
       "0 <= x_(2,_1)_2 <= 1 Integer\n",
       "0 <= x_(2,_1)_3 <= 1 Integer\n",
       "0 <= x_(2,_1)_4 <= 1 Integer\n",
       "0 <= x_(2,_1)_5 <= 1 Integer\n",
       "0 <= x_(2,_2)_1 <= 1 Integer\n",
       "0 <= x_(2,_2)_2 <= 1 Integer\n",
       "0 <= x_(2,_2)_3 <= 1 Integer\n",
       "0 <= x_(2,_2)_4 <= 1 Integer\n",
       "0 <= x_(2,_2)_5 <= 1 Integer\n",
       "0 <= x_(2,_3)_1 <= 1 Integer\n",
       "0 <= x_(2,_3)_2 <= 1 Integer\n",
       "0 <= x_(2,_3)_3 <= 1 Integer\n",
       "0 <= x_(2,_3)_4 <= 1 Integer\n",
       "0 <= x_(2,_3)_5 <= 1 Integer\n",
       "0 <= x_(2,_4)_1 <= 1 Integer\n",
       "0 <= x_(2,_4)_2 <= 1 Integer\n",
       "0 <= x_(2,_4)_3 <= 1 Integer\n",
       "0 <= x_(2,_4)_4 <= 1 Integer\n",
       "0 <= x_(2,_4)_5 <= 1 Integer\n",
       "0 <= x_(2,_5)_1 <= 1 Integer\n",
       "0 <= x_(2,_5)_2 <= 1 Integer\n",
       "0 <= x_(2,_5)_3 <= 1 Integer\n",
       "0 <= x_(2,_5)_4 <= 1 Integer\n",
       "0 <= x_(2,_5)_5 <= 1 Integer\n",
       "0 <= x_(2,_6)_1 <= 1 Integer\n",
       "0 <= x_(2,_6)_2 <= 1 Integer\n",
       "0 <= x_(2,_6)_3 <= 1 Integer\n",
       "0 <= x_(2,_6)_4 <= 1 Integer\n",
       "0 <= x_(2,_6)_5 <= 1 Integer\n",
       "0 <= x_(3,_0)_1 <= 1 Integer\n",
       "0 <= x_(3,_0)_2 <= 1 Integer\n",
       "0 <= x_(3,_0)_3 <= 1 Integer\n",
       "0 <= x_(3,_0)_4 <= 1 Integer\n",
       "0 <= x_(3,_0)_5 <= 1 Integer\n",
       "0 <= x_(3,_1)_1 <= 1 Integer\n",
       "0 <= x_(3,_1)_2 <= 1 Integer\n",
       "0 <= x_(3,_1)_3 <= 1 Integer\n",
       "0 <= x_(3,_1)_4 <= 1 Integer\n",
       "0 <= x_(3,_1)_5 <= 1 Integer\n",
       "0 <= x_(3,_2)_1 <= 1 Integer\n",
       "0 <= x_(3,_2)_2 <= 1 Integer\n",
       "0 <= x_(3,_2)_3 <= 1 Integer\n",
       "0 <= x_(3,_2)_4 <= 1 Integer\n",
       "0 <= x_(3,_2)_5 <= 1 Integer\n",
       "0 <= x_(3,_3)_1 <= 1 Integer\n",
       "0 <= x_(3,_3)_2 <= 1 Integer\n",
       "0 <= x_(3,_3)_3 <= 1 Integer\n",
       "0 <= x_(3,_3)_4 <= 1 Integer\n",
       "0 <= x_(3,_3)_5 <= 1 Integer\n",
       "0 <= x_(3,_4)_1 <= 1 Integer\n",
       "0 <= x_(3,_4)_2 <= 1 Integer\n",
       "0 <= x_(3,_4)_3 <= 1 Integer\n",
       "0 <= x_(3,_4)_4 <= 1 Integer\n",
       "0 <= x_(3,_4)_5 <= 1 Integer\n",
       "0 <= x_(3,_5)_1 <= 1 Integer\n",
       "0 <= x_(3,_5)_2 <= 1 Integer\n",
       "0 <= x_(3,_5)_3 <= 1 Integer\n",
       "0 <= x_(3,_5)_4 <= 1 Integer\n",
       "0 <= x_(3,_5)_5 <= 1 Integer\n",
       "0 <= x_(3,_6)_1 <= 1 Integer\n",
       "0 <= x_(3,_6)_2 <= 1 Integer\n",
       "0 <= x_(3,_6)_3 <= 1 Integer\n",
       "0 <= x_(3,_6)_4 <= 1 Integer\n",
       "0 <= x_(3,_6)_5 <= 1 Integer\n",
       "0 <= x_(4,_0)_1 <= 1 Integer\n",
       "0 <= x_(4,_0)_2 <= 1 Integer\n",
       "0 <= x_(4,_0)_3 <= 1 Integer\n",
       "0 <= x_(4,_0)_4 <= 1 Integer\n",
       "0 <= x_(4,_0)_5 <= 1 Integer\n",
       "0 <= x_(4,_1)_1 <= 1 Integer\n",
       "0 <= x_(4,_1)_2 <= 1 Integer\n",
       "0 <= x_(4,_1)_3 <= 1 Integer\n",
       "0 <= x_(4,_1)_4 <= 1 Integer\n",
       "0 <= x_(4,_1)_5 <= 1 Integer\n",
       "0 <= x_(4,_2)_1 <= 1 Integer\n",
       "0 <= x_(4,_2)_2 <= 1 Integer\n",
       "0 <= x_(4,_2)_3 <= 1 Integer\n",
       "0 <= x_(4,_2)_4 <= 1 Integer\n",
       "0 <= x_(4,_2)_5 <= 1 Integer\n",
       "0 <= x_(4,_3)_1 <= 1 Integer\n",
       "0 <= x_(4,_3)_2 <= 1 Integer\n",
       "0 <= x_(4,_3)_3 <= 1 Integer\n",
       "0 <= x_(4,_3)_4 <= 1 Integer\n",
       "0 <= x_(4,_3)_5 <= 1 Integer\n",
       "0 <= x_(4,_4)_1 <= 1 Integer\n",
       "0 <= x_(4,_4)_2 <= 1 Integer\n",
       "0 <= x_(4,_4)_3 <= 1 Integer\n",
       "0 <= x_(4,_4)_4 <= 1 Integer\n",
       "0 <= x_(4,_4)_5 <= 1 Integer\n",
       "0 <= x_(4,_5)_1 <= 1 Integer\n",
       "0 <= x_(4,_5)_2 <= 1 Integer\n",
       "0 <= x_(4,_5)_3 <= 1 Integer\n",
       "0 <= x_(4,_5)_4 <= 1 Integer\n",
       "0 <= x_(4,_5)_5 <= 1 Integer\n",
       "0 <= x_(4,_6)_1 <= 1 Integer\n",
       "0 <= x_(4,_6)_2 <= 1 Integer\n",
       "0 <= x_(4,_6)_3 <= 1 Integer\n",
       "0 <= x_(4,_6)_4 <= 1 Integer\n",
       "0 <= x_(4,_6)_5 <= 1 Integer\n",
       "0 <= x_(5,_0)_1 <= 1 Integer\n",
       "0 <= x_(5,_0)_2 <= 1 Integer\n",
       "0 <= x_(5,_0)_3 <= 1 Integer\n",
       "0 <= x_(5,_0)_4 <= 1 Integer\n",
       "0 <= x_(5,_0)_5 <= 1 Integer\n",
       "0 <= x_(5,_1)_1 <= 1 Integer\n",
       "0 <= x_(5,_1)_2 <= 1 Integer\n",
       "0 <= x_(5,_1)_3 <= 1 Integer\n",
       "0 <= x_(5,_1)_4 <= 1 Integer\n",
       "0 <= x_(5,_1)_5 <= 1 Integer\n",
       "0 <= x_(5,_2)_1 <= 1 Integer\n",
       "0 <= x_(5,_2)_2 <= 1 Integer\n",
       "0 <= x_(5,_2)_3 <= 1 Integer\n",
       "0 <= x_(5,_2)_4 <= 1 Integer\n",
       "0 <= x_(5,_2)_5 <= 1 Integer\n",
       "0 <= x_(5,_3)_1 <= 1 Integer\n",
       "0 <= x_(5,_3)_2 <= 1 Integer\n",
       "0 <= x_(5,_3)_3 <= 1 Integer\n",
       "0 <= x_(5,_3)_4 <= 1 Integer\n",
       "0 <= x_(5,_3)_5 <= 1 Integer\n",
       "0 <= x_(5,_4)_1 <= 1 Integer\n",
       "0 <= x_(5,_4)_2 <= 1 Integer\n",
       "0 <= x_(5,_4)_3 <= 1 Integer\n",
       "0 <= x_(5,_4)_4 <= 1 Integer\n",
       "0 <= x_(5,_4)_5 <= 1 Integer\n",
       "0 <= x_(5,_5)_1 <= 1 Integer\n",
       "0 <= x_(5,_5)_2 <= 1 Integer\n",
       "0 <= x_(5,_5)_3 <= 1 Integer\n",
       "0 <= x_(5,_5)_4 <= 1 Integer\n",
       "0 <= x_(5,_5)_5 <= 1 Integer\n",
       "0 <= x_(5,_6)_1 <= 1 Integer\n",
       "0 <= x_(5,_6)_2 <= 1 Integer\n",
       "0 <= x_(5,_6)_3 <= 1 Integer\n",
       "0 <= x_(5,_6)_4 <= 1 Integer\n",
       "0 <= x_(5,_6)_5 <= 1 Integer\n",
       "0 <= x_(6,_0)_1 <= 1 Integer\n",
       "0 <= x_(6,_0)_2 <= 1 Integer\n",
       "0 <= x_(6,_0)_3 <= 1 Integer\n",
       "0 <= x_(6,_0)_4 <= 1 Integer\n",
       "0 <= x_(6,_0)_5 <= 1 Integer\n",
       "0 <= x_(6,_1)_1 <= 1 Integer\n",
       "0 <= x_(6,_1)_2 <= 1 Integer\n",
       "0 <= x_(6,_1)_3 <= 1 Integer\n",
       "0 <= x_(6,_1)_4 <= 1 Integer\n",
       "0 <= x_(6,_1)_5 <= 1 Integer\n",
       "0 <= x_(6,_2)_1 <= 1 Integer\n",
       "0 <= x_(6,_2)_2 <= 1 Integer\n",
       "0 <= x_(6,_2)_3 <= 1 Integer\n",
       "0 <= x_(6,_2)_4 <= 1 Integer\n",
       "0 <= x_(6,_2)_5 <= 1 Integer\n",
       "0 <= x_(6,_3)_1 <= 1 Integer\n",
       "0 <= x_(6,_3)_2 <= 1 Integer\n",
       "0 <= x_(6,_3)_3 <= 1 Integer\n",
       "0 <= x_(6,_3)_4 <= 1 Integer\n",
       "0 <= x_(6,_3)_5 <= 1 Integer\n",
       "0 <= x_(6,_4)_1 <= 1 Integer\n",
       "0 <= x_(6,_4)_2 <= 1 Integer\n",
       "0 <= x_(6,_4)_3 <= 1 Integer\n",
       "0 <= x_(6,_4)_4 <= 1 Integer\n",
       "0 <= x_(6,_4)_5 <= 1 Integer\n",
       "0 <= x_(6,_5)_1 <= 1 Integer\n",
       "0 <= x_(6,_5)_2 <= 1 Integer\n",
       "0 <= x_(6,_5)_3 <= 1 Integer\n",
       "0 <= x_(6,_5)_4 <= 1 Integer\n",
       "0 <= x_(6,_5)_5 <= 1 Integer\n",
       "0 <= x_(6,_6)_1 <= 1 Integer\n",
       "0 <= x_(6,_6)_2 <= 1 Integer\n",
       "0 <= x_(6,_6)_3 <= 1 Integer\n",
       "0 <= x_(6,_6)_4 <= 1 Integer\n",
       "0 <= x_(6,_6)_5 <= 1 Integer"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b2143f19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob.solve()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bb257236",
   "metadata": {},
   "outputs": [],
   "source": [
    "Tsol=T.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bed9372a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[2, 2, 2, 4, 4, 4, 4],\n",
       " [2, 3, 2, 2, 2, 5, 4],\n",
       " [2, 3, 3, 3, 1, 5, 4],\n",
       " [2, 5, 5, 5, 1, 5, 4],\n",
       " [2, 5, 1, 1, 1, 5, 4],\n",
       " [2, 5, 1, 5, 5, 5, 4],\n",
       " [2, 5, 5, 5, 4, 4, 4]]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for (i,j) in cells:\n",
    "    for k in paths:\n",
    "        val = pulp.value(x[(i,j)][k])\n",
    "        if val >0.9:\n",
    "            # print(i,j,\"-->\",k)\n",
    "            Tsol[i][j]=k\n",
    "Tsol"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20c8d64e",
   "metadata": {},
   "source": [
    "# Data as a graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e3409741",
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "79ebf872",
   "metadata": {},
   "outputs": [],
   "source": [
    "T = [\n",
    "    [0,0,0,4,0,0,0],\n",
    "    [0,3,0,0,2,5,0],\n",
    "    [0,0,0,3,1,0,0],\n",
    "    [0,0,0,5,0,0,0],\n",
    "    [0,0,0,0,0,0,0],\n",
    "    [0,0,1,0,0,0,0],\n",
    "    [2,0,0,0,4,0,0],\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "039e8436",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0, 0, 0, 10, 0, 0, 0, 0, 0, 0, 11, 0, 0, 0, 0],\n",
       " [0, 3, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11],\n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0],\n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 12, 0],\n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0],\n",
       " [4, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 12, 0, 0, 0, 0],\n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0],\n",
       " [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 10, 8, 0, 0, 0],\n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 5, 8, 0, 0, 0, 0, 0],\n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 5, 0, 0],\n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0],\n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T = [\n",
    "    [0,0,0,10,0,0,0,0,0,0,11,0,0,0,0],\n",
    "    [0,3,0,6,0,0,0,0,0,0,0,0,0,0,11],\n",
    "    [0,0,0,0,0,0,0,0,0,0,0,0,6,0,0],\n",
    "    [0,0,0,0,0,0,0,0,2,0,0,0,0,12,0],\n",
    "    [0,0,0,0,0,0,0,0,0,0,0,9,0,0,0],\n",
    "    [4,0,0,0,4,0,0,0,0,0,0,0,0,0,0],\n",
    "    [0,0,0,0,0,0,0,0,9,0,0,0,0,0,0],\n",
    "    [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],\n",
    "    [0,0,0,0,0,0,0,1,0,0,12,0,0,0,0],\n",
    "    [0,0,0,0,0,0,0,0,0,0,0,0,0,7,0],\n",
    "    [0,0,0,0,2,0,0,0,0,0,10,8,0,0,0],\n",
    "    [0,0,0,0,0,0,0,0,5,8,0,0,0,0,0],\n",
    "    [0,0,0,0,0,0,0,0,0,0,1,3,5,0,0],\n",
    "    [0,0,0,0,0,0,0,0,0,0,0,0,7,0,0],\n",
    "    [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],\n",
    "]\n",
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e31ea13f",
   "metadata": {},
   "outputs": [],
   "source": [
    "G=nx.grid_2d_graph(len(T),len(T))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dc5791e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for v in G.nodes():\n",
    "    G.nodes[v][\"layer\"]=v[0]\n",
    "    val = T[v[1]][v[0]]\n",
    "    if val > 0:\n",
    "        G.nodes[v][\"value\"]=val\n",
    "    else:\n",
    "        G.nodes[v][\"value\"]=\"\"\n",
    "nx.draw(G,pos=nx.multipartite_layout(G,subset_key=\"layer\"),\n",
    "          labels = nx.get_node_attributes(G, 'value') )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0842c94b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paths = [p for p in range(1,max(G.nodes[v][\"value\"] for v in G.nodes() if G.nodes[v][\"value\"])+1)]\n",
    "paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c8e4bd03",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create problem\n",
    "prob = pulp.LpProblem(\"numberlink\", pulp.LpMaximize)\n",
    "\n",
    "# define variables\n",
    "x = pulp.LpVariable.dicts(\"x\",(G.nodes(),paths),cat=pulp.LpBinary)\n",
    "\n",
    "# objective function\n",
    "prob += 0\n",
    "\n",
    "# each cell has a unique number\n",
    "for (i,j) in G.nodes():\n",
    "    prob += pulp.lpSum(x[(i,j)][k] for k in paths) == 1\n",
    "    \n",
    "# enforce endpoints\n",
    "for (i,j) in G.nodes():\n",
    "    val = G.nodes[(i,j)][\"value\"]\n",
    "    if val:\n",
    "        prob += x[(i,j)][val] == 1\n",
    "        \n",
    "# if cell is an endpoint, then one neighbor must have same value\n",
    "for (i,j) in G.nodes():\n",
    "    val = G.nodes[(i,j)][\"value\"]\n",
    "    if val:\n",
    "        prob += pulp.lpSum(x[(u,v)][val] for (u,v) in G.neighbors((i,j))) == 1\n",
    "        \n",
    "# if cell is not an end point, then 2 neighbours must have same value\n",
    "for (i,j) in G.nodes():\n",
    "    val = G.nodes[(i,j)][\"value\"]\n",
    "    if not val:\n",
    "        for k in paths:\n",
    "            prob += pulp.lpSum(x[(u,v)][k] for (u,v) in G.neighbors((i,j))) >= 2*x[(i,j)][k]\n",
    "            prob += pulp.lpSum(x[(u,v)][k] for (u,v) in G.neighbors((i,j))) <= 2*x[(i,j)][k]+4*(1-x[(i,j)][k])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bc411c0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prob.writeLP(\"prob.lp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "95b4c67e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob.solve(solver = pulp.GUROBI_CMD())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "688f6324",
   "metadata": {},
   "outputs": [],
   "source": [
    "for (i,j) in G.nodes():\n",
    "    for k in paths:\n",
    "        val = pulp.value(x[(i,j)][k])\n",
    "        if val >0.9:\n",
    "            G.nodes[(i,j)][\"value\"]=k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "26c45c07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(G,pos=nx.multipartite_layout(G,subset_key=\"layer\"),\n",
    "          labels = nx.get_node_attributes(G, 'value'),\n",
    "          node_color=[G.nodes[(i,j)][\"value\"] for (i,j) in G.nodes()])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "617337bd",
   "metadata": {},
   "source": [
    "# MIP 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5b613c72",
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "051f9794",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "T = [\n",
    "    [0,0,0,4,0,0,0],\n",
    "    [0,3,0,0,2,5,0],\n",
    "    [0,0,0,3,1,0,0],\n",
    "    [0,0,0,5,0,0,0],\n",
    "    [0,0,0,0,0,0,0],\n",
    "    [0,0,1,0,0,0,0],\n",
    "    [2,0,0,0,4,0,0],\n",
    "]\n",
    "T = [\n",
    "    [0,0,0,10,0,0,0,0,0,0,11,0,0,0,0],\n",
    "    [0,3,0,6,0,0,0,0,0,0,0,0,0,0,11],\n",
    "    [0,0,0,0,0,0,0,0,0,0,0,0,6,0,0],\n",
    "    [0,0,0,0,0,0,0,0,2,0,0,0,0,12,0],\n",
    "    [0,0,0,0,0,0,0,0,0,0,0,9,0,0,0],\n",
    "    [4,0,0,0,4,0,0,0,0,0,0,0,0,0,0],\n",
    "    [0,0,0,0,0,0,0,0,9,0,0,0,0,0,0],\n",
    "    [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],\n",
    "    [0,0,0,0,0,0,0,1,0,0,12,0,0,0,0],\n",
    "    [0,0,0,0,0,0,0,0,0,0,0,0,0,7,0],\n",
    "    [0,0,0,0,2,0,0,0,0,0,10,8,0,0,0],\n",
    "    [0,0,0,0,0,0,0,0,5,8,0,0,0,0,0],\n",
    "    [0,0,0,0,0,0,0,0,0,0,1,3,5,0,0],\n",
    "    [0,0,0,0,0,0,0,0,0,0,0,0,7,0,0],\n",
    "    [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],\n",
    "]\n",
    "G=nx.grid_2d_graph(len(T),len(T))\n",
    "for v in G.nodes():\n",
    "    G.nodes[v][\"layer\"]=v[0]\n",
    "    val = T[v[1]][v[0]]\n",
    "    if val > 0:\n",
    "        G.nodes[v][\"value\"]=val\n",
    "    else:\n",
    "        G.nodes[v][\"value\"]=\"\"\n",
    "nx.draw(G,pos=nx.multipartite_layout(G,subset_key=\"layer\"),\n",
    "          labels = nx.get_node_attributes(G, 'value') )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5092cf7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "G=G.to_directed()\n",
    "nx.draw(G,pos=nx.multipartite_layout(G,subset_key=\"layer\"),\n",
    "          labels = nx.get_node_attributes(G, 'value') )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fa979d9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paths = [p for p in range(1,max(G.nodes[v][\"value\"] for v in G.nodes() if G.nodes[v][\"value\"])+1)]\n",
    "paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "27f432f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 5),\n",
       " (1, 1),\n",
       " (3, 0),\n",
       " (3, 1),\n",
       " (4, 5),\n",
       " (4, 10),\n",
       " (7, 8),\n",
       " (8, 3),\n",
       " (8, 6),\n",
       " (8, 11),\n",
       " (9, 11),\n",
       " (10, 0),\n",
       " (10, 8),\n",
       " (10, 10),\n",
       " (10, 12),\n",
       " (11, 4),\n",
       " (11, 10),\n",
       " (11, 12),\n",
       " (12, 2),\n",
       " (12, 12),\n",
       " (12, 13),\n",
       " (13, 3),\n",
       " (13, 9),\n",
       " (14, 1)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[v for v in G.nodes() if G.nodes[v][\"value\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f809a020",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{((0, 5), (4, 5)): 4,\n",
       " ((1, 1), (11, 12)): 3,\n",
       " ((3, 0), (10, 10)): 10,\n",
       " ((3, 1), (12, 2)): 6,\n",
       " ((4, 10), (8, 3)): 2,\n",
       " ((7, 8), (10, 12)): 1,\n",
       " ((8, 6), (11, 4)): 9,\n",
       " ((8, 11), (12, 12)): 5,\n",
       " ((9, 11), (11, 10)): 8,\n",
       " ((10, 0), (14, 1)): 11,\n",
       " ((10, 8), (13, 3)): 12,\n",
       " ((12, 13), (13, 9)): 7}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "endpoints_pairs = [(u,v) for u in G.nodes() for v in G.nodes() if u<v and G.nodes[u][\"value\"] and G.nodes[v][\"value\"] and G.nodes[u][\"value\"]==G.nodes[v][\"value\"]]\n",
    "endpoints = {}\n",
    "for (u,v) in endpoints_pairs:\n",
    "    endpoints[(u,v)]=G.nodes[u][\"value\"]\n",
    "endpoints              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3d047336",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create problem\n",
    "prob = pulp.LpProblem(\"numberlink\", pulp.LpMaximize)\n",
    "\n",
    "# define variables\n",
    "x = pulp.LpVariable.dicts(\"x\",(G.edges(),paths),cat=pulp.LpBinary)\n",
    "y = pulp.LpVariable.dicts(\"y\",(G.nodes(),paths),cat=pulp.LpBinary)\n",
    "s = pulp.LpVariable.dicts(\"s\",([v for v in G.nodes() if G.nodes[v][\"value\"]],paths),cat=pulp.LpBinary)\n",
    "\n",
    "# objective function\n",
    "prob += 0\n",
    "\n",
    "# one number per cell\n",
    "for u in G.nodes():\n",
    "    prob += pulp.lpSum(y[u][k] for k in paths) == 1\n",
    "    \n",
    "# enforce endpoints\n",
    "for u in G.nodes():\n",
    "    val = G.nodes[u][\"value\"]\n",
    "    if val:\n",
    "        prob += y[u][val] == 1\n",
    "        \n",
    "# flow conservation constraints \n",
    "for u in G.nodes():\n",
    "    val = G.nodes[u][\"value\"]\n",
    "    if val:\n",
    "        k = val\n",
    "        prob += pulp.lpSum(x[(u,v)][k] for v in G.successors(u))==s[u][k]\n",
    "        prob += pulp.lpSum(x[(v,u)][k] for v in G.predecessors(u))==1-s[u][k]\n",
    "    else:\n",
    "        for k in paths:\n",
    "            prob += pulp.lpSum(x[(u,v)][k] for v in G.successors(u))==pulp.lpSum(x[(v,u)][k] for v in G.predecessors(u))\n",
    "            \n",
    "# for each pair of endpoints, one source and one sink\n",
    "for (u,v) in endpoints:\n",
    "    k = endpoints[(u,v)]\n",
    "    prob += s[u][k]+s[v][k]==1\n",
    "    \n",
    "for (i,j) in G.edges():\n",
    "    for k in paths:\n",
    "        prob += x[(i,j)][k]<=y[i][k]\n",
    "        prob += x[(i,j)][k]<=y[j][k]\n",
    "\n",
    "for k in paths:\n",
    "    for i in G.nodes():\n",
    "        prob += y[i][k] <= pulp.lpSum(x[(i,j)][k] for j in G.successors(i))+pulp.lpSum(x[(j,i)][k] for j in G.predecessors(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a9fea5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "prob.solve(solver = pulp.GUROBI_CMD(timeLimit=20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "8ef4dbe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "3bbe488c",
   "metadata": {},
   "outputs": [],
   "source": [
    "for (i,j) in G.nodes():\n",
    "    for k in paths:\n",
    "        val = pulp.value(y[(i,j)][k])\n",
    "        if val >0.9:\n",
    "            G.nodes[(i,j)][\"value\"]=k\n",
    "for (i,j) in x:\n",
    "    remove=True\n",
    "    for k in x[(i,j)]:\n",
    "        val = pulp.value(x[(i,j)][k])\n",
    "        if val >0.9:\n",
    "            remove=False\n",
    "    if remove:\n",
    "        G.remove_edge(i,j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "b1fd54f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(G,pos=nx.multipartite_layout(G,subset_key=\"layer\"),\n",
    "          labels = nx.get_node_attributes(G, 'value'),\n",
    "          node_color=[G.nodes[(i,j)][\"value\"] for (i,j) in G.nodes()])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}