pglib_case24.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MathOptInterface"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using PowerModels, Gurobi, Ipopt, JuMP, COSMO, Mosek, MosekTools, MathOptInterface\n",
    "const MOI = MathOptInterface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"../pglib-opf/api/pglib_opf_case24_ieee_rts__api.m\""
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = \"../pglib-opf/api/pglib_opf_case24_ieee_rts__api.m\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m[info | PowerModels]: extending matpower format with data: areas 4x3\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 1: [13000.0, 400.6849]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 29: [0.1, 0.001]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 2: [13000.0, 400.6849]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 6: [13000.0, 400.6849]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 25: [0.1, 0.001]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 5: [13000.0, 400.6849]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 3 cost terms from generator 15: Float64[]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 27: [0.1, 0.001]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 28: [0.1, 0.001]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 26: [0.1, 0.001]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 30: [0.1, 0.001]\u001b[39m\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit http://projects.coin-or.org/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "This is Ipopt version 3.13.2, running with linear solver mumps.\n",
      "NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
      "\n",
      "Number of nonzeros in equality constraint Jacobian...:     1892\n",
      "Number of nonzeros in inequality constraint Jacobian.:      792\n",
      "Number of nonzeros in Lagrangian Hessian.............:     1456\n",
      "\n",
      "Total number of variables............................:      265\n",
      "                     variables with only lower bounds:        0\n",
      "                variables with lower and upper bounds:      265\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:      201\n",
      "Total number of inequality constraints...............:      192\n",
      "        inequality constraints with only lower bounds:       58\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:      134\n",
      "\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "   0  2.6268890e+04 5.88e+00 4.57e+01  -1.0 0.00e+00    -  0.00e+00 0.00e+00   0\n",
      "   1  4.5788027e+04 5.17e+00 5.87e+01  -1.0 5.65e+00    -  5.16e-03 1.21e-01h  3\n",
      "   2  4.0736668e+04 5.11e+00 5.79e+01  -1.0 1.54e+01    -  1.79e-02 1.17e-02f  1\n",
      "   3  4.1120325e+04 5.03e+00 5.67e+01  -1.0 6.98e+00    -  8.56e-02 1.54e-02h  1\n",
      "   4  5.1169305e+04 4.05e+00 4.59e+01  -1.0 7.79e+00    -  4.92e-01 1.94e-01h  1\n",
      "   5  5.8496875e+04 3.41e+00 3.86e+01  -1.0 1.84e+01    -  2.20e-01 1.60e-01h  1\n",
      "   6  7.4388495e+04 2.12e+00 2.40e+01  -1.0 2.42e+01    -  6.77e-01 3.78e-01H  1\n",
      "   7  8.1505297e+04 1.62e+00 1.85e+01  -1.0 2.49e+01    -  4.05e-01 2.33e-01h  1\n",
      "   8  9.0467124e+04 1.23e+00 1.44e+01  -1.0 1.45e+01    -  6.58e-01 2.43e-01h  1\n",
      "   9  1.0305412e+05 7.89e-01 9.09e+00  -1.0 1.69e+01    -  3.26e-01 3.59e-01h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  10  1.1490150e+05 4.00e-01 4.62e+00  -1.0 1.52e+01    -  5.64e-01 4.92e-01h  1\n",
      "  11  1.1761831e+05 3.24e-01 7.74e+00  -1.0 8.09e+00    -  2.45e-01 1.91e-01h  1\n",
      "  12  1.1768570e+05 3.22e-01 2.83e+01  -1.0 1.40e+01    -  4.44e-02 5.94e-03h  1\n",
      "  13  1.1846611e+05 3.02e-01 2.64e+01  -1.0 6.61e+00    -  7.25e-02 6.07e-02h  1\n",
      "  14  1.1951930e+05 2.77e-01 2.80e+01  -1.0 1.60e+01    -  2.87e-02 8.49e-02h  1\n",
      "  15  1.2377153e+05 2.12e-01 2.39e+01  -1.0 2.56e+00    -  1.00e+00 3.04e-01h  1\n",
      "  16  1.2576637e+05 1.85e-01 1.87e+01  -1.0 2.31e+00    -  1.81e-01 1.96e-01h  1\n",
      "  17  1.2831889e+05 1.28e-01 1.45e+01  -1.0 3.30e+00    -  3.47e-01 2.91e-01h  1\n",
      "  18  1.2838970e+05 1.26e-01 1.25e+02  -1.0 9.60e-01    -  5.97e-02 1.11e-02h  1\n",
      "  19  1.3511662e+05 1.53e-02 1.54e+01  -1.0 6.32e-01    -  6.38e-01 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  20  1.3525088e+05 7.31e-03 1.56e+02  -1.0 2.50e-01    -  9.60e-01 5.01e-01h  1\n",
      "  21  1.3545106e+05 1.17e-04 9.12e-01  -1.0 1.61e-01    -  1.00e+00 1.00e+00h  1\n",
      "  22  1.3505092e+05 1.01e-03 1.43e+00  -1.7 1.23e+00    -  1.00e+00 9.72e-01f  1\n",
      "  23  1.3504530e+05 3.03e-04 9.32e-03  -1.7 3.16e-01    -  1.00e+00 1.00e+00h  1\n",
      "  24  1.3496571e+05 1.00e-04 2.99e+00  -3.8 3.07e-01    -  9.56e-01 8.14e-01f  1\n",
      "  25  1.3495142e+05 6.80e-05 1.01e+00  -3.8 9.14e-02    -  1.00e+00 8.39e-01h  1\n",
      "  26  1.3494888e+05 1.43e-05 7.34e-04  -3.8 3.98e-02    -  1.00e+00 1.00e+00h  1\n",
      "  27  1.3494818e+05 8.44e-07 1.37e-03  -5.7 9.18e-03    -  1.00e+00 9.97e-01h  1\n",
      "  28  1.3494818e+05 4.69e-09 2.70e-07  -5.7 6.90e-04    -  1.00e+00 1.00e+00f  1\n",
      "  29  1.3494817e+05 3.98e-11 2.56e-09  -8.6 6.38e-05    -  1.00e+00 1.00e+00h  1\n",
      "\n",
      "Number of Iterations....: 29\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   1.0380628240435872e+03    1.3494816712566631e+05\n",
      "Dual infeasibility......:   2.5553280740119794e-09    3.3219264962155732e-07\n",
      "Constraint violation....:   3.9835912346575242e-11    3.9835912346575242e-11\n",
      "Complementarity.........:   3.6064661812884366e-09    4.6884060356749675e-07\n",
      "Overall NLP error.......:   3.6064661812884366e-09    4.6884060356749675e-07\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 35\n",
      "Number of objective gradient evaluations             = 30\n",
      "Number of equality constraint evaluations            = 35\n",
      "Number of inequality constraint evaluations          = 35\n",
      "Number of equality constraint Jacobian evaluations   = 30\n",
      "Number of inequality constraint Jacobian evaluations = 30\n",
      "Number of Lagrangian Hessian evaluations             = 29\n",
      "Total CPU secs in IPOPT (w/o function evaluations)   =      1.791\n",
      "Total CPU secs in NLP function evaluations           =      0.665\n",
      "\n",
      "EXIT: Optimal Solution Found.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Dict{String,Any} with 8 entries:\n",
       "  \"solve_time\"         => 2.82688\n",
       "  \"optimizer\"          => \"Ipopt\"\n",
       "  \"termination_status\" => LOCALLY_SOLVED\n",
       "  \"dual_status\"        => FEASIBLE_POINT\n",
       "  \"primal_status\"      => FEASIBLE_POINT\n",
       "  \"objective\"          => 1.34948e5\n",
       "  \"solution\"           => Dict{String,Any}(\"baseMVA\"=>100.0,\"branch\"=>Dict{Stri…\n",
       "  \"objective_lb\"       => -Inf"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipopt_rec = run_opf(file, ACRPowerModel, Ipopt.Optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m[info | PowerModels]: extending matpower format with data: areas 4x3\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 1: [13000.0, 400.6849]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 29: [0.1, 0.001]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 2: [13000.0, 400.6849]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 6: [13000.0, 400.6849]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 25: [0.1, 0.001]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 5: [13000.0, 400.6849]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 3 cost terms from generator 15: Float64[]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 27: [0.1, 0.001]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 28: [0.1, 0.001]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 26: [0.1, 0.001]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 30: [0.1, 0.001]\u001b[39m\n",
      "Problem\n",
      "  Name                   :                 \n",
      "  Objective sense        : min             \n",
      "  Type                   : CONIC (conic optimization problem)\n",
      "  Constraints            : 2382            \n",
      "  Cones                  : 98              \n",
      "  Scalar variables       : 1410            \n",
      "  Matrix variables       : 1               \n",
      "  Integer variables      : 0               \n",
      "\n",
      "Optimizer started.\n",
      "Presolve started.\n",
      "Linear dependency checker started.\n",
      "Linear dependency checker terminated.\n",
      "Eliminator started.\n",
      "Freed constraints in eliminator : 332\n",
      "Eliminator terminated.\n",
      "Eliminator started.\n",
      "Freed constraints in eliminator : 0\n",
      "Eliminator terminated.\n",
      "Eliminator - tries                  : 2                 time                   : 0.00            \n",
      "Lin. dep.  - tries                  : 1                 time                   : 0.00            \n",
      "Lin. dep.  - number                 : 276             \n",
      "Presolve terminated. Time: 0.00    \n",
      "Problem\n",
      "  Name                   :                 \n",
      "  Objective sense        : min             \n",
      "  Type                   : CONIC (conic optimization problem)\n",
      "  Constraints            : 2382            \n",
      "  Cones                  : 98              \n",
      "  Scalar variables       : 1410            \n",
      "  Matrix variables       : 1               \n",
      "  Integer variables      : 0               \n",
      "\n",
      "Optimizer  - threads                : 4               \n",
      "Optimizer  - solved problem         : the primal      \n",
      "Optimizer  - Constraints            : 1456\n",
      "Optimizer  - Cones                  : 99\n",
      "Optimizer  - Scalar variables       : 965               conic                  : 779             \n",
      "Optimizer  - Semi-definite variables: 1                 scalarized             : 1176            \n",
      "Factor     - setup time             : 0.05              dense det. time        : 0.00            \n",
      "Factor     - ML order time          : 0.02              GP order time          : 0.00            \n",
      "Factor     - nonzeros before factor : 6.97e+05          after factor           : 7.06e+05        \n",
      "Factor     - dense dim.             : 2                 flops                  : 5.50e+08        \n",
      "ITE PFEAS    DFEAS    GFEAS    PRSTATUS   POBJ              DOBJ              MU       TIME  \n",
      "0   4.4e+01  1.3e+04  1.4e+04  0.00e+00   7.541940994e+04   6.156550493e+04   1.0e+00  0.06  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Warning: MathOptInterface.VariablePrimalStart() is not supported by MathOptInterface.Bridges.LazyBridgeOptimizer{MosekModel}. This information will be discarded.\n",
      "└ @ MathOptInterface.Utilities /home/weiqizhang/.julia/packages/MathOptInterface/RmalA/src/Utilities/copy.jl:140\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1   3.1e+00  9.2e+02  3.7e+03  -1.00e+00  4.949537903e+04   3.567900844e+04   7.1e-02  0.10  \n",
      "2   1.4e+00  4.2e+02  2.4e+03  -9.85e-01  3.167132051e+04   1.840126800e+04   3.2e-02  0.15  \n",
      "3   2.9e-01  8.5e+01  7.2e+02  -7.97e-01  3.764145662e+04   2.971814171e+04   6.5e-03  0.18  \n",
      "4   2.0e-01  5.8e+01  4.5e+02  3.47e-02   4.329090667e+04   3.688441053e+04   4.5e-03  0.22  \n",
      "5   6.2e-02  1.8e+01  1.1e+02  2.23e-01   5.484562998e+04   5.196934700e+04   1.4e-03  0.26  \n",
      "6   3.7e-02  1.1e+01  6.0e+01  3.34e-01   5.924620892e+04   5.704603354e+04   8.3e-04  0.29  \n",
      "7   1.6e-02  4.8e+00  2.7e+01  3.63e-02   6.643568176e+04   6.487273828e+04   3.7e-04  0.33  \n",
      "8   8.0e-03  2.4e+00  1.4e+01  -6.41e-02  7.497568889e+04   7.378751094e+04   1.8e-04  0.37  \n",
      "9   6.1e-03  1.8e+00  1.1e+01  8.12e-03   7.827713985e+04   7.719858699e+04   1.4e-04  0.41  \n",
      "10  3.4e-03  1.0e+00  5.7e+00  4.38e-02   8.688418189e+04   8.608348623e+04   7.7e-05  0.45  \n",
      "11  2.4e-03  7.0e-01  3.9e+00  1.39e-01   9.242162245e+04   9.176794215e+04   5.4e-05  0.48  \n",
      "12  1.9e-03  5.7e-01  3.0e+00  2.54e-01   9.562341425e+04   9.505188348e+04   4.4e-05  0.52  \n",
      "13  6.5e-04  1.9e-01  8.4e-01  3.24e-01   1.092579756e+05   1.089934472e+05   1.5e-05  0.57  \n",
      "14  4.2e-04  1.2e-01  5.0e-01  5.11e-01   1.134099361e+05   1.132157392e+05   9.6e-06  0.61  \n",
      "15  3.2e-04  9.4e-02  3.5e-01  5.49e-01   1.160036080e+05   1.158480222e+05   7.3e-06  0.64  \n",
      "16  1.3e-04  3.8e-02  1.2e-01  5.92e-01   1.215359996e+05   1.214527289e+05   2.9e-06  0.68  \n",
      "17  4.5e-05  1.3e-02  3.4e-02  -1.46e-01  1.269473126e+05   1.269098312e+05   1.0e-06  0.72  \n",
      "18  5.8e-06  1.7e-03  2.1e-03  7.32e-01   1.311874118e+05   1.311824421e+05   1.3e-07  0.76  \n",
      "19  2.7e-06  7.9e-04  6.8e-04  9.41e-01   1.317059365e+05   1.317036316e+05   6.1e-08  0.86  \n",
      "20  3.5e-07  1.0e-04  3.4e-05  9.84e-01   1.320921460e+05   1.320918516e+05   8.3e-09  0.90  \n",
      "21  4.2e-08  2.3e-06  1.2e-07  1.00e+00   1.321558200e+05   1.321558137e+05   1.9e-10  0.97  \n",
      "22  2.7e-08  1.4e-07  1.8e-09  1.00e+00   1.321573882e+05   1.321573878e+05   1.2e-11  1.01  \n",
      "23  2.7e-08  1.4e-07  1.8e-09  9.96e-01   1.321573882e+05   1.321573878e+05   1.2e-11  1.06  \n",
      "24  2.7e-08  1.4e-07  1.8e-09  1.00e+00   1.321573882e+05   1.321573878e+05   1.2e-11  1.10  \n",
      "Optimizer terminated. Time: 1.15    \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Dict{String,Any} with 8 entries:\n",
       "  \"solve_time\"         => 1.14848\n",
       "  \"optimizer\"          => \"Mosek\"\n",
       "  \"termination_status\" => SLOW_PROGRESS\n",
       "  \"dual_status\"        => FEASIBLE_POINT\n",
       "  \"primal_status\"      => FEASIBLE_POINT\n",
       "  \"objective\"          => 1.32157e5\n",
       "  \"solution\"           => Dict{String,Any}(\"baseMVA\"=>100.0,\"branch\"=>Dict{Stri…\n",
       "  \"objective_lb\"       => 0.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mosek_sdp = run_opf(file, SDPWRMPowerModel, Mosek.Optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m[info | PowerModels]: extending matpower format with data: areas 4x3\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 1: [13000.0, 400.6849]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 29: [0.1, 0.001]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 2: [13000.0, 400.6849]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 6: [13000.0, 400.6849]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 25: [0.1, 0.001]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 5: [13000.0, 400.6849]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 3 cost terms from generator 15: Float64[]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 27: [0.1, 0.001]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 28: [0.1, 0.001]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 26: [0.1, 0.001]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 30: [0.1, 0.001]\u001b[39m\n",
      "Academic license - for non-commercial use only\n",
      "Academic license - for non-commercial use only\n",
      "Gurobi Optimizer version 9.0.2 build v9.0.2rc0 (linux64)\n",
      "Optimize a model with 336 rows, 310 columns and 1167 nonzeros\n",
      "Model fingerprint: 0x554a3aed\n",
      "Model has 22 quadratic objective terms\n",
      "Model has 110 quadratic constraints\n",
      "Coefficient statistics:\n",
      "  Matrix range     [3e-01, 7e+01]\n",
      "  QMatrix range    [1e+00, 1e+00]\n",
      "  Objective range  [1e-01, 1e+04]\n",
      "  QObjective range [4e+00, 7e+03]\n",
      "  Bounds range     [1e-02, 7e+00]\n",
      "  RHS range        [1e-01, 6e+00]\n",
      "  QRHS range       [3e+00, 2e+01]\n",
      "Presolve removed 2 rows and 32 columns\n",
      "Presolve time: 0.00s\n",
      "Presolved: 963 rows, 424 columns, 1897 nonzeros\n",
      "Presolved model has 111 second-order cone constraints\n",
      "Ordering time: 0.00s\n",
      "\n",
      "Barrier statistics:\n",
      " AA' NZ     : 1.018e+04\n",
      " Factor NZ  : 1.598e+04 (roughly 1 MByte of memory)\n",
      " Factor Ops : 3.750e+05 (less than 1 second per iteration)\n",
      " Threads    : 1\n",
      "\n",
      "                  Objective                Residual\n",
      "Iter       Primal          Dual         Primal    Dual     Compl     Time\n",
      "   0   8.22103374e+06  1.06248080e+05  3.34e+02 2.72e+03  6.04e+05     0s\n",
      "   1   1.95593865e+06 -5.39563891e+06  9.01e+01 4.07e+02  1.25e+05     0s\n",
      "   2   7.58463343e+05 -4.84109715e+06  2.67e+01 1.51e+02  3.96e+04     0s\n",
      "   3   2.66293844e+05 -4.08426367e+06  4.94e+00 8.25e+01  1.03e+04     0s\n",
      "   4   1.90956643e+05 -2.56223535e+06  1.19e+00 3.18e+01  3.62e+03     0s\n",
      "   5   1.39670034e+05 -1.28921681e+06  2.71e-01 1.40e+01  1.58e+03     0s\n",
      "   6   1.09380469e+05 -3.39209754e+05  1.47e-01 3.30e+00  5.11e+02     0s\n",
      "   7   1.04557577e+05 -1.30464317e+05  9.21e-02 1.63e+00  2.68e+02     0s\n",
      "   8   1.03005326e+05 -1.40990367e+04  7.49e-02 7.63e-01  1.36e+02     0s\n",
      "   9   1.03232916e+05  3.63218249e+04  5.32e-02 4.16e-01  8.22e+01     0s\n",
      "  10   1.03003632e+05  6.97791772e+04  4.20e-02 2.12e-01  4.80e+01     0s\n",
      "  11   1.05193645e+05  8.42760561e+04  2.47e-02 1.32e-01  3.10e+01     0s\n",
      "  12   1.06537818e+05  9.62010101e+04  1.43e-02 6.67e-02  1.72e+01     0s\n",
      "  13   1.07167648e+05  1.00550575e+05  9.77e-03 4.54e-02  1.21e+01     0s\n",
      "  14   1.08602168e+05  1.05111978e+05  5.39e-03 2.49e-02  6.63e+00     0s\n",
      "  15   1.09730995e+05  1.08150880e+05  2.40e-03 1.13e-02  3.06e+00     0s\n",
      "  16   1.10552866e+05  1.10285483e+05  6.66e-04 1.94e-03  7.53e-01     0s\n",
      "  17   1.10792255e+05  1.10808580e+05  1.04e-04 6.06e-05  6.73e-02     0s\n",
      "  18   1.10837935e+05  1.10837615e+05  4.44e-06 5.53e-06  3.96e-03     0s\n",
      "  19   1.10839027e+05  1.10838905e+05  6.96e-07 1.42e-06  6.79e-04     0s\n",
      "  20   1.10839072e+05  1.10839051e+05  1.70e-07 2.82e-07  1.59e-04     0s\n",
      "  21   1.10839091e+05  1.10839093e+05  2.09e-08 1.43e-08  1.58e-05     0s\n",
      "  22   1.10839094e+05  1.10839095e+05  7.10e-09 1.46e-06  5.46e-06     0s\n",
      "  23   1.10839096e+05  1.10839096e+05  2.37e-09 2.62e-06  1.85e-06     0s\n",
      "  24   1.10839097e+05  1.10839096e+05  2.61e-10 2.01e-04  1.88e-07     0s\n",
      "\n",
      "Barrier solved model in 24 iterations and 0.04 seconds\n",
      "Optimal objective 1.10839097e+05\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Dict{String,Any} with 8 entries:\n",
       "  \"solve_time\"         => 0.035476\n",
       "  \"optimizer\"          => \"Gurobi\"\n",
       "  \"termination_status\" => OPTIMAL\n",
       "  \"dual_status\"        => NO_SOLUTION\n",
       "  \"primal_status\"      => FEASIBLE_POINT\n",
       "  \"objective\"          => 1.10839e5\n",
       "  \"solution\"           => Dict{String,Any}(\"baseMVA\"=>100.0,\"branch\"=>Dict{Stri…\n",
       "  \"objective_lb\"       => -Inf"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grb_soc = run_opf(file, SOCWRPowerModel, Gurobi.Optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m[info | PowerModels]: extending matpower format with data: areas 4x3\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 1: [13000.0, 400.6849]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 29: [0.1, 0.001]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 2: [13000.0, 400.6849]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 6: [13000.0, 400.6849]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 25: [0.1, 0.001]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 5: [13000.0, 400.6849]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 3 cost terms from generator 15: Float64[]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 27: [0.1, 0.001]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 28: [0.1, 0.001]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 26: [0.1, 0.001]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 30: [0.1, 0.001]\u001b[39m\n",
      "Min 3284.12 0_pg[18]² + 71.7 0_pg[12]² + 83.42 0_pg[32]² + 3284.12 0_pg[16]² + 526.7199999999999 0_pg[11]² + 83.42 0_pg[21]² + 141.42 0_pg[7]² + 526.7199999999999 0_pg[10]² + 526.7199999999999 0_pg[9]² + 3284.12 0_pg[19]² + 3284.12 0_pg[17]² + 141.42 0_pg[8]² + 83.42 0_pg[22]² + 2.13 0_pg[24]² + 141.42 0_pg[4]² + 141.42 0_pg[3]² + 3284.12 0_pg[20]² + 2.13 0_pg[23]² + 71.7 0_pg[13]² + 83.42 0_pg[31]² + 71.7 0_pg[14]² + 48.95 0_pg[33]² + 5656.4 0_pg[18] + 4858.04 0_pg[12] + 13000 0_pg[1] + 1238.83 0_pg[32] + 13000 0_pg[2] + 5656.4 0_pg[16] + 4366.15 0_pg[11] + 1238.83 0_pg[21] + 1608.11 0_pg[7] + 0.1 0_pg[26] + 0.1 0_pg[25] + 4366.15 0_pg[10] + 4366.15 0_pg[9] + 0.1 0_pg[29] + 5656.4 0_pg[19] + 5656.4 0_pg[17] + 1608.11 0_pg[8] + 1238.83 0_pg[22] + 13000 0_pg[6] + 442.31 0_pg[24] + 1608.11 0_pg[4] + 1608.11 0_pg[3] + 0.1 0_pg[28] + 13000 0_pg[5] + 5656.4 0_pg[20] + 442.31 0_pg[23] + 4858.04 0_pg[13] + 1238.83 0_pg[31] + 4858.04 0_pg[14] + 0.1 0_pg[27] + 1184.95 0_pg[33] + 0.1 0_pg[30] + 10711.5531\n",
      "Subject to\n",
      " 0_vi[13] = 0.0\n",
      " 0_p[(1, 1, 2)] + 0_p[(2, 1, 3)] + 0_p[(3, 1, 5)] - 0_pg[1] - 0_pg[2] - 0_pg[4] - 0_pg[3] = -2.073\n",
      " 0_q[(1, 1, 2)] + 0_q[(2, 1, 3)] + 0_q[(3, 1, 5)] - 0_qg[1] - 0_qg[2] - 0_qg[4] - 0_qg[3] = -0.22\n",
      " 0_p[(4, 2, 4)] + 0_p[(5, 2, 6)] + 0_p[(1, 2, 1)] - 0_pg[7] - 0_pg[8] - 0_pg[6] - 0_pg[5] = -1.8618999999999999\n",
      " 0_q[(4, 2, 4)] + 0_q[(5, 2, 6)] + 0_q[(1, 2, 1)] - 0_qg[7] - 0_qg[8] - 0_qg[6] - 0_qg[5] = -0.2\n",
      " 0_p[(29, 16, 19)] + 0_p[(28, 16, 17)] + 0_p[(24, 16, 15)] + 0_p[(23, 16, 14)] - 0_pg[22] = -1.9195\n",
      " 0_q[(29, 16, 19)] + 0_q[(28, 16, 17)] + 0_q[(24, 16, 15)] + 0_q[(23, 16, 14)] - 0_qg[22] = -0.2\n",
      " 0_p[(18, 11, 13)] + 0_p[(19, 11, 14)] + 0_p[(16, 11, 10)] + 0_p[(14, 11, 9)] = 0.0\n",
      " 0_q[(18, 11, 13)] + 0_q[(19, 11, 14)] + 0_q[(16, 11, 10)] + 0_q[(14, 11, 9)] = 0.0\n",
      " 0_p[(38, 21, 22)] + 0_p[(32, 21, 18)] + 0_p[(26, 21, 15)] + 0_p[(25, 21, 15)] + 0_p[(33, 21, 18)] - 0_pg[24] = 0.0\n",
      " 0_q[(38, 21, 22)] + 0_q[(32, 21, 18)] + 0_q[(26, 21, 15)] + 0_q[(25, 21, 15)] + 0_q[(33, 21, 18)] - 0_qg[24] = 0.0\n",
      " 0_p[(11, 7, 8)] - 0_pg[11] - 0_pg[10] - 0_pg[9] = -2.3993\n",
      " 0_q[(11, 7, 8)] - 0_qg[11] - 0_qg[10] - 0_qg[9] = -0.25\n",
      " 0_p[(14, 9, 11)] + 0_p[(15, 9, 12)] + 0_p[(12, 9, 8)] + 0_p[(8, 9, 4)] + 0_p[(6, 9, 3)] = -3.3591\n",
      " 0_q[(14, 9, 11)] + 0_q[(15, 9, 12)] + 0_q[(12, 9, 8)] + 0_q[(8, 9, 4)] + 0_q[(6, 9, 3)] = -0.36\n",
      " 0_p[(16, 10, 11)] + 0_p[(17, 10, 12)] + 0_p[(10, 10, 6)] + 0_p[(9, 10, 5)] + 0_p[(13, 10, 8)] = -3.7429\n",
      " 0_q[(16, 10, 11)] + 0_q[(17, 10, 12)] + 0_q[(10, 10, 6)] + 0_q[(9, 10, 5)] + 0_q[(13, 10, 8)] = -0.4\n",
      " 0_p[(35, 19, 20)] + 0_p[(34, 19, 20)] + 0_p[(29, 19, 16)] = -3.4742\n",
      " 0_q[(35, 19, 20)] + 0_q[(34, 19, 20)] + 0_q[(29, 19, 16)] = -0.37\n",
      " 0_p[(31, 17, 22)] + 0_p[(30, 17, 18)] + 0_p[(28, 17, 16)] = 0.0\n",
      " 0_q[(31, 17, 22)] + 0_q[(30, 17, 18)] + 0_q[(28, 17, 16)] = 0.0\n",
      " 0_p[(12, 8, 9)] + 0_p[(13, 8, 10)] + 0_p[(11, 8, 7)] = -3.2823\n",
      " 0_q[(12, 8, 9)] + 0_q[(13, 8, 10)] + 0_q[(11, 8, 7)] = -0.35\n",
      " 0_p[(38, 22, 21)] + 0_p[(31, 22, 17)] - 0_pg[26] - 0_pg[25] - 0_pg[29] - 0_pg[28] - 0_pg[27] - 0_pg[30] = 0.0\n",
      " 0_q[(38, 22, 21)] + 0_q[(31, 22, 17)] - 0_qg[26] - 0_qg[25] - 0_qg[29] - 0_qg[28] - 0_qg[27] - 0_qg[30] = 0.0\n",
      " 0_p[(7, 24, 3)] + 0_p[(27, 24, 15)] = 0.0\n",
      " 0_q[(7, 24, 3)] + 0_q[(27, 24, 15)] = 0.0\n",
      " 0_p[(8, 4, 9)] + 0_p[(4, 4, 2)] = -1.4203999999999999\n",
      " 0_q[(8, 4, 9)] + 0_q[(4, 4, 2)] = -0.15\n",
      " 0_p[(7, 3, 24)] + 0_p[(6, 3, 9)] + 0_p[(2, 3, 1)] = -3.455\n",
      " 0_q[(7, 3, 24)] + 0_q[(6, 3, 9)] + 0_q[(2, 3, 1)] = -0.37\n",
      " 0_p[(9, 5, 10)] + 0_p[(3, 5, 1)] = -1.3628\n",
      " 0_q[(9, 5, 10)] + 0_q[(3, 5, 1)] = -0.14\n",
      " 0_p[(37, 20, 23)] + 0_p[(36, 20, 23)] + 0_p[(35, 20, 19)] + 0_p[(34, 20, 19)] = -2.4569\n",
      " 0_q[(37, 20, 23)] + 0_q[(36, 20, 23)] + 0_q[(35, 20, 19)] + 0_q[(34, 20, 19)] = -0.26\n",
      " 0_p[(21, 23, 12)] + 0_p[(22, 23, 13)] + 0_p[(37, 23, 20)] + 0_p[(36, 23, 20)] - 0_pg[32] - 0_pg[31] - 0_pg[33] = 0.0\n",
      " 0_q[(21, 23, 12)] + 0_q[(22, 23, 13)] + 0_q[(37, 23, 20)] + 0_q[(36, 23, 20)] - 0_qg[32] - 0_qg[31] - 0_qg[33] = 0.0\n",
      " 0_p[(22, 13, 23)] + 0_p[(18, 13, 11)] + 0_p[(20, 13, 12)] - 0_pg[12] - 0_pg[13] - 0_pg[14] = -5.086600000000001\n",
      " 0_q[(22, 13, 23)] + 0_q[(18, 13, 11)] + 0_q[(20, 13, 12)] - 0_qg[12] - 0_qg[13] - 0_qg[14] = -0.54\n",
      " 0_p[(23, 14, 16)] + 0_p[(19, 14, 11)] - 0_pg[15] = -3.7237\n",
      " 0_q[(23, 14, 16)] + 0_q[(19, 14, 11)] - 0_qg[15] = -0.39\n",
      " 0_p[(26, 15, 21)] + 0_p[(25, 15, 21)] + 0_p[(24, 15, 16)] + 0_p[(27, 15, 24)] - 0_pg[18] - 0_pg[16] - 0_pg[21] - 0_pg[19] - 0_pg[17] - 0_pg[20] = -6.084700000000001\n",
      " 0_q[(26, 15, 21)] + 0_q[(25, 15, 21)] + 0_q[(24, 15, 16)] + 0_q[(27, 15, 24)] - 0_qg[18] - 0_qg[16] - 0_qg[21] - 0_qg[19] - 0_qg[17] - 0_qg[20] = -0.64\n",
      " 0_p[(21, 12, 23)] + 0_p[(20, 12, 13)] + 0_p[(17, 12, 10)] + 0_p[(15, 12, 9)] = 0.0\n",
      " 0_q[(21, 12, 23)] + 0_q[(20, 12, 13)] + 0_q[(17, 12, 10)] + 0_q[(15, 12, 9)] = 0.0\n",
      " 0_p[(32, 18, 21)] + 0_p[(33, 18, 21)] + 0_p[(30, 18, 17)] - 0_pg[23] = -6.3918\n",
      " 0_q[(32, 18, 21)] + 0_q[(33, 18, 21)] + 0_q[(30, 18, 17)] - 0_qg[23] = -0.68\n",
      " 0_p[(10, 6, 10)] + 0_p[(5, 6, 2)] = -2.6105\n",
      " 0_vr[6]² + 0_vi[6]² + 0_q[(10, 6, 10)] + 0_q[(5, 6, 2)] = -0.28\n",
      " -2.648753566047321 0_vr[11]² - 2.648753566047321 0_vi[11]² + 2.648753566047321 0_vr[11]*0_vr[13] + 2.648753566047321 0_vi[11]*0_vi[13] - 20.668962253090573 0_vi[11]*0_vr[13] + 20.668962253090573 0_vi[13]*0_vr[11] + 0_p[(18, 11, 13)] = 0.0\n",
      " -20.619012253090574 0_vr[11]² - 20.619012253090574 0_vi[11]² + 20.668962253090573 0_vr[11]*0_vr[13] + 20.668962253090573 0_vi[11]*0_vi[13] + 2.648753566047321 0_vi[11]*0_vr[13] - 2.648753566047321 0_vi[13]*0_vr[11] + 0_q[(18, 11, 13)] = 0.0\n",
      " -2.648753566047321 0_vr[13]² - 2.648753566047321 0_vi[13]² + 2.648753566047321 0_vr[11]*0_vr[13] + 2.648753566047321 0_vi[11]*0_vi[13] + 20.668962253090573 0_vi[11]*0_vr[13] - 20.668962253090573 0_vr[11]*0_vi[13] + 0_p[(18, 13, 11)] = 0.0\n",
      " -20.619012253090574 0_vr[13]² - 20.619012253090574 0_vi[13]² + 20.668962253090573 0_vr[11]*0_vr[13] + 20.668962253090573 0_vi[11]*0_vi[13] - 2.648753566047321 0_vi[11]*0_vr[13] + 2.648753566047321 0_vr[11]*0_vi[13] + 0_q[(18, 13, 11)] = 0.0\n",
      " -1.4682974970169838 0_vr[8]² - 1.4682974970169838 0_vi[8]² + 1.4682974970169838 0_vr[8]*0_vr[9] + 1.4682974970169838 0_vi[8]*0_vi[9] - 5.677187746077377 0_vi[8]*0_vr[9] + 5.677187746077377 0_vi[9]*0_vr[8] + 0_p[(12, 8, 9)] = 0.0\n",
      " -5.654837746077376 0_vr[8]² - 5.654837746077376 0_vi[8]² + 5.677187746077377 0_vr[8]*0_vr[9] + 5.677187746077377 0_vi[8]*0_vi[9] + 1.4682974970169838 0_vi[8]*0_vr[9] - 1.4682974970169838 0_vi[9]*0_vr[8] + 0_q[(12, 8, 9)] = 0.0\n",
      " -1.4682974970169838 0_vr[9]² - 1.4682974970169838 0_vi[9]² + 1.4682974970169838 0_vr[8]*0_vr[9] + 1.4682974970169838 0_vi[8]*0_vi[9] + 5.677187746077377 0_vi[8]*0_vr[9] - 5.677187746077377 0_vr[8]*0_vi[9] + 0_p[(12, 9, 8)] = 0.0\n",
      " -5.654837746077376 0_vr[9]² - 5.654837746077376 0_vi[9]² + 5.677187746077377 0_vr[8]*0_vr[9] + 5.677187746077377 0_vi[8]*0_vi[9] - 1.4682974970169838 0_vi[8]*0_vr[9] + 1.4682974970169838 0_vr[8]*0_vi[9] + 0_q[(12, 9, 8)] = 0.0\n",
      " -13.001950292543883 0_vr[1]² - 13.001950292543883 0_vi[1]² + 13.001950292543883 0_vr[1]*0_vr[2] + 13.001950292543883 0_vi[1]*0_vi[2] - 69.51042656398461 0_vi[1]*0_vr[2] + 69.51042656398461 0_vi[2]*0_vr[1] + 0_p[(1, 1, 2)] = 0.0\n",
      " -69.27987656398462 0_vr[1]² - 69.27987656398462 0_vi[1]² + 69.51042656398461 0_vr[1]*0_vr[2] + 69.51042656398461 0_vi[1]*0_vi[2] + 13.001950292543883 0_vi[1]*0_vr[2] - 13.001950292543883 0_vi[2]*0_vr[1] + 0_q[(1, 1, 2)] = 0.0\n",
      " -13.001950292543883 0_vr[2]² - 13.001950292543883 0_vi[2]² + 13.001950292543883 0_vr[1]*0_vr[2] + 13.001950292543883 0_vi[1]*0_vi[2] + 69.51042656398461 0_vi[1]*0_vr[2] - 69.51042656398461 0_vr[1]*0_vi[2] + 0_p[(1, 2, 1)] = 0.0\n",
      " -69.27987656398462 0_vr[2]² - 69.27987656398462 0_vi[2]² + 69.51042656398461 0_vr[1]*0_vr[2] + 69.51042656398461 0_vi[1]*0_vi[2] - 13.001950292543883 0_vi[1]*0_vr[2] + 13.001950292543883 0_vr[1]*0_vi[2] + 0_q[(1, 2, 1)] = 0.0\n",
      " -4.840839078773654 0_vr[18]² - 4.840839078773654 0_vi[18]² + 4.840839078773654 0_vr[18]*0_vr[21] + 4.840839078773654 0_vi[18]*0_vi[21] - 37.99325216370838 0_vi[18]*0_vr[21] + 37.99325216370838 0_vi[21]*0_vr[18] + 0_p[(32, 18, 21)] = 0.0\n",
      " -37.966002163708374 0_vr[18]² - 37.966002163708374 0_vi[18]² + 37.99325216370838 0_vr[18]*0_vr[21] + 37.99325216370838 0_vi[18]*0_vi[21] + 4.840839078773654 0_vi[18]*0_vr[21] - 4.840839078773654 0_vi[21]*0_vr[18] + 0_q[(32, 18, 21)] = 0.0\n",
      " -4.840839078773654 0_vr[21]² - 4.840839078773654 0_vi[21]² + 4.840839078773654 0_vr[18]*0_vr[21] + 4.840839078773654 0_vi[18]*0_vi[21] + 37.99325216370838 0_vi[18]*0_vr[21] - 37.99325216370838 0_vr[18]*0_vi[21] + 0_p[(32, 21, 18)] = 0.0\n",
      " -37.966002163708374 0_vr[21]² - 37.966002163708374 0_vi[21]² + 37.99325216370838 0_vr[18]*0_vr[21] + 37.99325216370838 0_vi[18]*0_vi[21] - 4.840839078773654 0_vi[18]*0_vr[21] + 4.840839078773654 0_vr[18]*0_vi[21] + 0_q[(32, 21, 18)] = 0.0\n",
      " -1.1473818259762203 0_vr[1]² - 1.1473818259762203 0_vi[1]² + 1.1473818259762203 0_vr[1]*0_vr[3] + 1.1473818259762203 0_vi[1]*0_vi[3] - 4.438224205973951 0_vi[1]*0_vr[3] + 4.438224205973951 0_vi[3]*0_vr[1] + 0_p[(2, 1, 3)] = 0.0\n",
      " -4.409624205973951 0_vr[1]² - 4.409624205973951 0_vi[1]² + 4.438224205973951 0_vr[1]*0_vr[3] + 4.438224205973951 0_vi[1]*0_vi[3] + 1.1473818259762203 0_vi[1]*0_vr[3] - 1.1473818259762203 0_vi[3]*0_vr[1] + 0_q[(2, 1, 3)] = 0.0\n",
      " -1.1473818259762203 0_vr[3]² - 1.1473818259762203 0_vi[3]² + 1.1473818259762203 0_vr[1]*0_vr[3] + 1.1473818259762203 0_vi[1]*0_vi[3] + 4.438224205973951 0_vi[1]*0_vr[3] - 4.438224205973951 0_vr[1]*0_vi[3] + 0_p[(2, 3, 1)] = 0.0\n",
      " -4.409624205973951 0_vr[3]² - 4.409624205973951 0_vi[3]² + 4.438224205973951 0_vr[1]*0_vr[3] + 4.438224205973951 0_vi[1]*0_vi[3] - 1.1473818259762203 0_vi[1]*0_vr[3] + 1.1473818259762203 0_vr[1]*0_vi[3] + 0_q[(2, 3, 1)] = 0.0\n",
      " -0.31381761613277964 0_vr[10]² - 0.31381761613277964 0_vi[10]² + 0.3200939684554353 0_vr[10]*0_vr[11] + 0.3200939684554353 0_vi[10]*0_vi[11] - 11.676471284091749 0_vi[10]*0_vr[11] + 11.676471284091749 0_vi[11]*0_vr[10] + 0_p[(16, 10, 11)] = 0.0\n",
      " -11.447520866756616 0_vr[10]² - 11.447520866756616 0_vi[10]² + 11.676471284091749 0_vr[10]*0_vr[11] + 11.676471284091749 0_vi[10]*0_vi[11] + 0.3200939684554353 0_vi[10]*0_vr[11] - 0.3200939684554353 0_vi[11]*0_vr[10] + 0_q[(16, 10, 11)] = 0.0\n",
      " -0.32649584782454394 0_vr[11]² - 0.32649584782454394 0_vi[11]² + 0.3200939684554353 0_vr[10]*0_vr[11] + 0.3200939684554353 0_vi[10]*0_vi[11] + 11.676471284091749 0_vi[10]*0_vr[11] - 11.676471284091749 0_vr[10]*0_vi[11] + 0_p[(16, 11, 10)] = 0.0\n",
      " -11.910000709773582 0_vr[11]² - 11.910000709773582 0_vi[11]² + 11.676471284091749 0_vr[10]*0_vr[11] + 11.676471284091749 0_vi[10]*0_vi[11] - 0.3200939684554353 0_vi[10]*0_vr[11] + 0.3200939684554353 0_vr[10]*0_vi[11] + 0_q[(16, 11, 10)] = 0.0\n",
      " -3.9525003915212653 0_vr[7]² - 3.9525003915212653 0_vi[7]² + 3.9525003915212653 0_vr[7]*0_vr[8] + 3.9525003915212653 0_vi[7]*0_vi[8] - 15.263114719459477 0_vi[7]*0_vr[8] + 15.263114719459477 0_vi[8]*0_vr[7] + 0_p[(11, 7, 8)] = 0.0\n",
      " -15.254814719459477 0_vr[7]² - 15.254814719459477 0_vi[7]² + 15.263114719459477 0_vr[7]*0_vr[8] + 15.263114719459477 0_vi[7]*0_vi[8] + 3.9525003915212653 0_vi[7]*0_vr[8] - 3.9525003915212653 0_vi[8]*0_vr[7] + 0_q[(11, 7, 8)] = 0.0\n",
      " -3.9525003915212653 0_vr[8]² - 3.9525003915212653 0_vi[8]² + 3.9525003915212653 0_vr[7]*0_vr[8] + 3.9525003915212653 0_vi[7]*0_vi[8] + 15.263114719459477 0_vi[7]*0_vr[8] - 15.263114719459477 0_vr[7]*0_vi[8] + 0_p[(11, 8, 7)] = 0.0\n",
      " -15.254814719459477 0_vr[8]² - 15.254814719459477 0_vi[8]² + 15.263114719459477 0_vr[7]*0_vr[8] + 15.263114719459477 0_vi[7]*0_vi[8] - 3.9525003915212653 0_vi[7]*0_vr[8] + 3.9525003915212653 0_vr[7]*0_vi[8] + 0_q[(11, 8, 7)] = 0.0\n",
      " -1.3072832545449176 0_vr[12]² - 1.3072832545449176 0_vi[12]² + 1.3072832545449176 0_vr[12]*0_vr[23] + 1.3072832545449176 0_vi[12]*0_vi[23] - 10.184158257180568 0_vi[12]*0_vr[23] + 10.184158257180568 0_vi[23]*0_vr[12] + 0_p[(21, 12, 23)] = 0.0\n",
      " -10.082658257180569 0_vr[12]² - 10.082658257180569 0_vi[12]² + 10.184158257180568 0_vr[12]*0_vr[23] + 10.184158257180568 0_vi[12]*0_vi[23] + 1.3072832545449176 0_vi[12]*0_vr[23] - 1.3072832545449176 0_vi[23]*0_vr[12] + 0_q[(21, 12, 23)] = 0.0\n",
      " -1.3072832545449176 0_vr[23]² - 1.3072832545449176 0_vi[23]² + 1.3072832545449176 0_vr[12]*0_vr[23] + 1.3072832545449176 0_vi[12]*0_vi[23] + 10.184158257180568 0_vi[12]*0_vr[23] - 10.184158257180568 0_vr[12]*0_vi[23] + 0_p[(21, 23, 12)] = 0.0\n",
      " -10.082658257180569 0_vr[23]² - 10.082658257180569 0_vi[23]² + 10.184158257180568 0_vr[12]*0_vr[23] + 10.184158257180568 0_vi[12]*0_vi[23] - 1.3072832545449176 0_vi[12]*0_vr[23] + 1.3072832545449176 0_vr[12]*0_vi[23] + 0_q[(21, 23, 12)] = 0.0\n",
      " -0.3077536505085719 0_vr[3]² - 0.3077536505085719 0_vi[3]² + 0.31698626002382907 0_vr[3]*0_vr[24] + 0.31698626002382907 0_vi[3]*0_vi[24] - 11.56310748521707 0_vi[3]*0_vr[24] + 11.56310748521707 0_vi[24]*0_vr[3] + 0_p[(7, 3, 24)] = 0.0\n",
      " -11.226317946812689 0_vr[3]² - 11.226317946812689 0_vi[3]² + 11.56310748521707 0_vr[3]*0_vr[24] + 11.56310748521707 0_vi[3]*0_vi[24] + 0.31698626002382907 0_vi[3]*0_vr[24] - 0.31698626002382907 0_vi[24]*0_vr[3] + 0_q[(7, 3, 24)] = 0.0\n",
      " -0.32649584782454394 0_vr[24]² - 0.32649584782454394 0_vi[24]² + 0.31698626002382907 0_vr[3]*0_vr[24] + 0.31698626002382907 0_vi[3]*0_vi[24] + 11.56310748521707 0_vi[3]*0_vr[24] - 11.56310748521707 0_vr[3]*0_vi[24] + 0_p[(7, 24, 3)] = 0.0\n",
      " -11.910000709773582 0_vr[24]² - 11.910000709773582 0_vi[24]² + 11.56310748521707 0_vr[3]*0_vr[24] + 11.56310748521707 0_vi[3]*0_vi[24] - 0.31698626002382907 0_vi[3]*0_vr[24] + 0.31698626002382907 0_vr[3]*0_vi[24] + 0_q[(7, 24, 3)] = 0.0\n",
      " -2.5812372730662227 0_vr[15]² - 2.5812372730662227 0_vi[15]² + 2.5812372730662227 0_vr[15]*0_vr[21] + 2.5812372730662227 0_vi[15]*0_vi[21] - 20.07628990162618 0_vi[15]*0_vr[21] + 20.07628990162618 0_vi[21]*0_vr[15] + 0_p[(26, 15, 21)] = 0.0\n",
      " -20.02478990162618 0_vr[15]² - 20.02478990162618 0_vi[15]² + 20.07628990162618 0_vr[15]*0_vr[21] + 20.07628990162618 0_vi[15]*0_vi[21] + 2.5812372730662227 0_vi[15]*0_vr[21] - 2.5812372730662227 0_vi[21]*0_vr[15] + 0_q[(26, 15, 21)] = 0.0\n",
      " -2.5812372730662227 0_vr[21]² - 2.5812372730662227 0_vi[21]² + 2.5812372730662227 0_vr[15]*0_vr[21] + 2.5812372730662227 0_vi[15]*0_vi[21] + 20.07628990162618 0_vi[15]*0_vr[21] - 20.07628990162618 0_vr[15]*0_vi[21] + 0_p[(26, 21, 15)] = 0.0\n",
      " -20.02478990162618 0_vr[21]² - 20.02478990162618 0_vi[21]² + 20.07628990162618 0_vr[15]*0_vr[21] + 20.07628990162618 0_vi[15]*0_vi[21] - 2.5812372730662227 0_vi[15]*0_vr[21] + 2.5812372730662227 0_vr[15]*0_vi[21] + 0_q[(26, 21, 15)] = 0.0\n",
      " -2.5812372730662227 0_vr[15]² - 2.5812372730662227 0_vi[15]² + 2.5812372730662227 0_vr[15]*0_vr[21] + 2.5812372730662227 0_vi[15]*0_vi[21] - 20.07628990162618 0_vi[15]*0_vr[21] + 20.07628990162618 0_vi[21]*0_vr[15] + 0_p[(25, 15, 21)] = 0.0\n",
      " -20.02478990162618 0_vr[15]² - 20.02478990162618 0_vi[15]² + 20.07628990162618 0_vr[15]*0_vr[21] + 20.07628990162618 0_vi[15]*0_vi[21] + 2.5812372730662227 0_vi[15]*0_vr[21] - 2.5812372730662227 0_vi[21]*0_vr[15] + 0_q[(25, 15, 21)] = 0.0\n",
      " -2.5812372730662227 0_vr[21]² - 2.5812372730662227 0_vi[21]² + 2.5812372730662227 0_vr[15]*0_vr[21] + 2.5812372730662227 0_vi[15]*0_vi[21] + 20.07628990162618 0_vi[15]*0_vr[21] - 20.07628990162618 0_vr[15]*0_vi[21] + 0_p[(25, 21, 15)] = 0.0\n",
      " -20.02478990162618 0_vr[21]² - 20.02478990162618 0_vi[21]² + 20.07628990162618 0_vr[15]*0_vr[21] + 20.07628990162618 0_vi[15]*0_vi[21] - 2.5812372730662227 0_vi[15]*0_vr[21] + 2.5812372730662227 0_vr[15]*0_vi[21] + 0_q[(25, 21, 15)] = 0.0\n",
      " -3.1991569280566057 0_vr[19]² - 3.1991569280566057 0_vi[19]² + 3.1991569280566057 0_vr[19]*0_vr[20] + 3.1991569280566057 0_vi[19]*0_vi[20] - 24.840512617851292 0_vi[19]*0_vr[20] + 24.840512617851292 0_vi[20]*0_vr[19] + 0_p[(35, 19, 20)] = 0.0\n",
      " -24.79886261785129 0_vr[19]² - 24.79886261785129 0_vi[19]² + 24.840512617851292 0_vr[19]*0_vr[20] + 24.840512617851292 0_vi[19]*0_vi[20] + 3.1991569280566057 0_vi[19]*0_vr[20] - 3.1991569280566057 0_vi[20]*0_vr[19] + 0_q[(35, 19, 20)] = 0.0\n",
      " -3.1991569280566057 0_vr[20]² - 3.1991569280566057 0_vi[20]² + 3.1991569280566057 0_vr[19]*0_vr[20] + 3.1991569280566057 0_vi[19]*0_vi[20] + 24.840512617851292 0_vi[19]*0_vr[20] - 24.840512617851292 0_vr[19]*0_vi[20] + 0_p[(35, 20, 19)] = 0.0\n",
      " -24.79886261785129 0_vr[20]² - 24.79886261785129 0_vi[20]² + 24.840512617851292 0_vr[19]*0_vr[20] + 24.840512617851292 0_vi[19]*0_vi[20] - 3.1991569280566057 0_vi[19]*0_vr[20] + 3.1991569280566057 0_vr[19]*0_vi[20] + 0_q[(35, 20, 19)] = 0.0\n",
      " -3.1991569280566057 0_vr[19]² - 3.1991569280566057 0_vi[19]² + 3.1991569280566057 0_vr[19]*0_vr[20] + 3.1991569280566057 0_vi[19]*0_vi[20] - 24.840512617851292 0_vi[19]*0_vr[20] + 24.840512617851292 0_vi[20]*0_vr[19] + 0_p[(34, 19, 20)] = 0.0\n",
      " -24.79886261785129 0_vr[19]² - 24.79886261785129 0_vi[19]² + 24.840512617851292 0_vr[19]*0_vr[20] + 24.840512617851292 0_vi[19]*0_vi[20] + 3.1991569280566057 0_vi[19]*0_vr[20] - 3.1991569280566057 0_vi[20]*0_vr[19] + 0_q[(34, 19, 20)] = 0.0\n",
      " -3.1991569280566057 0_vr[20]² - 3.1991569280566057 0_vi[20]² + 3.1991569280566057 0_vr[19]*0_vr[20] + 3.1991569280566057 0_vi[19]*0_vi[20] + 24.840512617851292 0_vi[19]*0_vr[20] - 24.840512617851292 0_vr[19]*0_vi[20] + 0_p[(34, 20, 19)] = 0.0\n",
      " -24.79886261785129 0_vr[20]² - 24.79886261785129 0_vi[20]² + 24.840512617851292 0_vr[19]*0_vr[20] + 24.840512617851292 0_vi[19]*0_vi[20] - 3.1991569280566057 0_vi[19]*0_vr[20] + 3.1991569280566057 0_vr[19]*0_vi[20] + 0_q[(34, 20, 19)] = 0.0\n",
      " -5.52883286338254 0_vr[16]² - 5.52883286338254 0_vi[16]² + 5.52883286338254 0_vr[16]*0_vr[19] + 5.52883286338254 0_vi[16]*0_vi[19] - 42.572013048045555 0_vi[16]*0_vr[19] + 42.572013048045555 0_vi[19]*0_vr[16] + 0_p[(29, 16, 19)] = 0.0\n",
      " -42.54776304804555 0_vr[16]² - 42.54776304804555 0_vi[16]² + 42.572013048045555 0_vr[16]*0_vr[19] + 42.572013048045555 0_vi[16]*0_vi[19] + 5.52883286338254 0_vi[16]*0_vr[19] - 5.52883286338254 0_vi[19]*0_vr[16] + 0_q[(29, 16, 19)] = 0.0\n",
      " -5.52883286338254 0_vr[19]² - 5.52883286338254 0_vi[19]² + 5.52883286338254 0_vr[16]*0_vr[19] + 5.52883286338254 0_vi[16]*0_vi[19] + 42.572013048045555 0_vi[16]*0_vr[19] - 42.572013048045555 0_vr[16]*0_vi[19] + 0_p[(29, 19, 16)] = 0.0\n",
      " -42.54776304804555 0_vr[19]² - 42.54776304804555 0_vi[19]² + 42.572013048045555 0_vr[16]*0_vr[19] + 42.572013048045555 0_vi[16]*0_vi[19] - 5.52883286338254 0_vi[16]*0_vr[19] + 5.52883286338254 0_vr[16]*0_vi[19] + 0_q[(29, 19, 16)] = 0.0\n",
      " -3.6071478619214945 0_vr[6]² - 3.6071478619214945 0_vi[6]² + 3.6071478619214945 0_vr[6]*0_vr[10] + 3.6071478619214945 0_vi[6]*0_vi[10] - 15.700175945773411 0_vi[6]*0_vr[10] + 15.700175945773411 0_vi[10]*0_vr[6] + 0_p[(10, 6, 10)] = 0.0\n",
      " -14.470675945773412 0_vr[6]² - 14.470675945773412 0_vi[6]² + 15.700175945773411 0_vr[6]*0_vr[10] + 15.700175945773411 0_vi[6]*0_vi[10] + 3.6071478619214945 0_vi[6]*0_vr[10] - 3.6071478619214945 0_vi[10]*0_vr[6] + 0_q[(10, 6, 10)] = 0.0\n",
      " -3.6071478619214945 0_vr[10]² - 3.6071478619214945 0_vi[10]² + 3.6071478619214945 0_vr[6]*0_vr[10] + 3.6071478619214945 0_vi[6]*0_vi[10] + 15.700175945773411 0_vi[6]*0_vr[10] - 15.700175945773411 0_vr[6]*0_vi[10] + 0_p[(10, 10, 6)] = 0.0\n",
      " -14.470675945773412 0_vr[10]² - 14.470675945773412 0_vi[10]² + 15.700175945773411 0_vr[6]*0_vr[10] + 15.700175945773411 0_vi[6]*0_vi[10] - 3.6071478619214945 0_vi[6]*0_vr[10] + 3.6071478619214945 0_vr[6]*0_vi[10] + 0_q[(10, 10, 6)] = 0.0\n",
      " -2.7414620890662555 0_vr[5]² - 2.7414620890662555 0_vi[5]² + 2.7414620890662555 0_vr[5]*0_vr[10] + 2.7414620890662555 0_vi[5]*0_vi[10] - 10.617153616866243 0_vi[5]*0_vr[10] + 10.617153616866243 0_vi[10]*0_vr[5] + 0_p[(9, 5, 10)] = 0.0\n",
      " -10.605203616866243 0_vr[5]² - 10.605203616866243 0_vi[5]² + 10.617153616866243 0_vr[5]*0_vr[10] + 10.617153616866243 0_vi[5]*0_vi[10] + 2.7414620890662555 0_vi[5]*0_vr[10] - 2.7414620890662555 0_vi[10]*0_vr[5] + 0_q[(9, 5, 10)] = 0.0\n",
      " -2.7414620890662555 0_vr[10]² - 2.7414620890662555 0_vi[10]² + 2.7414620890662555 0_vr[5]*0_vr[10] + 2.7414620890662555 0_vi[5]*0_vi[10] + 10.617153616866243 0_vi[5]*0_vr[10] - 10.617153616866243 0_vr[5]*0_vi[10] + 0_p[(9, 10, 5)] = 0.0\n",
      " -10.605203616866243 0_vr[10]² - 10.605203616866243 0_vi[10]² + 10.617153616866243 0_vr[5]*0_vr[10] + 10.617153616866243 0_vi[5]*0_vi[10] - 2.7414620890662555 0_vi[5]*0_vr[10] + 2.7414620890662555 0_vr[5]*0_vi[10] + 0_q[(9, 10, 5)] = 0.0\n",
      " -3.0398558883134434 0_vr[11]² - 3.0398558883134434 0_vi[11]² + 3.0398558883134434 0_vr[11]*0_vr[14] + 3.0398558883134434 0_vi[11]*0_vi[14] - 23.530736320648504 0_vi[11]*0_vr[14] + 23.530736320648504 0_vi[14]*0_vr[11] + 0_p[(19, 11, 14)] = 0.0\n",
      " -23.486786320648505 0_vr[11]² - 23.486786320648505 0_vi[11]² + 23.530736320648504 0_vr[11]*0_vr[14] + 23.530736320648504 0_vi[11]*0_vi[14] + 3.0398558883134434 0_vi[11]*0_vr[14] - 3.0398558883134434 0_vi[14]*0_vr[11] + 0_q[(19, 11, 14)] = 0.0\n",
      " -3.0398558883134434 0_vr[14]² - 3.0398558883134434 0_vi[14]² + 3.0398558883134434 0_vr[11]*0_vr[14] + 3.0398558883134434 0_vi[11]*0_vi[14] + 23.530736320648504 0_vi[11]*0_vr[14] - 23.530736320648504 0_vr[11]*0_vi[14] + 0_p[(19, 14, 11)] = 0.0\n",
      " -23.486786320648505 0_vr[14]² - 23.486786320648505 0_vi[14]² + 23.530736320648504 0_vr[11]*0_vr[14] + 23.530736320648504 0_vi[11]*0_vi[14] - 3.0398558883134434 0_vi[11]*0_vr[14] + 3.0398558883134434 0_vr[11]*0_vi[14] + 0_q[(19, 14, 11)] = 0.0\n",
      " -0.31381761613277964 0_vr[10]² - 0.31381761613277964 0_vi[10]² + 0.3200939684554353 0_vr[10]*0_vr[12] + 0.3200939684554353 0_vi[10]*0_vi[12] - 11.676471284091749 0_vi[10]*0_vr[12] + 11.676471284091749 0_vi[12]*0_vr[10] + 0_p[(17, 10, 12)] = 0.0\n",
      " -11.447520866756616 0_vr[10]² - 11.447520866756616 0_vi[10]² + 11.676471284091749 0_vr[10]*0_vr[12] + 11.676471284091749 0_vi[10]*0_vi[12] + 0.3200939684554353 0_vi[10]*0_vr[12] - 0.3200939684554353 0_vi[12]*0_vr[10] + 0_q[(17, 10, 12)] = 0.0\n",
      " -0.32649584782454394 0_vr[12]² - 0.32649584782454394 0_vi[12]² + 0.3200939684554353 0_vr[10]*0_vr[12] + 0.3200939684554353 0_vi[10]*0_vi[12] + 11.676471284091749 0_vi[10]*0_vr[12] - 11.676471284091749 0_vr[10]*0_vi[12] + 0_p[(17, 12, 10)] = 0.0\n",
      " -11.910000709773582 0_vr[12]² - 11.910000709773582 0_vi[12]² + 11.676471284091749 0_vr[10]*0_vr[12] + 11.676471284091749 0_vi[10]*0_vi[12] - 0.3200939684554353 0_vi[10]*0_vr[12] + 0.3200939684554353 0_vr[10]*0_vi[12] + 0_q[(17, 12, 10)] = 0.0\n",
      " -2.3361369882835756 0_vr[4]² - 2.3361369882835756 0_vi[4]² + 2.3361369882835756 0_vr[4]*0_vr[9] + 2.3361369882835756 0_vi[4]*0_vi[9] - 9.039455436007716 0_vi[4]*0_vr[9] + 9.039455436007716 0_vi[9]*0_vr[4] + 0_p[(8, 4, 9)] = 0.0\n",
      " -9.025405436007716 0_vr[4]² - 9.025405436007716 0_vi[4]² + 9.039455436007716 0_vr[4]*0_vr[9] + 9.039455436007716 0_vi[4]*0_vi[9] + 2.3361369882835756 0_vi[4]*0_vr[9] - 2.3361369882835756 0_vi[9]*0_vr[4] + 0_q[(8, 4, 9)] = 0.0\n",
      " -2.3361369882835756 0_vr[9]² - 2.3361369882835756 0_vi[9]² + 2.3361369882835756 0_vr[4]*0_vr[9] + 2.3361369882835756 0_vi[4]*0_vi[9] + 9.039455436007716 0_vi[4]*0_vr[9] - 9.039455436007716 0_vr[4]*0_vi[9] + 0_p[(8, 9, 4)] = 0.0\n",
      " -9.025405436007716 0_vr[9]² - 9.025405436007716 0_vi[9]² + 9.039455436007716 0_vr[4]*0_vr[9] + 9.039455436007716 0_vi[4]*0_vi[9] - 2.3361369882835756 0_vi[4]*0_vr[9] + 2.3361369882835756 0_vr[4]*0_vi[9] + 0_q[(8, 9, 4)] = 0.0\n",
      " -1.459477796214825 0_vr[13]² - 1.459477796214825 0_vi[13]² + 1.459477796214825 0_vr[13]*0_vr[23] + 1.459477796214825 0_vi[13]*0_vi[23] - 11.373408051583995 0_vi[13]*0_vr[23] + 11.373408051583995 0_vi[23]*0_vr[13] + 0_p[(22, 13, 23)] = 0.0\n",
      " -11.282508051583996 0_vr[13]² - 11.282508051583996 0_vi[13]² + 11.373408051583995 0_vr[13]*0_vr[23] + 11.373408051583995 0_vi[13]*0_vi[23] + 1.459477796214825 0_vi[13]*0_vr[23] - 1.459477796214825 0_vi[23]*0_vr[13] + 0_q[(22, 13, 23)] = 0.0\n",
      " -1.459477796214825 0_vr[23]² - 1.459477796214825 0_vi[23]² + 1.459477796214825 0_vr[13]*0_vr[23] + 1.459477796214825 0_vi[13]*0_vi[23] + 11.373408051583995 0_vi[13]*0_vr[23] - 11.373408051583995 0_vr[13]*0_vi[23] + 0_p[(22, 23, 13)] = 0.0\n",
      " -11.282508051583996 0_vr[23]² - 11.282508051583996 0_vi[23]² + 11.373408051583995 0_vr[13]*0_vr[23] + 11.373408051583995 0_vi[13]*0_vi[23] - 1.459477796214825 0_vi[13]*0_vr[23] + 1.459477796214825 0_vr[13]*0_vi[23] + 0_q[(22, 23, 13)] = 0.0\n",
      " -2.0384337416377893 0_vr[3]² - 2.0384337416377893 0_vi[3]² + 2.0384337416377893 0_vr[3]*0_vr[9] + 2.0384337416377893 0_vi[3]*0_vi[9] - 7.875766729055094 0_vi[3]*0_vr[9] + 7.875766729055094 0_vi[9]*0_vr[3] + 0_p[(6, 3, 9)] = 0.0\n",
      " -7.859666729055094 0_vr[3]² - 7.859666729055094 0_vi[3]² + 7.875766729055094 0_vr[3]*0_vr[9] + 7.875766729055094 0_vi[3]*0_vi[9] + 2.0384337416377893 0_vi[3]*0_vr[9] - 2.0384337416377893 0_vi[9]*0_vr[3] + 0_q[(6, 3, 9)] = 0.0\n",
      " -2.0384337416377893 0_vr[9]² - 2.0384337416377893 0_vi[9]² + 2.0384337416377893 0_vr[3]*0_vr[9] + 2.0384337416377893 0_vi[3]*0_vi[9] + 7.875766729055094 0_vi[3]*0_vr[9] - 7.875766729055094 0_vr[3]*0_vi[9] + 0_p[(6, 9, 3)] = 0.0\n",
      " -7.859666729055094 0_vr[9]² - 7.859666729055094 0_vi[9]² + 7.875766729055094 0_vr[3]*0_vr[9] + 7.875766729055094 0_vi[3]*0_vi[9] - 2.0384337416377893 0_vi[3]*0_vr[9] + 2.0384337416377893 0_vr[3]*0_vi[9] + 0_q[(6, 9, 3)] = 0.0\n",
      " -7.233748725873806 0_vr[15]² - 7.233748725873806 0_vi[15]² + 7.233748725873806 0_vr[15]*0_vr[16] + 7.233748725873806 0_vi[15]*0_vi[16] - 56.88356952618946 0_vi[15]*0_vr[16] + 56.88356952618946 0_vi[16]*0_vr[15] + 0_p[(24, 15, 16)] = 0.0\n",
      " -56.86536952618946 0_vr[15]² - 56.86536952618946 0_vi[15]² + 56.88356952618946 0_vr[15]*0_vr[16] + 56.88356952618946 0_vi[15]*0_vi[16] + 7.233748725873806 0_vi[15]*0_vr[16] - 7.233748725873806 0_vi[16]*0_vr[15] + 0_q[(24, 15, 16)] = 0.0\n",
      " -7.233748725873806 0_vr[16]² - 7.233748725873806 0_vi[16]² + 7.233748725873806 0_vr[15]*0_vr[16] + 7.233748725873806 0_vi[15]*0_vi[16] + 56.88356952618946 0_vi[15]*0_vr[16] - 56.88356952618946 0_vr[15]*0_vi[16] + 0_p[(24, 16, 15)] = 0.0\n",
      " -56.86536952618946 0_vr[16]² - 56.86536952618946 0_vi[16]² + 56.88356952618946 0_vr[15]*0_vr[16] + 56.88356952618946 0_vi[15]*0_vi[16] - 7.233748725873806 0_vi[15]*0_vr[16] + 7.233748725873806 0_vr[15]*0_vi[16] + 0_q[(24, 16, 15)] = 0.0\n",
      " -1.9149113798863084 0_vr[2]² - 1.9149113798863084 0_vi[2]² + 1.9149113798863084 0_vr[2]*0_vr[4] + 1.9149113798863084 0_vi[2]*0_vi[4] - 7.3969290192559525 0_vi[2]*0_vr[4] + 7.3969290192559525 0_vi[4]*0_vr[2] + 0_p[(4, 2, 4)] = 0.0\n",
      " -7.3797790192559525 0_vr[2]² - 7.3797790192559525 0_vi[2]² + 7.3969290192559525 0_vr[2]*0_vr[4] + 7.3969290192559525 0_vi[2]*0_vi[4] + 1.9149113798863084 0_vi[2]*0_vr[4] - 1.9149113798863084 0_vi[4]*0_vr[2] + 0_q[(4, 2, 4)] = 0.0\n",
      " -1.9149113798863084 0_vr[4]² - 1.9149113798863084 0_vi[4]² + 1.9149113798863084 0_vr[2]*0_vr[4] + 1.9149113798863084 0_vi[2]*0_vi[4] + 7.3969290192559525 0_vi[2]*0_vr[4] - 7.3969290192559525 0_vr[2]*0_vi[4] + 0_p[(4, 4, 2)] = 0.0\n",
      " -7.3797790192559525 0_vr[4]² - 7.3797790192559525 0_vi[4]² + 7.3969290192559525 0_vr[2]*0_vr[4] + 7.3969290192559525 0_vi[2]*0_vi[4] - 1.9149113798863084 0_vi[2]*0_vr[4] + 1.9149113798863084 0_vr[2]*0_vi[4] + 0_q[(4, 4, 2)] = 0.0\n",
      " -5.902192242833051 0_vr[20]² - 5.902192242833051 0_vi[20]² + 5.902192242833051 0_vr[20]*0_vr[23] + 5.902192242833051 0_vi[20]*0_vi[23] - 45.53119730185497 0_vi[20]*0_vr[23] + 45.53119730185497 0_vi[23]*0_vr[20] + 0_p[(37, 20, 23)] = 0.0\n",
      " -45.508447301854964 0_vr[20]² - 45.508447301854964 0_vi[20]² + 45.53119730185497 0_vr[20]*0_vr[23] + 45.53119730185497 0_vi[20]*0_vi[23] + 5.902192242833051 0_vi[20]*0_vr[23] - 5.902192242833051 0_vi[23]*0_vr[20] + 0_q[(37, 20, 23)] = 0.0\n",
      " -5.902192242833051 0_vr[23]² - 5.902192242833051 0_vi[23]² + 5.902192242833051 0_vr[20]*0_vr[23] + 5.902192242833051 0_vi[20]*0_vi[23] + 45.53119730185497 0_vi[20]*0_vr[23] - 45.53119730185497 0_vr[20]*0_vi[23] + 0_p[(37, 23, 20)] = 0.0\n",
      " -45.508447301854964 0_vr[23]² - 45.508447301854964 0_vi[23]² + 45.53119730185497 0_vr[20]*0_vr[23] + 45.53119730185497 0_vi[20]*0_vi[23] - 5.902192242833051 0_vi[20]*0_vr[23] + 5.902192242833051 0_vr[20]*0_vi[23] + 0_q[(37, 23, 20)] = 0.0\n",
      " -2.862586649053442 0_vr[1]² - 2.862586649053442 0_vi[1]² + 2.862586649053442 0_vr[1]*0_vr[5] + 2.862586649053442 0_vi[1]*0_vi[5] - 11.095806047936508 0_vi[1]*0_vr[5] + 11.095806047936508 0_vi[5]*0_vr[1] + 0_p[(3, 1, 5)] = 0.0\n",
      " -11.084356047936508 0_vr[1]² - 11.084356047936508 0_vi[1]² + 11.095806047936508 0_vr[1]*0_vr[5] + 11.095806047936508 0_vi[1]*0_vi[5] + 2.862586649053442 0_vi[1]*0_vr[5] - 2.862586649053442 0_vi[5]*0_vr[1] + 0_q[(3, 1, 5)] = 0.0\n",
      " -2.862586649053442 0_vr[5]² - 2.862586649053442 0_vi[5]² + 2.862586649053442 0_vr[1]*0_vr[5] + 2.862586649053442 0_vi[1]*0_vi[5] + 11.095806047936508 0_vi[1]*0_vr[5] - 11.095806047936508 0_vr[1]*0_vi[5] + 0_p[(3, 5, 1)] = 0.0\n",
      " -11.084356047936508 0_vr[5]² - 11.084356047936508 0_vi[5]² + 11.095806047936508 0_vr[1]*0_vr[5] + 11.095806047936508 0_vi[1]*0_vi[5] - 2.862586649053442 0_vi[1]*0_vr[5] + 2.862586649053442 0_vr[1]*0_vi[5] + 0_q[(3, 5, 1)] = 0.0\n",
      " -4.840839078773654 0_vr[16]² - 4.840839078773654 0_vi[16]² + 4.840839078773654 0_vr[16]*0_vr[17] + 4.840839078773654 0_vi[16]*0_vi[17] - 37.99325216370838 0_vi[16]*0_vr[17] + 37.99325216370838 0_vi[17]*0_vr[16] + 0_p[(28, 16, 17)] = 0.0\n",
      " -37.966002163708374 0_vr[16]² - 37.966002163708374 0_vi[16]² + 37.99325216370838 0_vr[16]*0_vr[17] + 37.99325216370838 0_vi[16]*0_vi[17] + 4.840839078773654 0_vi[16]*0_vr[17] - 4.840839078773654 0_vi[17]*0_vr[16] + 0_q[(28, 16, 17)] = 0.0\n",
      " -4.840839078773654 0_vr[17]² - 4.840839078773654 0_vi[17]² + 4.840839078773654 0_vr[16]*0_vr[17] + 4.840839078773654 0_vi[16]*0_vi[17] + 37.99325216370838 0_vi[16]*0_vr[17] - 37.99325216370838 0_vr[16]*0_vi[17] + 0_p[(28, 17, 16)] = 0.0\n",
      " -37.966002163708374 0_vr[17]² - 37.966002163708374 0_vi[17]² + 37.99325216370838 0_vr[16]*0_vr[17] + 37.99325216370838 0_vi[16]*0_vi[17] - 4.840839078773654 0_vi[16]*0_vr[17] + 4.840839078773654 0_vr[16]*0_vi[17] + 0_q[(28, 17, 16)] = 0.0\n",
      " -1.2635350150467444 0_vr[2]² - 1.2635350150467444 0_vi[2]² + 1.2635350150467444 0_vr[2]*0_vr[6] + 1.2635350150467444 0_vi[2]*0_vi[6] - 4.881262029959254 0_vi[2]*0_vr[6] + 4.881262029959254 0_vi[6]*0_vr[2] + 0_p[(5, 2, 6)] = 0.0\n",
      " -4.855262029959254 0_vr[2]² - 4.855262029959254 0_vi[2]² + 4.881262029959254 0_vr[2]*0_vr[6] + 4.881262029959254 0_vi[2]*0_vi[6] + 1.2635350150467444 0_vi[2]*0_vr[6] - 1.2635350150467444 0_vi[6]*0_vr[2] + 0_q[(5, 2, 6)] = 0.0\n",
      " -1.2635350150467444 0_vr[6]² - 1.2635350150467444 0_vi[6]² + 1.2635350150467444 0_vr[2]*0_vr[6] + 1.2635350150467444 0_vi[2]*0_vi[6] + 4.881262029959254 0_vi[2]*0_vr[6] - 4.881262029959254 0_vr[2]*0_vi[6] + 0_p[(5, 6, 2)] = 0.0\n",
      " -4.855262029959254 0_vr[6]² - 4.855262029959254 0_vi[6]² + 4.881262029959254 0_vr[2]*0_vr[6] + 4.881262029959254 0_vi[2]*0_vi[6] - 1.2635350150467444 0_vi[2]*0_vr[6] + 1.2635350150467444 0_vr[2]*0_vi[6] + 0_q[(5, 6, 2)] = 0.0\n",
      " -1.8619463117411768 0_vr[21]² - 1.8619463117411768 0_vi[21]² + 1.8619463117411768 0_vr[21]*0_vr[22] + 1.8619463117411768 0_vi[21]*0_vi[22] - 14.510340222534687 0_vi[21]*0_vr[22] + 14.510340222534687 0_vi[22]*0_vr[21] + 0_p[(38, 21, 22)] = 0.0\n",
      " -14.439140222534688 0_vr[21]² - 14.439140222534688 0_vi[21]² + 14.510340222534687 0_vr[21]*0_vr[22] + 14.510340222534687 0_vi[21]*0_vi[22] + 1.8619463117411768 0_vi[21]*0_vr[22] - 1.8619463117411768 0_vi[22]*0_vr[21] + 0_q[(38, 21, 22)] = 0.0\n",
      " -1.8619463117411768 0_vr[22]² - 1.8619463117411768 0_vi[22]² + 1.8619463117411768 0_vr[21]*0_vr[22] + 1.8619463117411768 0_vi[21]*0_vi[22] + 14.510340222534687 0_vi[21]*0_vr[22] - 14.510340222534687 0_vr[21]*0_vi[22] + 0_p[(38, 22, 21)] = 0.0\n",
      " -14.439140222534688 0_vr[22]² - 14.439140222534688 0_vi[22]² + 14.510340222534687 0_vr[21]*0_vr[22] + 14.510340222534687 0_vi[21]*0_vi[22] - 1.8619463117411768 0_vi[21]*0_vr[22] + 1.8619463117411768 0_vr[21]*0_vi[22] + 0_q[(38, 22, 21)] = 0.0\n",
      " -2.648753566047321 0_vr[12]² - 2.648753566047321 0_vi[12]² + 2.648753566047321 0_vr[12]*0_vr[13] + 2.648753566047321 0_vi[12]*0_vi[13] - 20.668962253090573 0_vi[12]*0_vr[13] + 20.668962253090573 0_vi[13]*0_vr[12] + 0_p[(20, 12, 13)] = 0.0\n",
      " -20.619012253090574 0_vr[12]² - 20.619012253090574 0_vi[12]² + 20.668962253090573 0_vr[12]*0_vr[13] + 20.668962253090573 0_vi[12]*0_vi[13] + 2.648753566047321 0_vi[12]*0_vr[13] - 2.648753566047321 0_vi[13]*0_vr[12] + 0_q[(20, 12, 13)] = 0.0\n",
      " -2.648753566047321 0_vr[13]² - 2.648753566047321 0_vi[13]² + 2.648753566047321 0_vr[12]*0_vr[13] + 2.648753566047321 0_vi[12]*0_vi[13] + 20.668962253090573 0_vi[12]*0_vr[13] - 20.668962253090573 0_vr[12]*0_vi[13] + 0_p[(20, 13, 12)] = 0.0\n",
      " -20.619012253090574 0_vr[13]² - 20.619012253090574 0_vi[13]² + 20.668962253090573 0_vr[12]*0_vr[13] + 20.668962253090573 0_vi[12]*0_vi[13] - 2.648753566047321 0_vi[12]*0_vr[13] + 2.648753566047321 0_vr[12]*0_vi[13] + 0_q[(20, 13, 12)] = 0.0\n",
      " -3.2505314618940204 0_vr[14]² - 3.2505314618940204 0_vi[14]² + 3.2505314618940204 0_vr[14]*0_vr[16] + 3.2505314618940204 0_vi[14]*0_vi[16] - 25.289134773535476 0_vi[14]*0_vr[16] + 25.289134773535476 0_vi[16]*0_vr[14] + 0_p[(23, 14, 16)] = 0.0\n",
      " -25.248234773535476 0_vr[14]² - 25.248234773535476 0_vi[14]² + 25.289134773535476 0_vr[14]*0_vr[16] + 25.289134773535476 0_vi[14]*0_vi[16] + 3.2505314618940204 0_vi[14]*0_vr[16] - 3.2505314618940204 0_vi[16]*0_vr[14] + 0_q[(23, 14, 16)] = 0.0\n",
      " -3.2505314618940204 0_vr[16]² - 3.2505314618940204 0_vi[16]² + 3.2505314618940204 0_vr[14]*0_vr[16] + 3.2505314618940204 0_vi[14]*0_vi[16] + 25.289134773535476 0_vi[14]*0_vr[16] - 25.289134773535476 0_vr[14]*0_vi[16] + 0_p[(23, 16, 14)] = 0.0\n",
      " -25.248234773535476 0_vr[16]² - 25.248234773535476 0_vi[16]² + 25.289134773535476 0_vr[14]*0_vr[16] + 25.289134773535476 0_vi[14]*0_vi[16] - 3.2505314618940204 0_vi[14]*0_vr[16] + 3.2505314618940204 0_vr[14]*0_vi[16] + 0_q[(23, 16, 14)] = 0.0\n",
      " -1.4682974970169838 0_vr[8]² - 1.4682974970169838 0_vi[8]² + 1.4682974970169838 0_vr[8]*0_vr[10] + 1.4682974970169838 0_vi[8]*0_vi[10] - 5.677187746077377 0_vi[8]*0_vr[10] + 5.677187746077377 0_vi[10]*0_vr[8] + 0_p[(13, 8, 10)] = 0.0\n",
      " -5.654837746077376 0_vr[8]² - 5.654837746077376 0_vi[8]² + 5.677187746077377 0_vr[8]*0_vr[10] + 5.677187746077377 0_vi[8]*0_vi[10] + 1.4682974970169838 0_vi[8]*0_vr[10] - 1.4682974970169838 0_vi[10]*0_vr[8] + 0_q[(13, 8, 10)] = 0.0\n",
      " -1.4682974970169838 0_vr[10]² - 1.4682974970169838 0_vi[10]² + 1.4682974970169838 0_vr[8]*0_vr[10] + 1.4682974970169838 0_vi[8]*0_vi[10] + 5.677187746077377 0_vi[8]*0_vr[10] - 5.677187746077377 0_vr[8]*0_vi[10] + 0_p[(13, 10, 8)] = 0.0\n",
      " -5.654837746077376 0_vr[10]² - 5.654837746077376 0_vi[10]² + 5.677187746077377 0_vr[8]*0_vr[10] + 5.677187746077377 0_vi[8]*0_vi[10] - 1.4682974970169838 0_vi[8]*0_vr[10] + 1.4682974970169838 0_vr[8]*0_vi[10] + 0_q[(13, 10, 8)] = 0.0\n",
      " -1.197834315557472 0_vr[17]² - 1.197834315557472 0_vi[17]² + 1.197834315557472 0_vr[17]*0_vr[22] + 1.197834315557472 0_vi[17]*0_vi[22] - 9.343107661348283 0_vi[17]*0_vr[22] + 9.343107661348283 0_vi[22]*0_vr[17] + 0_p[(31, 17, 22)] = 0.0\n",
      " -9.232507661348283 0_vr[17]² - 9.232507661348283 0_vi[17]² + 9.343107661348283 0_vr[17]*0_vr[22] + 9.343107661348283 0_vi[17]*0_vi[22] + 1.197834315557472 0_vi[17]*0_vr[22] - 1.197834315557472 0_vi[22]*0_vr[17] + 0_q[(31, 17, 22)] = 0.0\n",
      " -1.197834315557472 0_vr[22]² - 1.197834315557472 0_vi[22]² + 1.197834315557472 0_vr[17]*0_vr[22] + 1.197834315557472 0_vi[17]*0_vi[22] + 9.343107661348283 0_vi[17]*0_vr[22] - 9.343107661348283 0_vr[17]*0_vi[22] + 0_p[(31, 22, 17)] = 0.0\n",
      " -9.232507661348283 0_vr[22]² - 9.232507661348283 0_vi[22]² + 9.343107661348283 0_vr[17]*0_vr[22] + 9.343107661348283 0_vi[17]*0_vi[22] - 1.197834315557472 0_vi[17]*0_vr[22] + 1.197834315557472 0_vr[17]*0_vi[22] + 0_q[(31, 22, 17)] = 0.0\n",
      " -0.3077536505085719 0_vr[9]² - 0.3077536505085719 0_vi[9]² + 0.31698626002382907 0_vr[9]*0_vr[11] + 0.31698626002382907 0_vi[9]*0_vi[11] - 11.56310748521707 0_vi[9]*0_vr[11] + 11.56310748521707 0_vi[11]*0_vr[9] + 0_p[(14, 9, 11)] = 0.0\n",
      " -11.226317946812689 0_vr[9]² - 11.226317946812689 0_vi[9]² + 11.56310748521707 0_vr[9]*0_vr[11] + 11.56310748521707 0_vi[9]*0_vi[11] + 0.31698626002382907 0_vi[9]*0_vr[11] - 0.31698626002382907 0_vi[11]*0_vr[9] + 0_q[(14, 9, 11)] = 0.0\n",
      " -0.32649584782454394 0_vr[11]² - 0.32649584782454394 0_vi[11]² + 0.31698626002382907 0_vr[9]*0_vr[11] + 0.31698626002382907 0_vi[9]*0_vi[11] + 11.56310748521707 0_vi[9]*0_vr[11] - 11.56310748521707 0_vr[9]*0_vi[11] + 0_p[(14, 11, 9)] = 0.0\n",
      " -11.910000709773582 0_vr[11]² - 11.910000709773582 0_vi[11]² + 11.56310748521707 0_vr[9]*0_vr[11] + 11.56310748521707 0_vi[9]*0_vi[11] - 0.31698626002382907 0_vi[9]*0_vr[11] + 0.31698626002382907 0_vr[9]*0_vi[11] + 0_q[(14, 11, 9)] = 0.0\n",
      " -2.446594851195911 0_vr[15]² - 2.446594851195911 0_vi[15]² + 2.446594851195911 0_vr[15]*0_vr[24] + 2.446594851195911 0_vi[15]*0_vi[24] - 18.95198101150265 0_vi[15]*0_vr[24] + 18.95198101150265 0_vi[24]*0_vr[15] + 0_p[(27, 15, 24)] = 0.0\n",
      " -18.89743101150265 0_vr[15]² - 18.89743101150265 0_vi[15]² + 18.95198101150265 0_vr[15]*0_vr[24] + 18.95198101150265 0_vi[15]*0_vi[24] + 2.446594851195911 0_vi[15]*0_vr[24] - 2.446594851195911 0_vi[24]*0_vr[15] + 0_q[(27, 15, 24)] = 0.0\n",
      " -2.446594851195911 0_vr[24]² - 2.446594851195911 0_vi[24]² + 2.446594851195911 0_vr[15]*0_vr[24] + 2.446594851195911 0_vi[15]*0_vi[24] + 18.95198101150265 0_vi[15]*0_vr[24] - 18.95198101150265 0_vr[15]*0_vi[24] + 0_p[(27, 24, 15)] = 0.0\n",
      " -18.89743101150265 0_vr[24]² - 18.89743101150265 0_vi[24]² + 18.95198101150265 0_vr[15]*0_vr[24] + 18.95198101150265 0_vi[15]*0_vi[24] - 2.446594851195911 0_vi[15]*0_vr[24] + 2.446594851195911 0_vr[15]*0_vi[24] + 0_q[(27, 24, 15)] = 0.0\n",
      " -5.902192242833051 0_vr[20]² - 5.902192242833051 0_vi[20]² + 5.902192242833051 0_vr[20]*0_vr[23] + 5.902192242833051 0_vi[20]*0_vi[23] - 45.53119730185497 0_vi[20]*0_vr[23] + 45.53119730185497 0_vi[23]*0_vr[20] + 0_p[(36, 20, 23)] = 0.0\n",
      " -45.508447301854964 0_vr[20]² - 45.508447301854964 0_vi[20]² + 45.53119730185497 0_vr[20]*0_vr[23] + 45.53119730185497 0_vi[20]*0_vi[23] + 5.902192242833051 0_vi[20]*0_vr[23] - 5.902192242833051 0_vi[23]*0_vr[20] + 0_q[(36, 20, 23)] = 0.0\n",
      " -5.902192242833051 0_vr[23]² - 5.902192242833051 0_vi[23]² + 5.902192242833051 0_vr[20]*0_vr[23] + 5.902192242833051 0_vi[20]*0_vi[23] + 45.53119730185497 0_vi[20]*0_vr[23] - 45.53119730185497 0_vr[20]*0_vi[23] + 0_p[(36, 23, 20)] = 0.0\n",
      " -45.508447301854964 0_vr[23]² - 45.508447301854964 0_vi[23]² + 45.53119730185497 0_vr[20]*0_vr[23] + 45.53119730185497 0_vi[20]*0_vi[23] - 5.902192242833051 0_vi[20]*0_vr[23] + 5.902192242833051 0_vr[20]*0_vi[23] + 0_q[(36, 23, 20)] = 0.0\n",
      " -0.3077536505085719 0_vr[9]² - 0.3077536505085719 0_vi[9]² + 0.31698626002382907 0_vr[9]*0_vr[12] + 0.31698626002382907 0_vi[9]*0_vi[12] - 11.56310748521707 0_vi[9]*0_vr[12] + 11.56310748521707 0_vi[12]*0_vr[9] + 0_p[(15, 9, 12)] = 0.0\n",
      " -11.226317946812689 0_vr[9]² - 11.226317946812689 0_vi[9]² + 11.56310748521707 0_vr[9]*0_vr[12] + 11.56310748521707 0_vi[9]*0_vi[12] + 0.31698626002382907 0_vi[9]*0_vr[12] - 0.31698626002382907 0_vi[12]*0_vr[9] + 0_q[(15, 9, 12)] = 0.0\n",
      " -0.32649584782454394 0_vr[12]² - 0.32649584782454394 0_vi[12]² + 0.31698626002382907 0_vr[9]*0_vr[12] + 0.31698626002382907 0_vi[9]*0_vi[12] + 11.56310748521707 0_vi[9]*0_vr[12] - 11.56310748521707 0_vr[9]*0_vi[12] + 0_p[(15, 12, 9)] = 0.0\n",
      " -11.910000709773582 0_vr[12]² - 11.910000709773582 0_vi[12]² + 11.56310748521707 0_vr[9]*0_vr[12] + 11.56310748521707 0_vi[9]*0_vi[12] - 0.31698626002382907 0_vi[9]*0_vr[12] + 0.31698626002382907 0_vr[9]*0_vi[12] + 0_q[(15, 12, 9)] = 0.0\n",
      " -4.840839078773654 0_vr[18]² - 4.840839078773654 0_vi[18]² + 4.840839078773654 0_vr[18]*0_vr[21] + 4.840839078773654 0_vi[18]*0_vi[21] - 37.99325216370838 0_vi[18]*0_vr[21] + 37.99325216370838 0_vi[21]*0_vr[18] + 0_p[(33, 18, 21)] = 0.0\n",
      " -37.966002163708374 0_vr[18]² - 37.966002163708374 0_vi[18]² + 37.99325216370838 0_vr[18]*0_vr[21] + 37.99325216370838 0_vi[18]*0_vi[21] + 4.840839078773654 0_vi[18]*0_vr[21] - 4.840839078773654 0_vi[21]*0_vr[18] + 0_q[(33, 18, 21)] = 0.0\n",
      " -4.840839078773654 0_vr[21]² - 4.840839078773654 0_vi[21]² + 4.840839078773654 0_vr[18]*0_vr[21] + 4.840839078773654 0_vi[18]*0_vi[21] + 37.99325216370838 0_vi[18]*0_vr[21] - 37.99325216370838 0_vr[18]*0_vi[21] + 0_p[(33, 21, 18)] = 0.0\n",
      " -37.966002163708374 0_vr[21]² - 37.966002163708374 0_vi[21]² + 37.99325216370838 0_vr[18]*0_vr[21] + 37.99325216370838 0_vi[18]*0_vi[21] - 4.840839078773654 0_vi[18]*0_vr[21] + 4.840839078773654 0_vr[18]*0_vi[21] + 0_q[(33, 21, 18)] = 0.0\n",
      " -8.547008547008547 0_vr[17]² - 8.547008547008547 0_vi[17]² + 8.547008547008547 0_vr[17]*0_vr[18] + 8.547008547008547 0_vi[17]*0_vi[18] - 68.37606837606837 0_vi[17]*0_vr[18] + 68.37606837606837 0_vi[18]*0_vr[17] + 0_p[(30, 17, 18)] = 0.0\n",
      " -68.36091837606837 0_vr[17]² - 68.36091837606837 0_vi[17]² + 68.37606837606837 0_vr[17]*0_vr[18] + 68.37606837606837 0_vi[17]*0_vi[18] + 8.547008547008547 0_vi[17]*0_vr[18] - 8.547008547008547 0_vi[18]*0_vr[17] + 0_q[(30, 17, 18)] = 0.0\n",
      " -8.547008547008547 0_vr[18]² - 8.547008547008547 0_vi[18]² + 8.547008547008547 0_vr[17]*0_vr[18] + 8.547008547008547 0_vi[17]*0_vi[18] + 68.37606837606837 0_vi[17]*0_vr[18] - 68.37606837606837 0_vr[17]*0_vi[18] + 0_p[(30, 18, 17)] = 0.0\n",
      " -68.36091837606837 0_vr[18]² - 68.36091837606837 0_vi[18]² + 68.37606837606837 0_vr[17]*0_vr[18] + 68.37606837606837 0_vi[17]*0_vi[18] - 8.547008547008547 0_vi[17]*0_vr[18] + 8.547008547008547 0_vr[17]*0_vi[18] + 0_q[(30, 18, 17)] = 0.0\n",
      " -0_vr[1]² - 0_vi[1]² ≥ -1.1025\n",
      " -0_vr[2]² - 0_vi[2]² ≥ -1.1025\n",
      " -0_vr[16]² - 0_vi[16]² ≥ -1.1025\n",
      " -0_vr[11]² - 0_vi[11]² ≥ -1.1025\n",
      " -0_vr[21]² - 0_vi[21]² ≥ -1.1025\n",
      " -0_vr[7]² - 0_vi[7]² ≥ -1.1025\n",
      " -0_vr[9]² - 0_vi[9]² ≥ -1.1025\n",
      " -0_vr[10]² - 0_vi[10]² ≥ -1.1025\n",
      " -0_vr[19]² - 0_vi[19]² ≥ -1.1025\n",
      " -0_vr[17]² - 0_vi[17]² ≥ -1.1025\n",
      " -0_vr[8]² - 0_vi[8]² ≥ -1.1025\n",
      " -0_vr[22]² - 0_vi[22]² ≥ -1.1025\n",
      " -0_vr[6]² - 0_vi[6]² ≥ -1.1025\n",
      " -0_vr[24]² - 0_vi[24]² ≥ -1.1025\n",
      " -0_vr[4]² - 0_vi[4]² ≥ -1.1025\n",
      " -0_vr[3]² - 0_vi[3]² ≥ -1.1025\n",
      " -0_vr[5]² - 0_vi[5]² ≥ -1.1025\n",
      " -0_vr[20]² - 0_vi[20]² ≥ -1.1025\n",
      " -0_vr[23]² - 0_vi[23]² ≥ -1.1025\n",
      " -0_vr[13]² - 0_vi[13]² ≥ -1.1025\n",
      " -0_vr[14]² - 0_vi[14]² ≥ -1.1025\n",
      " -0_vr[15]² - 0_vi[15]² ≥ -1.1025\n",
      " -0_vr[12]² - 0_vi[12]² ≥ -1.1025\n",
      " -0_vr[18]² - 0_vi[18]² ≥ -1.1025\n",
      " 0_vi[11]*0_vr[13] - 0_vi[13]*0_vr[11] + 0.5773502691896257 0_vr[11]*0_vr[13] + 0.5773502691896257 0_vi[11]*0_vi[13] ≥ 0.0\n",
      " 0_vi[8]*0_vr[9] - 0_vi[9]*0_vr[8] + 0.5773502691896257 0_vr[8]*0_vr[9] + 0.5773502691896257 0_vi[8]*0_vi[9] ≥ 0.0\n",
      " 0_vi[1]*0_vr[2] - 0_vi[2]*0_vr[1] + 0.5773502691896257 0_vr[1]*0_vr[2] + 0.5773502691896257 0_vi[1]*0_vi[2] ≥ 0.0\n",
      " 0_vi[18]*0_vr[21] - 0_vi[21]*0_vr[18] + 0.5773502691896257 0_vr[18]*0_vr[21] + 0.5773502691896257 0_vi[18]*0_vi[21] ≥ 0.0\n",
      " 0_vi[1]*0_vr[3] - 0_vi[3]*0_vr[1] + 0.5773502691896257 0_vr[1]*0_vr[3] + 0.5773502691896257 0_vi[1]*0_vi[3] ≥ 0.0\n",
      " 0_vi[10]*0_vr[11] - 0_vi[11]*0_vr[10] + 0.5773502691896257 0_vr[10]*0_vr[11] + 0.5773502691896257 0_vi[10]*0_vi[11] ≥ 0.0\n",
      " 0_vi[7]*0_vr[8] - 0_vi[8]*0_vr[7] + 0.5773502691896257 0_vr[7]*0_vr[8] + 0.5773502691896257 0_vi[7]*0_vi[8] ≥ 0.0\n",
      " 0_vi[12]*0_vr[23] - 0_vi[23]*0_vr[12] + 0.5773502691896257 0_vr[12]*0_vr[23] + 0.5773502691896257 0_vi[12]*0_vi[23] ≥ 0.0\n",
      " 0_vi[3]*0_vr[24] - 0_vi[24]*0_vr[3] + 0.5773502691896257 0_vr[3]*0_vr[24] + 0.5773502691896257 0_vi[3]*0_vi[24] ≥ 0.0\n",
      " 0_vi[15]*0_vr[21] - 0_vi[21]*0_vr[15] + 0.5773502691896257 0_vr[15]*0_vr[21] + 0.5773502691896257 0_vi[15]*0_vi[21] ≥ 0.0\n",
      " 0_vi[19]*0_vr[20] - 0_vi[20]*0_vr[19] + 0.5773502691896257 0_vr[19]*0_vr[20] + 0.5773502691896257 0_vi[19]*0_vi[20] ≥ 0.0\n",
      " 0_vi[16]*0_vr[19] - 0_vi[19]*0_vr[16] + 0.5773502691896257 0_vr[16]*0_vr[19] + 0.5773502691896257 0_vi[16]*0_vi[19] ≥ 0.0\n",
      " 0_vi[6]*0_vr[10] - 0_vi[10]*0_vr[6] + 0.5773502691896257 0_vr[6]*0_vr[10] + 0.5773502691896257 0_vi[6]*0_vi[10] ≥ 0.0\n",
      " 0_vi[5]*0_vr[10] - 0_vi[10]*0_vr[5] + 0.5773502691896257 0_vr[5]*0_vr[10] + 0.5773502691896257 0_vi[5]*0_vi[10] ≥ 0.0\n",
      " 0_vi[11]*0_vr[14] - 0_vi[14]*0_vr[11] + 0.5773502691896257 0_vr[11]*0_vr[14] + 0.5773502691896257 0_vi[11]*0_vi[14] ≥ 0.0\n",
      " 0_vi[10]*0_vr[12] - 0_vi[12]*0_vr[10] + 0.5773502691896257 0_vr[10]*0_vr[12] + 0.5773502691896257 0_vi[10]*0_vi[12] ≥ 0.0\n",
      " 0_vi[4]*0_vr[9] - 0_vi[9]*0_vr[4] + 0.5773502691896257 0_vr[4]*0_vr[9] + 0.5773502691896257 0_vi[4]*0_vi[9] ≥ 0.0\n",
      " 0_vi[13]*0_vr[23] - 0_vi[23]*0_vr[13] + 0.5773502691896257 0_vr[13]*0_vr[23] + 0.5773502691896257 0_vi[13]*0_vi[23] ≥ 0.0\n",
      " 0_vi[3]*0_vr[9] - 0_vi[9]*0_vr[3] + 0.5773502691896257 0_vr[3]*0_vr[9] + 0.5773502691896257 0_vi[3]*0_vi[9] ≥ 0.0\n",
      " 0_vi[15]*0_vr[16] - 0_vi[16]*0_vr[15] + 0.5773502691896257 0_vr[15]*0_vr[16] + 0.5773502691896257 0_vi[15]*0_vi[16] ≥ 0.0\n",
      " 0_vi[2]*0_vr[4] - 0_vi[4]*0_vr[2] + 0.5773502691896257 0_vr[2]*0_vr[4] + 0.5773502691896257 0_vi[2]*0_vi[4] ≥ 0.0\n",
      " 0_vi[1]*0_vr[5] - 0_vi[5]*0_vr[1] + 0.5773502691896257 0_vr[1]*0_vr[5] + 0.5773502691896257 0_vi[1]*0_vi[5] ≥ 0.0\n",
      " 0_vi[16]*0_vr[17] - 0_vi[17]*0_vr[16] + 0.5773502691896257 0_vr[16]*0_vr[17] + 0.5773502691896257 0_vi[16]*0_vi[17] ≥ 0.0\n",
      " 0_vi[2]*0_vr[6] - 0_vi[6]*0_vr[2] + 0.5773502691896257 0_vr[2]*0_vr[6] + 0.5773502691896257 0_vi[2]*0_vi[6] ≥ 0.0\n",
      " 0_vi[21]*0_vr[22] - 0_vi[22]*0_vr[21] + 0.5773502691896257 0_vr[21]*0_vr[22] + 0.5773502691896257 0_vi[21]*0_vi[22] ≥ 0.0\n",
      " 0_vi[12]*0_vr[13] - 0_vi[13]*0_vr[12] + 0.5773502691896257 0_vr[12]*0_vr[13] + 0.5773502691896257 0_vi[12]*0_vi[13] ≥ 0.0\n",
      " 0_vi[14]*0_vr[16] - 0_vi[16]*0_vr[14] + 0.5773502691896257 0_vr[14]*0_vr[16] + 0.5773502691896257 0_vi[14]*0_vi[16] ≥ 0.0\n",
      " 0_vi[8]*0_vr[10] - 0_vi[10]*0_vr[8] + 0.5773502691896257 0_vr[8]*0_vr[10] + 0.5773502691896257 0_vi[8]*0_vi[10] ≥ 0.0\n",
      " 0_vi[17]*0_vr[22] - 0_vi[22]*0_vr[17] + 0.5773502691896257 0_vr[17]*0_vr[22] + 0.5773502691896257 0_vi[17]*0_vi[22] ≥ 0.0\n",
      " 0_vi[9]*0_vr[11] - 0_vi[11]*0_vr[9] + 0.5773502691896257 0_vr[9]*0_vr[11] + 0.5773502691896257 0_vi[9]*0_vi[11] ≥ 0.0\n",
      " 0_vi[15]*0_vr[24] - 0_vi[24]*0_vr[15] + 0.5773502691896257 0_vr[15]*0_vr[24] + 0.5773502691896257 0_vi[15]*0_vi[24] ≥ 0.0\n",
      " 0_vi[20]*0_vr[23] - 0_vi[23]*0_vr[20] + 0.5773502691896257 0_vr[20]*0_vr[23] + 0.5773502691896257 0_vi[20]*0_vi[23] ≥ 0.0\n",
      " 0_vi[9]*0_vr[12] - 0_vi[12]*0_vr[9] + 0.5773502691896257 0_vr[9]*0_vr[12] + 0.5773502691896257 0_vi[9]*0_vi[12] ≥ 0.0\n",
      " 0_vi[17]*0_vr[18] - 0_vi[18]*0_vr[17] + 0.5773502691896257 0_vr[17]*0_vr[18] + 0.5773502691896257 0_vi[17]*0_vi[18] ≥ 0.0\n",
      " -0_vr[1]² - 0_vi[1]² ≤ -0.9025\n",
      " -0_vr[2]² - 0_vi[2]² ≤ -0.9025\n",
      " -0_vr[16]² - 0_vi[16]² ≤ -0.9025\n",
      " -0_vr[11]² - 0_vi[11]² ≤ -0.9025\n",
      " -0_vr[21]² - 0_vi[21]² ≤ -0.9025\n",
      " -0_vr[7]² - 0_vi[7]² ≤ -0.9025\n",
      " -0_vr[9]² - 0_vi[9]² ≤ -0.9025\n",
      " -0_vr[10]² - 0_vi[10]² ≤ -0.9025\n",
      " -0_vr[19]² - 0_vi[19]² ≤ -0.9025\n",
      " -0_vr[17]² - 0_vi[17]² ≤ -0.9025\n",
      " -0_vr[8]² - 0_vi[8]² ≤ -0.9025\n",
      " -0_vr[22]² - 0_vi[22]² ≤ -0.9025\n",
      " -0_vr[6]² - 0_vi[6]² ≤ -0.9025\n",
      " -0_vr[24]² - 0_vi[24]² ≤ -0.9025\n",
      " -0_vr[4]² - 0_vi[4]² ≤ -0.9025\n",
      " -0_vr[3]² - 0_vi[3]² ≤ -0.9025\n",
      " -0_vr[5]² - 0_vi[5]² ≤ -0.9025\n",
      " -0_vr[20]² - 0_vi[20]² ≤ -0.9025\n",
      " -0_vr[23]² - 0_vi[23]² ≤ -0.9025\n",
      " -0_vr[13]² - 0_vi[13]² ≤ -0.9025\n",
      " -0_vr[14]² - 0_vi[14]² ≤ -0.9025\n",
      " -0_vr[15]² - 0_vi[15]² ≤ -0.9025\n",
      " -0_vr[12]² - 0_vi[12]² ≤ -0.9025\n",
      " -0_vr[18]² - 0_vi[18]² ≤ -0.9025\n",
      " 0_vi[11]*0_vr[13] - 0_vi[13]*0_vr[11] - 0.5773502691896257 0_vr[11]*0_vr[13] - 0.5773502691896257 0_vi[11]*0_vi[13] ≤ 0.0\n",
      " 0_p[(18, 11, 13)]² + 0_q[(18, 11, 13)]² ≤ 25.0\n",
      " 0_p[(18, 13, 11)]² + 0_q[(18, 13, 11)]² ≤ 25.0\n",
      " 0_vi[8]*0_vr[9] - 0_vi[9]*0_vr[8] - 0.5773502691896257 0_vr[8]*0_vr[9] - 0.5773502691896257 0_vi[8]*0_vi[9] ≤ 0.0\n",
      " 0_p[(12, 8, 9)]² + 0_q[(12, 8, 9)]² ≤ 3.0625\n",
      " 0_p[(12, 9, 8)]² + 0_q[(12, 9, 8)]² ≤ 3.0625\n",
      " 0_vi[1]*0_vr[2] - 0_vi[2]*0_vr[1] - 0.5773502691896257 0_vr[1]*0_vr[2] - 0.5773502691896257 0_vi[1]*0_vi[2] ≤ 0.0\n",
      " 0_p[(1, 1, 2)]² + 0_q[(1, 1, 2)]² ≤ 3.0625\n",
      " 0_p[(1, 2, 1)]² + 0_q[(1, 2, 1)]² ≤ 3.0625\n",
      " 0_vi[18]*0_vr[21] - 0_vi[21]*0_vr[18] - 0.5773502691896257 0_vr[18]*0_vr[21] - 0.5773502691896257 0_vi[18]*0_vi[21] ≤ 0.0\n",
      " 0_p[(32, 18, 21)]² + 0_q[(32, 18, 21)]² ≤ 25.0\n",
      " 0_p[(32, 21, 18)]² + 0_q[(32, 21, 18)]² ≤ 25.0\n",
      " 0_vi[1]*0_vr[3] - 0_vi[3]*0_vr[1] - 0.5773502691896257 0_vr[1]*0_vr[3] - 0.5773502691896257 0_vi[1]*0_vi[3] ≤ 0.0\n",
      " 0_p[(2, 1, 3)]² + 0_q[(2, 1, 3)]² ≤ 3.0625\n",
      " 0_p[(2, 3, 1)]² + 0_q[(2, 3, 1)]² ≤ 3.0625\n",
      " 0_vi[10]*0_vr[11] - 0_vi[11]*0_vr[10] - 0.5773502691896257 0_vr[10]*0_vr[11] - 0.5773502691896257 0_vi[10]*0_vi[11] ≤ 0.0\n",
      " 0_p[(16, 10, 11)]² + 0_q[(16, 10, 11)]² ≤ 16.0\n",
      " 0_p[(16, 11, 10)]² + 0_q[(16, 11, 10)]² ≤ 16.0\n",
      " 0_vi[7]*0_vr[8] - 0_vi[8]*0_vr[7] - 0.5773502691896257 0_vr[7]*0_vr[8] - 0.5773502691896257 0_vi[7]*0_vi[8] ≤ 0.0\n",
      " 0_p[(11, 7, 8)]² + 0_q[(11, 7, 8)]² ≤ 3.0625\n",
      " 0_p[(11, 8, 7)]² + 0_q[(11, 8, 7)]² ≤ 3.0625\n",
      " 0_vi[12]*0_vr[23] - 0_vi[23]*0_vr[12] - 0.5773502691896257 0_vr[12]*0_vr[23] - 0.5773502691896257 0_vi[12]*0_vi[23] ≤ 0.0\n",
      " 0_p[(21, 12, 23)]² + 0_q[(21, 12, 23)]² ≤ 25.0\n",
      " 0_p[(21, 23, 12)]² + 0_q[(21, 23, 12)]² ≤ 25.0\n",
      " 0_vi[3]*0_vr[24] - 0_vi[24]*0_vr[3] - 0.5773502691896257 0_vr[3]*0_vr[24] - 0.5773502691896257 0_vi[3]*0_vi[24] ≤ 0.0\n",
      " 0_p[(7, 3, 24)]² + 0_q[(7, 3, 24)]² ≤ 16.0\n",
      " 0_p[(7, 24, 3)]² + 0_q[(7, 24, 3)]² ≤ 16.0\n",
      " 0_p[(26, 15, 21)]² + 0_q[(26, 15, 21)]² ≤ 25.0\n",
      " 0_p[(26, 21, 15)]² + 0_q[(26, 21, 15)]² ≤ 25.0\n",
      " 0_vi[15]*0_vr[21] - 0_vi[21]*0_vr[15] - 0.5773502691896257 0_vr[15]*0_vr[21] - 0.5773502691896257 0_vi[15]*0_vi[21] ≤ 0.0\n",
      " 0_p[(25, 15, 21)]² + 0_q[(25, 15, 21)]² ≤ 25.0\n",
      " 0_p[(25, 21, 15)]² + 0_q[(25, 21, 15)]² ≤ 25.0\n",
      " 0_p[(35, 19, 20)]² + 0_q[(35, 19, 20)]² ≤ 25.0\n",
      " 0_p[(35, 20, 19)]² + 0_q[(35, 20, 19)]² ≤ 25.0\n",
      " 0_vi[19]*0_vr[20] - 0_vi[20]*0_vr[19] - 0.5773502691896257 0_vr[19]*0_vr[20] - 0.5773502691896257 0_vi[19]*0_vi[20] ≤ 0.0\n",
      " 0_p[(34, 19, 20)]² + 0_q[(34, 19, 20)]² ≤ 25.0\n",
      " 0_p[(34, 20, 19)]² + 0_q[(34, 20, 19)]² ≤ 25.0\n",
      " 0_vi[16]*0_vr[19] - 0_vi[19]*0_vr[16] - 0.5773502691896257 0_vr[16]*0_vr[19] - 0.5773502691896257 0_vi[16]*0_vi[19] ≤ 0.0\n",
      " 0_p[(29, 16, 19)]² + 0_q[(29, 16, 19)]² ≤ 25.0\n",
      " 0_p[(29, 19, 16)]² + 0_q[(29, 19, 16)]² ≤ 25.0\n",
      " 0_vi[6]*0_vr[10] - 0_vi[10]*0_vr[6] - 0.5773502691896257 0_vr[6]*0_vr[10] - 0.5773502691896257 0_vi[6]*0_vi[10] ≤ 0.0\n",
      " 0_p[(10, 6, 10)]² + 0_q[(10, 6, 10)]² ≤ 3.0625\n",
      " 0_p[(10, 10, 6)]² + 0_q[(10, 10, 6)]² ≤ 3.0625\n",
      " 0_vi[5]*0_vr[10] - 0_vi[10]*0_vr[5] - 0.5773502691896257 0_vr[5]*0_vr[10] - 0.5773502691896257 0_vi[5]*0_vi[10] ≤ 0.0\n",
      " 0_p[(9, 5, 10)]² + 0_q[(9, 5, 10)]² ≤ 3.0625\n",
      " 0_p[(9, 10, 5)]² + 0_q[(9, 10, 5)]² ≤ 3.0625\n",
      " 0_vi[11]*0_vr[14] - 0_vi[14]*0_vr[11] - 0.5773502691896257 0_vr[11]*0_vr[14] - 0.5773502691896257 0_vi[11]*0_vi[14] ≤ 0.0\n",
      " 0_p[(19, 11, 14)]² + 0_q[(19, 11, 14)]² ≤ 25.0\n",
      " 0_p[(19, 14, 11)]² + 0_q[(19, 14, 11)]² ≤ 25.0\n",
      " 0_vi[10]*0_vr[12] - 0_vi[12]*0_vr[10] - 0.5773502691896257 0_vr[10]*0_vr[12] - 0.5773502691896257 0_vi[10]*0_vi[12] ≤ 0.0\n",
      " 0_p[(17, 10, 12)]² + 0_q[(17, 10, 12)]² ≤ 16.0\n",
      " 0_p[(17, 12, 10)]² + 0_q[(17, 12, 10)]² ≤ 16.0\n",
      " 0_vi[4]*0_vr[9] - 0_vi[9]*0_vr[4] - 0.5773502691896257 0_vr[4]*0_vr[9] - 0.5773502691896257 0_vi[4]*0_vi[9] ≤ 0.0\n",
      " 0_p[(8, 4, 9)]² + 0_q[(8, 4, 9)]² ≤ 3.0625\n",
      " 0_p[(8, 9, 4)]² + 0_q[(8, 9, 4)]² ≤ 3.0625\n",
      " 0_vi[13]*0_vr[23] - 0_vi[23]*0_vr[13] - 0.5773502691896257 0_vr[13]*0_vr[23] - 0.5773502691896257 0_vi[13]*0_vi[23] ≤ 0.0\n",
      " 0_p[(22, 13, 23)]² + 0_q[(22, 13, 23)]² ≤ 25.0\n",
      " 0_p[(22, 23, 13)]² + 0_q[(22, 23, 13)]² ≤ 25.0\n",
      " 0_vi[3]*0_vr[9] - 0_vi[9]*0_vr[3] - 0.5773502691896257 0_vr[3]*0_vr[9] - 0.5773502691896257 0_vi[3]*0_vi[9] ≤ 0.0\n",
      " 0_p[(6, 3, 9)]² + 0_q[(6, 3, 9)]² ≤ 3.0625\n",
      " 0_p[(6, 9, 3)]² + 0_q[(6, 9, 3)]² ≤ 3.0625\n",
      " 0_vi[15]*0_vr[16] - 0_vi[16]*0_vr[15] - 0.5773502691896257 0_vr[15]*0_vr[16] - 0.5773502691896257 0_vi[15]*0_vi[16] ≤ 0.0\n",
      " 0_p[(24, 15, 16)]² + 0_q[(24, 15, 16)]² ≤ 25.0\n",
      " 0_p[(24, 16, 15)]² + 0_q[(24, 16, 15)]² ≤ 25.0\n",
      " 0_vi[2]*0_vr[4] - 0_vi[4]*0_vr[2] - 0.5773502691896257 0_vr[2]*0_vr[4] - 0.5773502691896257 0_vi[2]*0_vi[4] ≤ 0.0\n",
      " 0_p[(4, 2, 4)]² + 0_q[(4, 2, 4)]² ≤ 3.0625\n",
      " 0_p[(4, 4, 2)]² + 0_q[(4, 4, 2)]² ≤ 3.0625\n",
      " 0_p[(37, 20, 23)]² + 0_q[(37, 20, 23)]² ≤ 25.0\n",
      " 0_p[(37, 23, 20)]² + 0_q[(37, 23, 20)]² ≤ 25.0\n",
      " 0_vi[1]*0_vr[5] - 0_vi[5]*0_vr[1] - 0.5773502691896257 0_vr[1]*0_vr[5] - 0.5773502691896257 0_vi[1]*0_vi[5] ≤ 0.0\n",
      " 0_p[(3, 1, 5)]² + 0_q[(3, 1, 5)]² ≤ 3.0625\n",
      " 0_p[(3, 5, 1)]² + 0_q[(3, 5, 1)]² ≤ 3.0625\n",
      " 0_vi[16]*0_vr[17] - 0_vi[17]*0_vr[16] - 0.5773502691896257 0_vr[16]*0_vr[17] - 0.5773502691896257 0_vi[16]*0_vi[17] ≤ 0.0\n",
      " 0_p[(28, 16, 17)]² + 0_q[(28, 16, 17)]² ≤ 25.0\n",
      " 0_p[(28, 17, 16)]² + 0_q[(28, 17, 16)]² ≤ 25.0\n",
      " 0_vi[2]*0_vr[6] - 0_vi[6]*0_vr[2] - 0.5773502691896257 0_vr[2]*0_vr[6] - 0.5773502691896257 0_vi[2]*0_vi[6] ≤ 0.0\n",
      " 0_p[(5, 2, 6)]² + 0_q[(5, 2, 6)]² ≤ 3.0625\n",
      " 0_p[(5, 6, 2)]² + 0_q[(5, 6, 2)]² ≤ 3.0625\n",
      " 0_vi[21]*0_vr[22] - 0_vi[22]*0_vr[21] - 0.5773502691896257 0_vr[21]*0_vr[22] - 0.5773502691896257 0_vi[21]*0_vi[22] ≤ 0.0\n",
      " 0_p[(38, 21, 22)]² + 0_q[(38, 21, 22)]² ≤ 25.0\n",
      " 0_p[(38, 22, 21)]² + 0_q[(38, 22, 21)]² ≤ 25.0\n",
      " 0_vi[12]*0_vr[13] - 0_vi[13]*0_vr[12] - 0.5773502691896257 0_vr[12]*0_vr[13] - 0.5773502691896257 0_vi[12]*0_vi[13] ≤ 0.0\n",
      " 0_p[(20, 12, 13)]² + 0_q[(20, 12, 13)]² ≤ 25.0\n",
      " 0_p[(20, 13, 12)]² + 0_q[(20, 13, 12)]² ≤ 25.0\n",
      " 0_vi[14]*0_vr[16] - 0_vi[16]*0_vr[14] - 0.5773502691896257 0_vr[14]*0_vr[16] - 0.5773502691896257 0_vi[14]*0_vi[16] ≤ 0.0\n",
      " 0_p[(23, 14, 16)]² + 0_q[(23, 14, 16)]² ≤ 25.0\n",
      " 0_p[(23, 16, 14)]² + 0_q[(23, 16, 14)]² ≤ 25.0\n",
      " 0_vi[8]*0_vr[10] - 0_vi[10]*0_vr[8] - 0.5773502691896257 0_vr[8]*0_vr[10] - 0.5773502691896257 0_vi[8]*0_vi[10] ≤ 0.0\n",
      " 0_p[(13, 8, 10)]² + 0_q[(13, 8, 10)]² ≤ 3.0625\n",
      " 0_p[(13, 10, 8)]² + 0_q[(13, 10, 8)]² ≤ 3.0625\n",
      " 0_vi[17]*0_vr[22] - 0_vi[22]*0_vr[17] - 0.5773502691896257 0_vr[17]*0_vr[22] - 0.5773502691896257 0_vi[17]*0_vi[22] ≤ 0.0\n",
      " 0_p[(31, 17, 22)]² + 0_q[(31, 17, 22)]² ≤ 25.0\n",
      " 0_p[(31, 22, 17)]² + 0_q[(31, 22, 17)]² ≤ 25.0\n",
      " 0_vi[9]*0_vr[11] - 0_vi[11]*0_vr[9] - 0.5773502691896257 0_vr[9]*0_vr[11] - 0.5773502691896257 0_vi[9]*0_vi[11] ≤ 0.0\n",
      " 0_p[(14, 9, 11)]² + 0_q[(14, 9, 11)]² ≤ 16.0\n",
      " 0_p[(14, 11, 9)]² + 0_q[(14, 11, 9)]² ≤ 16.0\n",
      " 0_vi[15]*0_vr[24] - 0_vi[24]*0_vr[15] - 0.5773502691896257 0_vr[15]*0_vr[24] - 0.5773502691896257 0_vi[15]*0_vi[24] ≤ 0.0\n",
      " 0_p[(27, 15, 24)]² + 0_q[(27, 15, 24)]² ≤ 25.0\n",
      " 0_p[(27, 24, 15)]² + 0_q[(27, 24, 15)]² ≤ 25.0\n",
      " 0_vi[20]*0_vr[23] - 0_vi[23]*0_vr[20] - 0.5773502691896257 0_vr[20]*0_vr[23] - 0.5773502691896257 0_vi[20]*0_vi[23] ≤ 0.0\n",
      " 0_p[(36, 20, 23)]² + 0_q[(36, 20, 23)]² ≤ 25.0\n",
      " 0_p[(36, 23, 20)]² + 0_q[(36, 23, 20)]² ≤ 25.0\n",
      " 0_vi[9]*0_vr[12] - 0_vi[12]*0_vr[9] - 0.5773502691896257 0_vr[9]*0_vr[12] - 0.5773502691896257 0_vi[9]*0_vi[12] ≤ 0.0\n",
      " 0_p[(15, 9, 12)]² + 0_q[(15, 9, 12)]² ≤ 16.0\n",
      " 0_p[(15, 12, 9)]² + 0_q[(15, 12, 9)]² ≤ 16.0\n",
      " 0_p[(33, 18, 21)]² + 0_q[(33, 18, 21)]² ≤ 25.0\n",
      " 0_p[(33, 21, 18)]² + 0_q[(33, 21, 18)]² ≤ 25.0\n",
      " 0_vi[17]*0_vr[18] - 0_vi[18]*0_vr[17] - 0.5773502691896257 0_vr[17]*0_vr[18] - 0.5773502691896257 0_vi[17]*0_vi[18] ≤ 0.0\n",
      " 0_p[(30, 17, 18)]² + 0_q[(30, 17, 18)]² ≤ 25.0\n",
      " 0_p[(30, 18, 17)]² + 0_q[(30, 18, 17)]² ≤ 25.0\n",
      " 0_vr[1] ≥ -1.05\n",
      " 0_vr[2] ≥ -1.05\n",
      " 0_vr[16] ≥ -1.05\n",
      " 0_vr[11] ≥ -1.05\n",
      " 0_vr[21] ≥ -1.05\n",
      " 0_vr[7] ≥ -1.05\n",
      " 0_vr[9] ≥ -1.05\n",
      " 0_vr[10] ≥ -1.05\n",
      " 0_vr[19] ≥ -1.05\n",
      " 0_vr[17] ≥ -1.05\n",
      " 0_vr[8] ≥ -1.05\n",
      " 0_vr[22] ≥ -1.05\n",
      " 0_vr[6] ≥ -1.05\n",
      " 0_vr[24] ≥ -1.05\n",
      " 0_vr[4] ≥ -1.05\n",
      " 0_vr[3] ≥ -1.05\n",
      " 0_vr[5] ≥ -1.05\n",
      " 0_vr[20] ≥ -1.05\n",
      " 0_vr[23] ≥ -1.05\n",
      " 0_vr[13] ≥ -1.05\n",
      " 0_vr[14] ≥ -1.05\n",
      " 0_vr[15] ≥ -1.05\n",
      " 0_vr[12] ≥ -1.05\n",
      " 0_vr[18] ≥ -1.05\n",
      " 0_vi[1] ≥ -1.05\n",
      " 0_vi[2] ≥ -1.05\n",
      " 0_vi[16] ≥ -1.05\n",
      " 0_vi[11] ≥ -1.05\n",
      " 0_vi[21] ≥ -1.05\n",
      " 0_vi[7] ≥ -1.05\n",
      " 0_vi[9] ≥ -1.05\n",
      " 0_vi[10] ≥ -1.05\n",
      " 0_vi[19] ≥ -1.05\n",
      " 0_vi[17] ≥ -1.05\n",
      " 0_vi[8] ≥ -1.05\n",
      " 0_vi[22] ≥ -1.05\n",
      " 0_vi[6] ≥ -1.05\n",
      " 0_vi[24] ≥ -1.05\n",
      " 0_vi[4] ≥ -1.05\n",
      " 0_vi[3] ≥ -1.05\n",
      " 0_vi[5] ≥ -1.05\n",
      " 0_vi[20] ≥ -1.05\n",
      " 0_vi[23] ≥ -1.05\n",
      " 0_vi[13] ≥ -1.05\n",
      " 0_vi[14] ≥ -1.05\n",
      " 0_vi[15] ≥ -1.05\n",
      " 0_vi[12] ≥ -1.05\n",
      " 0_vi[18] ≥ -1.05\n",
      " 0_pg[18] ≥ 0.012\n",
      " 0_pg[12] ≥ 0.345\n",
      " 0_pg[1] ≥ 0.08\n",
      " 0_pg[32] ≥ 0.27149999999999996\n",
      " 0_pg[2] ≥ 0.08\n",
      " 0_pg[16] ≥ 0.012\n",
      " 0_pg[11] ≥ 0.125\n",
      " 0_pg[21] ≥ 0.27149999999999996\n",
      " 0_pg[7] ≥ 0.076\n",
      " 0_pg[26] ≥ 0.05\n",
      " 0_pg[25] ≥ 0.05\n",
      " 0_pg[10] ≥ 0.125\n",
      " 0_pg[9] ≥ 0.125\n",
      " 0_pg[29] ≥ 0.05\n",
      " 0_pg[19] ≥ 0.012\n",
      " 0_pg[17] ≥ 0.012\n",
      " 0_pg[8] ≥ 0.076\n",
      " 0_pg[22] ≥ 0.27149999999999996\n",
      " 0_pg[6] ≥ 0.08\n",
      " 0_pg[24] ≥ 0.5\n",
      " 0_pg[4] ≥ 0.076\n",
      " 0_pg[3] ≥ 0.076\n",
      " 0_pg[28] ≥ 0.05\n",
      " 0_pg[5] ≥ 0.08\n",
      " 0_pg[20] ≥ 0.012\n",
      " 0_pg[23] ≥ 0.5\n",
      " 0_pg[13] ≥ 0.345\n",
      " 0_pg[31] ≥ 0.27149999999999996\n",
      " 0_pg[14] ≥ 0.345\n",
      " 0_pg[27] ≥ 0.05\n",
      " 0_pg[15] ≥ 0.0\n",
      " 0_pg[33] ≥ 0.7\n",
      " 0_pg[30] ≥ 0.05\n",
      " 0_qg[18] ≥ -0.98\n",
      " 0_qg[12] ≥ -2.72\n",
      " 0_qg[1] ≥ -0.4\n",
      " 0_qg[32] ≥ -1.46\n",
      " 0_qg[2] ≥ -1.06\n",
      " 0_qg[16] ≥ -0.948\n",
      " 0_qg[11] ≥ -2.98\n",
      " 0_qg[21] ≥ -1.03\n",
      " 0_qg[7] ≥ -0.88\n",
      " 0_qg[26] ≥ -0.92\n",
      " 0_qg[25] ≥ -1.11\n",
      " 0_qg[10] ≥ -0.77\n",
      " 0_qg[9] ≥ -2.15\n",
      " 0_qg[29] ≥ -1.08\n",
      " 0_qg[19] ≥ -0.948\n",
      " 0_qg[17] ≥ -1.03\n",
      " 0_qg[8] ≥ -3.32\n",
      " 0_qg[22] ≥ -2.94\n",
      " 0_qg[6] ≥ -0.91\n",
      " 0_qg[24] ≥ -3.38\n",
      " 0_qg[4] ≥ -0.39\n",
      " 0_qg[3] ≥ -0.38\n",
      " 0_qg[28] ≥ -1.17\n",
      " 0_qg[5] ≥ -0.94\n",
      " 0_qg[20] ≥ -0.948\n",
      " 0_qg[23] ≥ -1.91\n",
      " 0_qg[13] ≥ -2.71\n",
      " 0_qg[31] ≥ -2.11\n",
      " 0_qg[14] ≥ -3.05\n",
      " 0_qg[27] ≥ -0.73\n",
      " 0_qg[15] ≥ -2.508\n",
      " 0_qg[33] ≥ -1.91\n",
      " 0_qg[30] ≥ -0.42\n",
      " 0_p[(18, 11, 13)] ≥ -5.0\n",
      " 0_p[(12, 8, 9)] ≥ -1.75\n",
      " 0_p[(1, 1, 2)] ≥ -1.75\n",
      " 0_p[(32, 18, 21)] ≥ -5.0\n",
      " 0_p[(2, 1, 3)] ≥ -1.75\n",
      " 0_p[(16, 10, 11)] ≥ -4.0\n",
      " 0_p[(11, 7, 8)] ≥ -1.75\n",
      " 0_p[(21, 12, 23)] ≥ -5.0\n",
      " 0_p[(7, 3, 24)] ≥ -4.0\n",
      " 0_p[(26, 15, 21)] ≥ -5.0\n",
      " 0_p[(25, 15, 21)] ≥ -5.0\n",
      " 0_p[(35, 19, 20)] ≥ -5.0\n",
      " 0_p[(34, 19, 20)] ≥ -5.0\n",
      " 0_p[(29, 16, 19)] ≥ -5.0\n",
      " 0_p[(10, 6, 10)] ≥ -1.75\n",
      " 0_p[(9, 5, 10)] ≥ -1.75\n",
      " 0_p[(19, 11, 14)] ≥ -5.0\n",
      " 0_p[(17, 10, 12)] ≥ -4.0\n",
      " 0_p[(8, 4, 9)] ≥ -1.75\n",
      " 0_p[(22, 13, 23)] ≥ -5.0\n",
      " 0_p[(6, 3, 9)] ≥ -1.75\n",
      " 0_p[(24, 15, 16)] ≥ -5.0\n",
      " 0_p[(4, 2, 4)] ≥ -1.75\n",
      " 0_p[(37, 20, 23)] ≥ -5.0\n",
      " 0_p[(3, 1, 5)] ≥ -1.75\n",
      " 0_p[(28, 16, 17)] ≥ -5.0\n",
      " 0_p[(5, 2, 6)] ≥ -1.75\n",
      " 0_p[(38, 21, 22)] ≥ -5.0\n",
      " 0_p[(20, 12, 13)] ≥ -5.0\n",
      " 0_p[(23, 14, 16)] ≥ -5.0\n",
      " 0_p[(13, 8, 10)] ≥ -1.75\n",
      " 0_p[(31, 17, 22)] ≥ -5.0\n",
      " 0_p[(14, 9, 11)] ≥ -4.0\n",
      " 0_p[(27, 15, 24)] ≥ -5.0\n",
      " 0_p[(36, 20, 23)] ≥ -5.0\n",
      " 0_p[(15, 9, 12)] ≥ -4.0\n",
      " 0_p[(33, 18, 21)] ≥ -5.0\n",
      " 0_p[(30, 17, 18)] ≥ -5.0\n",
      " 0_p[(18, 13, 11)] ≥ -5.0\n",
      " 0_p[(12, 9, 8)] ≥ -1.75\n",
      " 0_p[(1, 2, 1)] ≥ -1.75\n",
      " 0_p[(32, 21, 18)] ≥ -5.0\n",
      " 0_p[(2, 3, 1)] ≥ -1.75\n",
      " 0_p[(16, 11, 10)] ≥ -4.0\n",
      " 0_p[(11, 8, 7)] ≥ -1.75\n",
      " 0_p[(21, 23, 12)] ≥ -5.0\n",
      " 0_p[(7, 24, 3)] ≥ -4.0\n",
      " 0_p[(26, 21, 15)] ≥ -5.0\n",
      " 0_p[(25, 21, 15)] ≥ -5.0\n",
      " 0_p[(35, 20, 19)] ≥ -5.0\n",
      " 0_p[(34, 20, 19)] ≥ -5.0\n",
      " 0_p[(29, 19, 16)] ≥ -5.0\n",
      " 0_p[(10, 10, 6)] ≥ -1.75\n",
      " 0_p[(9, 10, 5)] ≥ -1.75\n",
      " 0_p[(19, 14, 11)] ≥ -5.0\n",
      " 0_p[(17, 12, 10)] ≥ -4.0\n",
      " 0_p[(8, 9, 4)] ≥ -1.75\n",
      " 0_p[(22, 23, 13)] ≥ -5.0\n",
      " 0_p[(6, 9, 3)] ≥ -1.75\n",
      " 0_p[(24, 16, 15)] ≥ -5.0\n",
      " 0_p[(4, 4, 2)] ≥ -1.75\n",
      " 0_p[(37, 23, 20)] ≥ -5.0\n",
      " 0_p[(3, 5, 1)] ≥ -1.75\n",
      " 0_p[(28, 17, 16)] ≥ -5.0\n",
      " 0_p[(5, 6, 2)] ≥ -1.75\n",
      " 0_p[(38, 22, 21)] ≥ -5.0\n",
      " 0_p[(20, 13, 12)] ≥ -5.0\n",
      " 0_p[(23, 16, 14)] ≥ -5.0\n",
      " 0_p[(13, 10, 8)] ≥ -1.75\n",
      " 0_p[(31, 22, 17)] ≥ -5.0\n",
      " 0_p[(14, 11, 9)] ≥ -4.0\n",
      " 0_p[(27, 24, 15)] ≥ -5.0\n",
      " 0_p[(36, 23, 20)] ≥ -5.0\n",
      " 0_p[(15, 12, 9)] ≥ -4.0\n",
      " 0_p[(33, 21, 18)] ≥ -5.0\n",
      " 0_p[(30, 18, 17)] ≥ -5.0\n",
      " 0_q[(18, 11, 13)] ≥ -5.0\n",
      " 0_q[(12, 8, 9)] ≥ -1.75\n",
      " 0_q[(1, 1, 2)] ≥ -1.75\n",
      " 0_q[(32, 18, 21)] ≥ -5.0\n",
      " 0_q[(2, 1, 3)] ≥ -1.75\n",
      " 0_q[(16, 10, 11)] ≥ -4.0\n",
      " 0_q[(11, 7, 8)] ≥ -1.75\n",
      " 0_q[(21, 12, 23)] ≥ -5.0\n",
      " 0_q[(7, 3, 24)] ≥ -4.0\n",
      " 0_q[(26, 15, 21)] ≥ -5.0\n",
      " 0_q[(25, 15, 21)] ≥ -5.0\n",
      " 0_q[(35, 19, 20)] ≥ -5.0\n",
      " 0_q[(34, 19, 20)] ≥ -5.0\n",
      " 0_q[(29, 16, 19)] ≥ -5.0\n",
      " 0_q[(10, 6, 10)] ≥ -1.75\n",
      " 0_q[(9, 5, 10)] ≥ -1.75\n",
      " 0_q[(19, 11, 14)] ≥ -5.0\n",
      " 0_q[(17, 10, 12)] ≥ -4.0\n",
      " 0_q[(8, 4, 9)] ≥ -1.75\n",
      " 0_q[(22, 13, 23)] ≥ -5.0\n",
      " 0_q[(6, 3, 9)] ≥ -1.75\n",
      " 0_q[(24, 15, 16)] ≥ -5.0\n",
      " 0_q[(4, 2, 4)] ≥ -1.75\n",
      " 0_q[(37, 20, 23)] ≥ -5.0\n",
      " 0_q[(3, 1, 5)] ≥ -1.75\n",
      " 0_q[(28, 16, 17)] ≥ -5.0\n",
      " 0_q[(5, 2, 6)] ≥ -1.75\n",
      " 0_q[(38, 21, 22)] ≥ -5.0\n",
      " 0_q[(20, 12, 13)] ≥ -5.0\n",
      " 0_q[(23, 14, 16)] ≥ -5.0\n",
      " 0_q[(13, 8, 10)] ≥ -1.75\n",
      " 0_q[(31, 17, 22)] ≥ -5.0\n",
      " 0_q[(14, 9, 11)] ≥ -4.0\n",
      " 0_q[(27, 15, 24)] ≥ -5.0\n",
      " 0_q[(36, 20, 23)] ≥ -5.0\n",
      " 0_q[(15, 9, 12)] ≥ -4.0\n",
      " 0_q[(33, 18, 21)] ≥ -5.0\n",
      " 0_q[(30, 17, 18)] ≥ -5.0\n",
      " 0_q[(18, 13, 11)] ≥ -5.0\n",
      " 0_q[(12, 9, 8)] ≥ -1.75\n",
      " 0_q[(1, 2, 1)] ≥ -1.75\n",
      " 0_q[(32, 21, 18)] ≥ -5.0\n",
      " 0_q[(2, 3, 1)] ≥ -1.75\n",
      " 0_q[(16, 11, 10)] ≥ -4.0\n",
      " 0_q[(11, 8, 7)] ≥ -1.75\n",
      " 0_q[(21, 23, 12)] ≥ -5.0\n",
      " 0_q[(7, 24, 3)] ≥ -4.0\n",
      " 0_q[(26, 21, 15)] ≥ -5.0\n",
      " 0_q[(25, 21, 15)] ≥ -5.0\n",
      " 0_q[(35, 20, 19)] ≥ -5.0\n",
      " 0_q[(34, 20, 19)] ≥ -5.0\n",
      " 0_q[(29, 19, 16)] ≥ -5.0\n",
      " 0_q[(10, 10, 6)] ≥ -1.75\n",
      " 0_q[(9, 10, 5)] ≥ -1.75\n",
      " 0_q[(19, 14, 11)] ≥ -5.0\n",
      " 0_q[(17, 12, 10)] ≥ -4.0\n",
      " 0_q[(8, 9, 4)] ≥ -1.75\n",
      " 0_q[(22, 23, 13)] ≥ -5.0\n",
      " 0_q[(6, 9, 3)] ≥ -1.75\n",
      " 0_q[(24, 16, 15)] ≥ -5.0\n",
      " 0_q[(4, 4, 2)] ≥ -1.75\n",
      " 0_q[(37, 23, 20)] ≥ -5.0\n",
      " 0_q[(3, 5, 1)] ≥ -1.75\n",
      " 0_q[(28, 17, 16)] ≥ -5.0\n",
      " 0_q[(5, 6, 2)] ≥ -1.75\n",
      " 0_q[(38, 22, 21)] ≥ -5.0\n",
      " 0_q[(20, 13, 12)] ≥ -5.0\n",
      " 0_q[(23, 16, 14)] ≥ -5.0\n",
      " 0_q[(13, 10, 8)] ≥ -1.75\n",
      " 0_q[(31, 22, 17)] ≥ -5.0\n",
      " 0_q[(14, 11, 9)] ≥ -4.0\n",
      " 0_q[(27, 24, 15)] ≥ -5.0\n",
      " 0_q[(36, 23, 20)] ≥ -5.0\n",
      " 0_q[(15, 12, 9)] ≥ -4.0\n",
      " 0_q[(33, 21, 18)] ≥ -5.0\n",
      " 0_q[(30, 18, 17)] ≥ -5.0\n",
      " 0_vr[1] ≤ 1.05\n",
      " 0_vr[2] ≤ 1.05\n",
      " 0_vr[16] ≤ 1.05\n",
      " 0_vr[11] ≤ 1.05\n",
      " 0_vr[21] ≤ 1.05\n",
      " 0_vr[7] ≤ 1.05\n",
      " 0_vr[9] ≤ 1.05\n",
      " 0_vr[10] ≤ 1.05\n",
      " 0_vr[19] ≤ 1.05\n",
      " 0_vr[17] ≤ 1.05\n",
      " 0_vr[8] ≤ 1.05\n",
      " 0_vr[22] ≤ 1.05\n",
      " 0_vr[6] ≤ 1.05\n",
      " 0_vr[24] ≤ 1.05\n",
      " 0_vr[4] ≤ 1.05\n",
      " 0_vr[3] ≤ 1.05\n",
      " 0_vr[5] ≤ 1.05\n",
      " 0_vr[20] ≤ 1.05\n",
      " 0_vr[23] ≤ 1.05\n",
      " 0_vr[13] ≤ 1.05\n",
      " 0_vr[14] ≤ 1.05\n",
      " 0_vr[15] ≤ 1.05\n",
      " 0_vr[12] ≤ 1.05\n",
      " 0_vr[18] ≤ 1.05\n",
      " 0_vi[1] ≤ 1.05\n",
      " 0_vi[2] ≤ 1.05\n",
      " 0_vi[16] ≤ 1.05\n",
      " 0_vi[11] ≤ 1.05\n",
      " 0_vi[21] ≤ 1.05\n",
      " 0_vi[7] ≤ 1.05\n",
      " 0_vi[9] ≤ 1.05\n",
      " 0_vi[10] ≤ 1.05\n",
      " 0_vi[19] ≤ 1.05\n",
      " 0_vi[17] ≤ 1.05\n",
      " 0_vi[8] ≤ 1.05\n",
      " 0_vi[22] ≤ 1.05\n",
      " 0_vi[6] ≤ 1.05\n",
      " 0_vi[24] ≤ 1.05\n",
      " 0_vi[4] ≤ 1.05\n",
      " 0_vi[3] ≤ 1.05\n",
      " 0_vi[5] ≤ 1.05\n",
      " 0_vi[20] ≤ 1.05\n",
      " 0_vi[23] ≤ 1.05\n",
      " 0_vi[13] ≤ 1.05\n",
      " 0_vi[14] ≤ 1.05\n",
      " 0_vi[15] ≤ 1.05\n",
      " 0_vi[12] ≤ 1.05\n",
      " 0_vi[18] ≤ 1.05\n",
      " 0_pg[18] ≤ 1.95\n",
      " 0_pg[12] ≤ 5.44\n",
      " 0_pg[1] ≤ 0.79\n",
      " 0_pg[32] ≤ 2.92\n",
      " 0_pg[2] ≤ 2.11\n",
      " 0_pg[16] ≤ 1.59\n",
      " 0_pg[11] ≤ 5.95\n",
      " 0_pg[21] ≤ 2.06\n",
      " 0_pg[7] ≤ 1.76\n",
      " 0_pg[26] ≤ 1.84\n",
      " 0_pg[25] ≤ 2.22\n",
      " 0_pg[10] ≤ 1.53\n",
      " 0_pg[9] ≤ 4.3\n",
      " 0_pg[29] ≤ 2.16\n",
      " 0_pg[19] ≤ 1.62\n",
      " 0_pg[17] ≤ 2.06\n",
      " 0_pg[8] ≤ 6.63\n",
      " 0_pg[22] ≤ 5.88\n",
      " 0_pg[6] ≤ 1.82\n",
      " 0_pg[24] ≤ 6.75\n",
      " 0_pg[4] ≤ 0.78\n",
      " 0_pg[3] ≤ 0.75\n",
      " 0_pg[28] ≤ 2.33\n",
      " 0_pg[5] ≤ 1.88\n",
      " 0_pg[20] ≤ 1.39\n",
      " 0_pg[23] ≤ 3.82\n",
      " 0_pg[13] ≤ 5.42\n",
      " 0_pg[31] ≤ 4.21\n",
      " 0_pg[14] ≤ 6.09\n",
      " 0_pg[27] ≤ 1.46\n",
      " 0_pg[15] ≤ 0.0\n",
      " 0_pg[33] ≤ 3.82\n",
      " 0_pg[30] ≤ 0.83\n",
      " 0_qg[18] ≤ 0.98\n",
      " 0_qg[12] ≤ 2.72\n",
      " 0_qg[1] ≤ 0.4\n",
      " 0_qg[32] ≤ 1.46\n",
      " 0_qg[2] ≤ 1.06\n",
      " 0_qg[16] ≤ 0.948\n",
      " 0_qg[11] ≤ 2.98\n",
      " 0_qg[21] ≤ 1.03\n",
      " 0_qg[7] ≤ 0.88\n",
      " 0_qg[26] ≤ 0.92\n",
      " 0_qg[25] ≤ 1.11\n",
      " 0_qg[10] ≤ 0.77\n",
      " 0_qg[9] ≤ 2.15\n",
      " 0_qg[29] ≤ 1.08\n",
      " 0_qg[19] ≤ 0.948\n",
      " 0_qg[17] ≤ 1.03\n",
      " 0_qg[8] ≤ 3.32\n",
      " 0_qg[22] ≤ 2.94\n",
      " 0_qg[6] ≤ 0.91\n",
      " 0_qg[24] ≤ 3.38\n",
      " 0_qg[4] ≤ 0.39\n",
      " 0_qg[3] ≤ 0.38\n",
      " 0_qg[28] ≤ 1.17\n",
      " 0_qg[5] ≤ 0.94\n",
      " 0_qg[20] ≤ 0.948\n",
      " 0_qg[23] ≤ 2.0\n",
      " 0_qg[13] ≤ 2.71\n",
      " 0_qg[31] ≤ 2.11\n",
      " 0_qg[14] ≤ 3.05\n",
      " 0_qg[27] ≤ 0.73\n",
      " 0_qg[15] ≤ 2.508\n",
      " 0_qg[33] ≤ 1.91\n",
      " 0_qg[30] ≤ 0.42\n",
      " 0_p[(18, 11, 13)] ≤ 5.0\n",
      " 0_p[(12, 8, 9)] ≤ 1.75\n",
      " 0_p[(1, 1, 2)] ≤ 1.75\n",
      " 0_p[(32, 18, 21)] ≤ 5.0\n",
      " 0_p[(2, 1, 3)] ≤ 1.75\n",
      " 0_p[(16, 10, 11)] ≤ 4.0\n",
      " 0_p[(11, 7, 8)] ≤ 1.75\n",
      " 0_p[(21, 12, 23)] ≤ 5.0\n",
      " 0_p[(7, 3, 24)] ≤ 4.0\n",
      " 0_p[(26, 15, 21)] ≤ 5.0\n",
      " 0_p[(25, 15, 21)] ≤ 5.0\n",
      " 0_p[(35, 19, 20)] ≤ 5.0\n",
      " 0_p[(34, 19, 20)] ≤ 5.0\n",
      " 0_p[(29, 16, 19)] ≤ 5.0\n",
      " 0_p[(10, 6, 10)] ≤ 1.75\n",
      " 0_p[(9, 5, 10)] ≤ 1.75\n",
      " 0_p[(19, 11, 14)] ≤ 5.0\n",
      " 0_p[(17, 10, 12)] ≤ 4.0\n",
      " 0_p[(8, 4, 9)] ≤ 1.75\n",
      " 0_p[(22, 13, 23)] ≤ 5.0\n",
      " 0_p[(6, 3, 9)] ≤ 1.75\n",
      " 0_p[(24, 15, 16)] ≤ 5.0\n",
      " 0_p[(4, 2, 4)] ≤ 1.75\n",
      " 0_p[(37, 20, 23)] ≤ 5.0\n",
      " 0_p[(3, 1, 5)] ≤ 1.75\n",
      " 0_p[(28, 16, 17)] ≤ 5.0\n",
      " 0_p[(5, 2, 6)] ≤ 1.75\n",
      " 0_p[(38, 21, 22)] ≤ 5.0\n",
      " 0_p[(20, 12, 13)] ≤ 5.0\n",
      " 0_p[(23, 14, 16)] ≤ 5.0\n",
      " 0_p[(13, 8, 10)] ≤ 1.75\n",
      " 0_p[(31, 17, 22)] ≤ 5.0\n",
      " 0_p[(14, 9, 11)] ≤ 4.0\n",
      " 0_p[(27, 15, 24)] ≤ 5.0\n",
      " 0_p[(36, 20, 23)] ≤ 5.0\n",
      " 0_p[(15, 9, 12)] ≤ 4.0\n",
      " 0_p[(33, 18, 21)] ≤ 5.0\n",
      " 0_p[(30, 17, 18)] ≤ 5.0\n",
      " 0_p[(18, 13, 11)] ≤ 5.0\n",
      " 0_p[(12, 9, 8)] ≤ 1.75\n",
      " 0_p[(1, 2, 1)] ≤ 1.75\n",
      " 0_p[(32, 21, 18)] ≤ 5.0\n",
      " 0_p[(2, 3, 1)] ≤ 1.75\n",
      " 0_p[(16, 11, 10)] ≤ 4.0\n",
      " 0_p[(11, 8, 7)] ≤ 1.75\n",
      " 0_p[(21, 23, 12)] ≤ 5.0\n",
      " 0_p[(7, 24, 3)] ≤ 4.0\n",
      " 0_p[(26, 21, 15)] ≤ 5.0\n",
      " 0_p[(25, 21, 15)] ≤ 5.0\n",
      " 0_p[(35, 20, 19)] ≤ 5.0\n",
      " 0_p[(34, 20, 19)] ≤ 5.0\n",
      " 0_p[(29, 19, 16)] ≤ 5.0\n",
      " 0_p[(10, 10, 6)] ≤ 1.75\n",
      " 0_p[(9, 10, 5)] ≤ 1.75\n",
      " 0_p[(19, 14, 11)] ≤ 5.0\n",
      " 0_p[(17, 12, 10)] ≤ 4.0\n",
      " 0_p[(8, 9, 4)] ≤ 1.75\n",
      " 0_p[(22, 23, 13)] ≤ 5.0\n",
      " 0_p[(6, 9, 3)] ≤ 1.75\n",
      " 0_p[(24, 16, 15)] ≤ 5.0\n",
      " 0_p[(4, 4, 2)] ≤ 1.75\n",
      " 0_p[(37, 23, 20)] ≤ 5.0\n",
      " 0_p[(3, 5, 1)] ≤ 1.75\n",
      " 0_p[(28, 17, 16)] ≤ 5.0\n",
      " 0_p[(5, 6, 2)] ≤ 1.75\n",
      " 0_p[(38, 22, 21)] ≤ 5.0\n",
      " 0_p[(20, 13, 12)] ≤ 5.0\n",
      " 0_p[(23, 16, 14)] ≤ 5.0\n",
      " 0_p[(13, 10, 8)] ≤ 1.75\n",
      " 0_p[(31, 22, 17)] ≤ 5.0\n",
      " 0_p[(14, 11, 9)] ≤ 4.0\n",
      " 0_p[(27, 24, 15)] ≤ 5.0\n",
      " 0_p[(36, 23, 20)] ≤ 5.0\n",
      " 0_p[(15, 12, 9)] ≤ 4.0\n",
      " 0_p[(33, 21, 18)] ≤ 5.0\n",
      " 0_p[(30, 18, 17)] ≤ 5.0\n",
      " 0_q[(18, 11, 13)] ≤ 5.0\n",
      " 0_q[(12, 8, 9)] ≤ 1.75\n",
      " 0_q[(1, 1, 2)] ≤ 1.75\n",
      " 0_q[(32, 18, 21)] ≤ 5.0\n",
      " 0_q[(2, 1, 3)] ≤ 1.75\n",
      " 0_q[(16, 10, 11)] ≤ 4.0\n",
      " 0_q[(11, 7, 8)] ≤ 1.75\n",
      " 0_q[(21, 12, 23)] ≤ 5.0\n",
      " 0_q[(7, 3, 24)] ≤ 4.0\n",
      " 0_q[(26, 15, 21)] ≤ 5.0\n",
      " 0_q[(25, 15, 21)] ≤ 5.0\n",
      " 0_q[(35, 19, 20)] ≤ 5.0\n",
      " 0_q[(34, 19, 20)] ≤ 5.0\n",
      " 0_q[(29, 16, 19)] ≤ 5.0\n",
      " 0_q[(10, 6, 10)] ≤ 1.75\n",
      " 0_q[(9, 5, 10)] ≤ 1.75\n",
      " 0_q[(19, 11, 14)] ≤ 5.0\n",
      " 0_q[(17, 10, 12)] ≤ 4.0\n",
      " 0_q[(8, 4, 9)] ≤ 1.75\n",
      " 0_q[(22, 13, 23)] ≤ 5.0\n",
      " 0_q[(6, 3, 9)] ≤ 1.75\n",
      " 0_q[(24, 15, 16)] ≤ 5.0\n",
      " 0_q[(4, 2, 4)] ≤ 1.75\n",
      " 0_q[(37, 20, 23)] ≤ 5.0\n",
      " 0_q[(3, 1, 5)] ≤ 1.75\n",
      " 0_q[(28, 16, 17)] ≤ 5.0\n",
      " 0_q[(5, 2, 6)] ≤ 1.75\n",
      " 0_q[(38, 21, 22)] ≤ 5.0\n",
      " 0_q[(20, 12, 13)] ≤ 5.0\n",
      " 0_q[(23, 14, 16)] ≤ 5.0\n",
      " 0_q[(13, 8, 10)] ≤ 1.75\n",
      " 0_q[(31, 17, 22)] ≤ 5.0\n",
      " 0_q[(14, 9, 11)] ≤ 4.0\n",
      " 0_q[(27, 15, 24)] ≤ 5.0\n",
      " 0_q[(36, 20, 23)] ≤ 5.0\n",
      " 0_q[(15, 9, 12)] ≤ 4.0\n",
      " 0_q[(33, 18, 21)] ≤ 5.0\n",
      " 0_q[(30, 17, 18)] ≤ 5.0\n",
      " 0_q[(18, 13, 11)] ≤ 5.0\n",
      " 0_q[(12, 9, 8)] ≤ 1.75\n",
      " 0_q[(1, 2, 1)] ≤ 1.75\n",
      " 0_q[(32, 21, 18)] ≤ 5.0\n",
      " 0_q[(2, 3, 1)] ≤ 1.75\n",
      " 0_q[(16, 11, 10)] ≤ 4.0\n",
      " 0_q[(11, 8, 7)] ≤ 1.75\n",
      " 0_q[(21, 23, 12)] ≤ 5.0\n",
      " 0_q[(7, 24, 3)] ≤ 4.0\n",
      " 0_q[(26, 21, 15)] ≤ 5.0\n",
      " 0_q[(25, 21, 15)] ≤ 5.0\n",
      " 0_q[(35, 20, 19)] ≤ 5.0\n",
      " 0_q[(34, 20, 19)] ≤ 5.0\n",
      " 0_q[(29, 19, 16)] ≤ 5.0\n",
      " 0_q[(10, 10, 6)] ≤ 1.75\n",
      " 0_q[(9, 10, 5)] ≤ 1.75\n",
      " 0_q[(19, 14, 11)] ≤ 5.0\n",
      " 0_q[(17, 12, 10)] ≤ 4.0\n",
      " 0_q[(8, 9, 4)] ≤ 1.75\n",
      " 0_q[(22, 23, 13)] ≤ 5.0\n",
      " 0_q[(6, 9, 3)] ≤ 1.75\n",
      " 0_q[(24, 16, 15)] ≤ 5.0\n",
      " 0_q[(4, 4, 2)] ≤ 1.75\n",
      " 0_q[(37, 23, 20)] ≤ 5.0\n",
      " 0_q[(3, 5, 1)] ≤ 1.75\n",
      " 0_q[(28, 17, 16)] ≤ 5.0\n",
      " 0_q[(5, 6, 2)] ≤ 1.75\n",
      " 0_q[(38, 22, 21)] ≤ 5.0\n",
      " 0_q[(20, 13, 12)] ≤ 5.0\n",
      " 0_q[(23, 16, 14)] ≤ 5.0\n",
      " 0_q[(13, 10, 8)] ≤ 1.75\n",
      " 0_q[(31, 22, 17)] ≤ 5.0\n",
      " 0_q[(14, 11, 9)] ≤ 4.0\n",
      " 0_q[(27, 24, 15)] ≤ 5.0\n",
      " 0_q[(36, 23, 20)] ≤ 5.0\n",
      " 0_q[(15, 12, 9)] ≤ 4.0\n",
      " 0_q[(33, 21, 18)] ≤ 5.0\n",
      " 0_q[(30, 18, 17)] ≤ 5.0\n"
     ]
    }
   ],
   "source": [
    "pm = instantiate_model(file, ACRPowerModel, PowerModels.build_opf)\n",
    "print(pm.model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 1.3.0",
   "language": "julia",
   "name": "julia-1.3"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.3.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}