pglib_case14.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Precompiling COSMO [1e616198-aa4e-51ec-90a2-23f7fbd31d8d]\n",
      "└ @ Base loading.jl:1273\n",
      "┌ Info: Precompiling MosekTools [1ec41992-ff65-5c91-ac43-2df89e9693a4]\n",
      "└ @ Base loading.jl:1273\n"
     ]
    },
    {
     "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_case14_ieee__api.m\""
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = \"../pglib-opf/api/pglib_opf_case14_ieee__api.m\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m[info | PowerModels]: removing 3 cost terms from generator 4: Float64[]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 1: [792.0951, 0.0]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 3 cost terms from generator 5: Float64[]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 2: [2326.9494, 0.0]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 3 cost terms from generator 3: Float64[]\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...:      870\n",
      "Number of nonzeros in inequality constraint Jacobian.:      456\n",
      "Number of nonzeros in Lagrangian Hessian.............:      718\n",
      "\n",
      "Total number of variables............................:      115\n",
      "                     variables with only lower bounds:        0\n",
      "                variables with lower and upper bounds:      115\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:      109\n",
      "Total number of inequality constraints...............:      108\n",
      "        inequality constraints with only lower bounds:       34\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:       74\n",
      "\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "   0  3.1190414e+01 1.85e+00 1.88e+01  -1.0 0.00e+00    -  0.00e+00 0.00e+00   0\n",
      "   1  7.2464260e+03 6.71e-02 3.25e+02  -1.0 2.58e+00    -  3.72e-03 9.90e-01H  1\n",
      "   2  6.8890028e+03 6.16e-02 2.95e+02  -1.0 8.69e+00    -  3.91e-02 8.33e-02f  1\n",
      "   3  5.9473276e+03 5.32e-02 2.40e+02  -1.0 1.44e+01    -  3.48e-02 1.51e-01f  1\n",
      "   4  5.9363676e+03 5.27e-02 2.38e+02  -1.0 4.63e+00    -  4.48e-01 8.77e-03h  1\n",
      "   5  5.8295068e+03 3.77e-02 9.21e+01  -1.0 1.61e+00    -  5.24e-01 5.87e-01f  1\n",
      "   6  5.8639998e+03 4.01e-02 5.46e+01  -1.0 4.17e-01    -  6.51e-01 4.08e-01h  1\n",
      "   7  5.8995143e+03 2.39e-02 3.24e+01  -1.0 3.03e-01    -  4.39e-01 3.87e-01h  1\n",
      "   8  5.9236337e+03 1.80e-02 6.67e+01  -1.0 2.05e-01    -  5.70e-01 3.10e-01h  1\n",
      "   9  6.0047396e+03 2.55e-03 4.23e+00  -1.0 1.34e-01    -  1.00e+00 8.64e-01h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  10  6.0038381e+03 1.13e-03 8.36e+01  -1.0 9.44e-02    -  1.00e+00 3.09e-01h  1\n",
      "  11  6.0188955e+03 2.95e-05 2.33e+00  -1.0 4.68e-02    -  1.00e+00 1.00e+00h  1\n",
      "  12  6.0181320e+03 1.25e-06 5.69e-02  -1.0 4.87e-03    -  1.00e+00 1.00e+00h  1\n",
      "  13  5.9998990e+03 7.88e-05 2.44e+00  -2.5 7.64e-02    -  9.29e-01 9.98e-01f  1\n",
      "  14  5.9995814e+03 2.76e-05 5.28e+00  -2.5 2.50e-03    -  1.00e+00 8.70e-01h  1\n",
      "  15  5.9998360e+03 3.15e-08 1.19e-02  -2.5 1.36e-03    -  1.00e+00 1.00e+00f  1\n",
      "  16  5.9993807e+03 4.67e-08 3.96e-03  -3.8 1.91e-03    -  1.00e+00 1.00e+00f  1\n",
      "  17  5.9993877e+03 2.05e-11 1.61e-05  -3.8 3.39e-05    -  1.00e+00 1.00e+00h  1\n",
      "  18  5.9993634e+03 1.29e-10 1.78e-05  -5.7 1.01e-04    -  1.00e+00 1.00e+00f  1\n",
      "  19  5.9993631e+03 1.02e-11 4.01e-01  -8.6 1.10e-06    -  1.00e+00 9.21e-01h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  20  5.9993631e+03 4.00e-15 2.00e-09  -8.6 8.08e-08    -  1.00e+00 1.00e+00f  1\n",
      "\n",
      "Number of Iterations....: 20\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   2.5782095299086012e+02    5.9993631186951015e+03\n",
      "Dual infeasibility......:   2.0017831281471388e-09    4.6580480489721083e-08\n",
      "Constraint violation....:   3.9968028886505635e-15    3.9968028886505635e-15\n",
      "Complementarity.........:   4.5968811581602491e-09    1.0696709852852297e-07\n",
      "Overall NLP error.......:   4.5968811581602491e-09    1.0696709852852297e-07\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 22\n",
      "Number of objective gradient evaluations             = 21\n",
      "Number of equality constraint evaluations            = 22\n",
      "Number of inequality constraint evaluations          = 22\n",
      "Number of equality constraint Jacobian evaluations   = 21\n",
      "Number of inequality constraint Jacobian evaluations = 21\n",
      "Number of Lagrangian Hessian evaluations             = 20\n",
      "Total CPU secs in IPOPT (w/o function evaluations)   =      1.785\n",
      "Total CPU secs in NLP function evaluations           =      0.646\n",
      "\n",
      "EXIT: Optimal Solution Found.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Dict{String,Any} with 8 entries:\n",
       "  \"solve_time\"         => 2.80645\n",
       "  \"optimizer\"          => \"Ipopt\"\n",
       "  \"termination_status\" => LOCALLY_SOLVED\n",
       "  \"dual_status\"        => FEASIBLE_POINT\n",
       "  \"primal_status\"      => FEASIBLE_POINT\n",
       "  \"objective\"          => 5999.36\n",
       "  \"solution\"           => Dict{String,Any}(\"baseMVA\"=>100.0,\"branch\"=>Dict{Stri…\n",
       "  \"objective_lb\"       => -Inf"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipopt_rec = run_opf(file, ACRPowerModel, Ipopt.Optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m[info | PowerModels]: removing 3 cost terms from generator 4: Float64[]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 1: [792.0951, 0.0]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 3 cost terms from generator 5: Float64[]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 2: [2326.9494, 0.0]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 3 cost terms from generator 3: Float64[]\u001b[39m\n",
      "Problem\n",
      "  Name                   :                 \n",
      "  Objective sense        : min             \n",
      "  Type                   : CONIC (conic optimization problem)\n",
      "  Constraints            : 910             \n",
      "  Cones                  : 40              \n",
      "  Scalar variables       : 511             \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 : 109\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                 : 90              \n",
      "Presolve terminated. Time: 0.00    \n",
      "Problem\n",
      "  Name                   :                 \n",
      "  Objective sense        : min             \n",
      "  Type                   : CONIC (conic optimization problem)\n",
      "  Constraints            : 910             \n",
      "  Cones                  : 40              \n",
      "  Scalar variables       : 511             \n",
      "  Matrix variables       : 1               \n",
      "  Integer variables      : 0               \n",
      "\n",
      "Optimizer  - threads                : 4               \n",
      "Optimizer  - solved problem         : the primal      \n",
      "Optimizer  - Constraints            : 562\n",
      "Optimizer  - Cones                  : 39\n",
      "Optimizer  - Scalar variables       : 370               conic                  : 257             \n",
      "Optimizer  - Semi-definite variables: 1                 scalarized             : 406             \n",
      "Factor     - setup time             : 0.01              dense det. time        : 0.00            \n",
      "Factor     - ML order time          : 0.01              GP order time          : 0.00            \n",
      "Factor     - nonzeros before factor : 8.50e+04          after factor           : 8.97e+04        \n",
      "Factor     - dense dim.             : 2                 flops                  : 2.37e+07        \n",
      "ITE PFEAS    DFEAS    GFEAS    PRSTATUS   POBJ              DOBJ              MU       TIME  \n",
      "0   5.6e+00  2.3e+03  7.4e+01  0.00e+00   9.926766140e+02   9.197766140e+02   1.0e+00  0.02  \n",
      "1   9.6e-01  4.0e+02  3.0e+01  -9.99e-01  -5.914502300e+02  -6.590826777e+02  1.7e-01  0.03  \n",
      "2   4.8e-01  2.0e+02  2.0e+01  -9.45e-01  -1.521387683e+03  -1.577979587e+03  8.6e-02  0.03  \n",
      "3   4.1e-01  1.7e+02  1.5e+01  -3.91e-01  -1.023239799e+03  -1.072867756e+03  7.3e-02  0.04  \n",
      "4   9.8e-02  4.0e+01  2.5e+00  -1.15e-01  -4.354879640e+02  -4.516176932e+02  1.7e-02  0.05  \n",
      "5   4.2e-02  1.7e+01  8.3e-01  7.93e-01   -2.132657726e+02  -2.211632523e+02  7.4e-03  0.06  \n",
      "6   2.5e-02  1.0e+01  6.3e-01  1.15e-01   -1.712900981e+02  -1.782607776e+02  4.4e-03  0.07  \n",
      "7   1.1e-02  4.4e+00  2.9e-01  -2.52e-01  2.433577615e+02   2.397449880e+02   1.9e-03  0.07  \n",
      "8   4.4e-03  1.8e+00  1.1e-01  2.27e-01   8.676915234e+02   8.665094217e+02   7.7e-04  0.08  \n",
      "9   2.2e-03  9.1e-01  6.2e-02  -3.24e-01  1.429245133e+03   1.429895518e+03   3.9e-04  0.09  \n",
      "10  8.8e-04  3.6e-01  2.2e-02  -6.84e-02  2.464607009e+03   2.465893656e+03   1.6e-04  0.10  \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": [
      "11  6.1e-04  2.5e-01  1.4e-02  4.54e-01   2.831865821e+03   2.833106894e+03   1.1e-04  0.11  \n",
      "12  2.8e-04  1.2e-01  7.2e-03  5.56e-01   3.098821580e+03   3.101210205e+03   5.0e-05  0.11  \n",
      "13  1.3e-04  5.4e-02  3.8e-03  -8.53e-01  3.658137851e+03   3.662004908e+03   2.3e-05  0.12  \n",
      "14  5.5e-05  2.3e-02  9.0e-04  6.39e-01   4.845071297e+03   4.846211379e+03   9.8e-06  0.13  \n",
      "15  2.8e-05  1.1e-02  3.8e-04  6.66e-01   5.200565722e+03   5.201424612e+03   4.9e-06  0.13  \n",
      "16  2.3e-05  9.6e-03  4.0e-04  1.18e-01   5.132719972e+03   5.134137659e+03   4.1e-06  0.14  \n",
      "17  1.5e-05  6.1e-03  1.8e-04  4.61e-01   5.506880095e+03   5.507616467e+03   2.6e-06  0.14  \n",
      "18  2.8e-06  1.2e-03  1.8e-05  5.97e-01   5.879275203e+03   5.879476309e+03   4.9e-07  0.15  \n",
      "19  2.4e-07  1.0e-04  6.7e-07  9.05e-01   5.984534048e+03   5.984574763e+03   4.3e-08  0.15  \n",
      "20  9.3e-08  5.3e-06  8.4e-09  9.99e-01   5.997257932e+03   5.997260262e+03   2.4e-09  0.17  \n",
      "21  5.5e-08  3.1e-06  3.8e-09  9.99e-01   5.997611699e+03   5.997613073e+03   1.4e-09  0.18  \n",
      "22  5.1e-08  2.2e-06  2.2e-09  9.98e-01   5.997793590e+03   5.997794552e+03   1.0e-09  0.18  \n",
      "23  2.3e-08  2.1e-07  6.8e-11  1.00e+00   5.998078854e+03   5.998078952e+03   1.0e-10  0.19  \n",
      "24  2.3e-08  2.1e-07  6.8e-11  1.00e+00   5.998078854e+03   5.998078952e+03   1.0e-10  0.20  \n",
      "25  2.3e-08  2.1e-07  6.8e-11  1.00e+00   5.998078854e+03   5.998078952e+03   1.0e-10  0.21  \n",
      "Optimizer terminated. Time: 0.22    \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Dict{String,Any} with 8 entries:\n",
       "  \"solve_time\"         => 0.215965\n",
       "  \"optimizer\"          => \"Mosek\"\n",
       "  \"termination_status\" => SLOW_PROGRESS\n",
       "  \"dual_status\"        => FEASIBLE_POINT\n",
       "  \"primal_status\"      => FEASIBLE_POINT\n",
       "  \"objective\"          => 5998.08\n",
       "  \"solution\"           => Dict{String,Any}(\"baseMVA\"=>100.0,\"branch\"=>Dict{Stri…\n",
       "  \"objective_lb\"       => 0.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mosek_sdp = run_opf(file, SDPWRMPowerModel, Mosek.Optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m[info | PowerModels]: removing 3 cost terms from generator 4: Float64[]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 1: [792.0951, 0.0]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 3 cost terms from generator 5: Float64[]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 2: [2326.9494, 0.0]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 3 cost terms from generator 3: Float64[]\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 188 rows, 144 columns and 581 nonzeros\n",
      "Model fingerprint: 0xc34aa71e\n",
      "Model has 60 quadratic constraints\n",
      "Coefficient statistics:\n",
      "  Matrix range     [2e-01, 2e+01]\n",
      "  QMatrix range    [1e+00, 1e+00]\n",
      "  Objective range  [8e+02, 2e+03]\n",
      "  Bounds range     [3e-01, 7e+00]\n",
      "  RHS range        [2e-02, 2e+00]\n",
      "  QRHS range       [3e-01, 4e+01]\n",
      "Presolve removed 6 rows and 7 columns\n",
      "Presolve time: 0.00s\n",
      "Presolved: 480 rows, 213 columns, 944 nonzeros\n",
      "Presolved model has 56 second-order cone constraints\n",
      "Ordering time: 0.00s\n",
      "\n",
      "Barrier statistics:\n",
      " AA' NZ     : 4.520e+03\n",
      " Factor NZ  : 7.317e+03\n",
      " Factor Ops : 1.437e+05 (less than 1 second per iteration)\n",
      " Threads    : 1\n",
      "\n",
      "                  Objective                Residual\n",
      "Iter       Primal          Dual         Primal    Dual     Compl     Time\n",
      "   0   1.26057163e+04 -3.68014561e+04  4.64e+00 8.77e+02  2.20e+03     0s\n",
      "   1   4.39833299e+03 -1.45740892e+05  5.51e-01 5.40e+01  4.19e+02     0s\n",
      "   2   2.72018338e+03 -1.95446758e+04  2.46e-01 2.72e+00  5.77e+01     0s\n",
      "   3   2.50606277e+03 -4.65370756e+03  1.29e-01 5.00e-01  1.87e+01     0s\n",
      "   4   3.08235470e+03  9.40423874e+01  7.17e-02 2.37e-01  9.95e+00     0s\n",
      "   5   3.36341182e+03  3.31121885e+03  4.64e-02 4.69e-02  3.53e+00     0s\n",
      "   6   3.72080373e+03  4.37893896e+03  3.17e-02 3.02e-02  2.35e+00     0s\n",
      "   7   5.15005333e+03  4.93894847e+03  1.22e-02 1.36e-02  1.82e+00     0s\n",
      "   8   5.47604335e+03  5.53343686e+03  3.99e-03 2.70e-03  4.41e-01     0s\n",
      "   9   5.63073978e+03  5.66423796e+03  1.06e-03 3.25e-04  9.15e-02     0s\n",
      "  10   5.68601418e+03  5.68921615e+03  1.40e-04 3.54e-06  1.45e-02     0s\n",
      "  11   5.69074692e+03  5.69116493e+03  1.99e-05 3.26e-07  2.14e-03     0s\n",
      "  12   5.69158778e+03  5.69175195e+03  3.63e-06 1.46e-08  2.38e-04     0s\n",
      "  13   5.69178774e+03  5.69179146e+03  1.94e-07 2.10e-07  2.16e-05     0s\n",
      "  14   5.69179883e+03  5.69179853e+03  1.52e-08 2.43e-08  2.74e-06     0s\n",
      "  15   5.69179883e+03  5.69179877e+03  2.19e-09 5.14e-09  4.31e-07     0s\n",
      "\n",
      "Barrier solved model in 15 iterations and 0.01 seconds\n",
      "Optimal objective 5.69179883e+03\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Dict{String,Any} with 8 entries:\n",
       "  \"solve_time\"         => 0.00883913\n",
       "  \"optimizer\"          => \"Gurobi\"\n",
       "  \"termination_status\" => OPTIMAL\n",
       "  \"dual_status\"        => NO_SOLUTION\n",
       "  \"primal_status\"      => FEASIBLE_POINT\n",
       "  \"objective\"          => 5691.8\n",
       "  \"solution\"           => Dict{String,Any}(\"baseMVA\"=>100.0,\"branch\"=>Dict{Stri…\n",
       "  \"objective_lb\"       => -Inf"
      ]
     },
     "execution_count": 6,
     "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]: removing 3 cost terms from generator 4: Float64[]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 1: [792.0951, 0.0]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 3 cost terms from generator 5: Float64[]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 1 cost terms from generator 2: [2326.9494, 0.0]\u001b[39m\n",
      "\u001b[32m[info | PowerModels]: removing 3 cost terms from generator 3: Float64[]\u001b[39m\n",
      "Min 2326.9494 0_pg[2] + 792.0951 0_pg[1]\n",
      "Subject to\n",
      " 0_vi[1] = 0.0\n",
      " 0_p[(4, 2, 4)] + 0_p[(3, 2, 3)] + 0_p[(5, 2, 5)] + 0_p[(1, 2, 1)] - 0_pg[2] = -0.4266\n",
      " 0_q[(4, 2, 4)] + 0_q[(3, 2, 3)] + 0_q[(5, 2, 5)] + 0_q[(1, 2, 1)] - 0_qg[2] = -0.127\n",
      " 0_p[(11, 11, 6)] + 0_p[(18, 11, 10)] = -0.0688\n",
      " 0_q[(11, 11, 6)] + 0_q[(18, 11, 10)] = -0.018000000000000002\n",
      " 0_p[(14, 7, 8)] + 0_p[(15, 7, 9)] + 0_p[(8, 7, 4)] = 0.0\n",
      " 0_q[(14, 7, 8)] + 0_q[(15, 7, 9)] + 0_q[(8, 7, 4)] = 0.0\n",
      " 0_p[(18, 10, 11)] + 0_p[(16, 10, 9)] = -0.1769\n",
      " 0_q[(18, 10, 11)] + 0_q[(16, 10, 9)] = -0.057999999999999996\n",
      " 0_p[(14, 8, 7)] - 0_pg[5] = 0.0\n",
      " 0_q[(14, 8, 7)] - 0_qg[5] = 0.0\n",
      " 0_p[(11, 6, 11)] + 0_p[(13, 6, 13)] + 0_p[(12, 6, 12)] + 0_p[(10, 6, 5)] - 0_pg[4] = -0.2202\n",
      " 0_q[(11, 6, 11)] + 0_q[(13, 6, 13)] + 0_q[(12, 6, 12)] + 0_q[(10, 6, 5)] - 0_qg[4] = -0.075\n",
      " 0_p[(7, 4, 5)] + 0_p[(9, 4, 9)] + 0_p[(8, 4, 7)] + 0_p[(6, 4, 3)] + 0_p[(4, 4, 2)] = -0.478\n",
      " 0_q[(7, 4, 5)] + 0_q[(9, 4, 9)] + 0_q[(8, 4, 7)] + 0_q[(6, 4, 3)] + 0_q[(4, 4, 2)] = 0.039\n",
      " 0_p[(6, 3, 4)] + 0_p[(3, 3, 2)] - 0_pg[3] = -1.8517\n",
      " 0_q[(6, 3, 4)] + 0_q[(3, 3, 2)] - 0_qg[3] = -0.19\n",
      " 0_p[(10, 5, 6)] + 0_p[(2, 5, 1)] + 0_p[(7, 5, 4)] + 0_p[(5, 5, 2)] = -0.1494\n",
      " 0_q[(10, 5, 6)] + 0_q[(2, 5, 1)] + 0_q[(7, 5, 4)] + 0_q[(5, 5, 2)] = -0.016\n",
      " 0_p[(20, 13, 14)] + 0_p[(19, 13, 12)] + 0_p[(13, 13, 6)] = -0.26539999999999997\n",
      " 0_q[(20, 13, 14)] + 0_q[(19, 13, 12)] + 0_q[(13, 13, 6)] = -0.057999999999999996\n",
      " 0_p[(17, 14, 9)] + 0_p[(20, 14, 13)] = -0.2929\n",
      " 0_q[(17, 14, 9)] + 0_q[(20, 14, 13)] = -0.05\n",
      " 0_p[(19, 12, 13)] + 0_p[(12, 12, 6)] = -0.1199\n",
      " 0_q[(19, 12, 13)] + 0_q[(12, 12, 6)] = -0.016\n",
      " 0_p[(1, 1, 2)] + 0_p[(2, 1, 5)] - 0_pg[1] = 0.0\n",
      " 0_q[(1, 1, 2)] + 0_q[(2, 1, 5)] - 0_qg[1] = 0.0\n",
      " 0_p[(16, 9, 10)] + 0_p[(17, 9, 14)] + 0_p[(9, 9, 4)] + 0_p[(15, 9, 7)] = -0.5799\n",
      " -0.19 0_vr[9]² - 0.19 0_vi[9]² + 0_q[(16, 9, 10)] + 0_q[(17, 9, 14)] + 0_q[(9, 9, 4)] + 0_q[(15, 9, 7)] = -0.166\n",
      " -4.999131600798035 0_vr[1]² - 4.999131600798035 0_vi[1]² + 4.999131600798035 0_vr[1]*0_vr[2] + 4.999131600798035 0_vi[1]*0_vi[2] - 15.263086523179553 0_vi[1]*0_vr[2] + 15.263086523179553 0_vi[2]*0_vr[1] + 0_p[(1, 1, 2)] = 0.0\n",
      " -15.236686523179552 0_vr[1]² - 15.236686523179552 0_vi[1]² + 15.263086523179553 0_vr[1]*0_vr[2] + 15.263086523179553 0_vi[1]*0_vi[2] + 4.999131600798035 0_vi[1]*0_vr[2] - 4.999131600798035 0_vi[2]*0_vr[1] + 0_q[(1, 1, 2)] = 0.0\n",
      " -4.999131600798035 0_vr[2]² - 4.999131600798035 0_vi[2]² + 4.999131600798035 0_vr[1]*0_vr[2] + 4.999131600798035 0_vi[1]*0_vi[2] + 15.263086523179553 0_vi[1]*0_vr[2] - 15.263086523179553 0_vr[1]*0_vi[2] + 0_p[(1, 2, 1)] = 0.0\n",
      " -15.236686523179552 0_vr[2]² - 15.236686523179552 0_vi[2]² + 15.263086523179553 0_vr[1]*0_vr[2] + 15.263086523179553 0_vi[1]*0_vi[2] - 4.999131600798035 0_vi[1]*0_vr[2] + 4.999131600798035 0_vr[1]*0_vi[2] + 0_q[(1, 2, 1)] = 0.0\n",
      " -1.025897454970189 0_vr[1]² - 1.025897454970189 0_vi[1]² + 1.025897454970189 0_vr[1]*0_vr[5] + 1.025897454970189 0_vi[1]*0_vi[5] - 4.234983682334831 0_vi[1]*0_vr[5] + 4.234983682334831 0_vi[5]*0_vr[1] + 0_p[(2, 1, 5)] = 0.0\n",
      " -4.210383682334831 0_vr[1]² - 4.210383682334831 0_vi[1]² + 4.234983682334831 0_vr[1]*0_vr[5] + 4.234983682334831 0_vi[1]*0_vi[5] + 1.025897454970189 0_vi[1]*0_vr[5] - 1.025897454970189 0_vi[5]*0_vr[1] + 0_q[(2, 1, 5)] = 0.0\n",
      " -1.025897454970189 0_vr[5]² - 1.025897454970189 0_vi[5]² + 1.025897454970189 0_vr[1]*0_vr[5] + 1.025897454970189 0_vi[1]*0_vi[5] + 4.234983682334831 0_vi[1]*0_vr[5] - 4.234983682334831 0_vr[1]*0_vi[5] + 0_p[(2, 5, 1)] = 0.0\n",
      " -4.210383682334831 0_vr[5]² - 4.210383682334831 0_vi[5]² + 4.234983682334831 0_vr[1]*0_vr[5] + 4.234983682334831 0_vi[1]*0_vi[5] - 1.025897454970189 0_vi[1]*0_vr[5] + 1.025897454970189 0_vr[1]*0_vi[5] + 0_q[(2, 5, 1)] = 0.0\n",
      " -3.902049552447428 0_vr[9]² - 3.902049552447428 0_vi[9]² + 3.902049552447428 0_vr[9]*0_vr[10] + 3.902049552447428 0_vi[9]*0_vi[10] - 10.365394127060915 0_vi[9]*0_vr[10] + 10.365394127060915 0_vi[10]*0_vr[9] + 0_p[(16, 9, 10)] = 0.0\n",
      " -10.365394127060915 0_vr[9]² - 10.365394127060915 0_vi[9]² + 10.365394127060915 0_vr[9]*0_vr[10] + 10.365394127060915 0_vi[9]*0_vi[10] + 3.902049552447428 0_vi[9]*0_vr[10] - 3.902049552447428 0_vi[10]*0_vr[9] + 0_q[(16, 9, 10)] = 0.0\n",
      " -3.902049552447428 0_vr[10]² - 3.902049552447428 0_vi[10]² + 3.902049552447428 0_vr[9]*0_vr[10] + 3.902049552447428 0_vi[9]*0_vi[10] + 10.365394127060915 0_vi[9]*0_vr[10] - 10.365394127060915 0_vr[9]*0_vi[10] + 0_p[(16, 10, 9)] = 0.0\n",
      " -10.365394127060915 0_vr[10]² - 10.365394127060915 0_vi[10]² + 10.365394127060915 0_vr[9]*0_vr[10] + 10.365394127060915 0_vi[9]*0_vi[10] - 3.902049552447428 0_vi[9]*0_vr[10] + 3.902049552447428 0_vr[9]*0_vi[10] + 0_q[(16, 10, 9)] = 0.0\n",
      " -1.9550285631772604 0_vr[6]² - 1.9550285631772604 0_vi[6]² + 1.9550285631772604 0_vr[6]*0_vr[11] + 1.9550285631772604 0_vi[6]*0_vi[11] - 4.0940743442404415 0_vi[6]*0_vr[11] + 4.0940743442404415 0_vi[11]*0_vr[6] + 0_p[(11, 6, 11)] = 0.0\n",
      " -4.0940743442404415 0_vr[6]² - 4.0940743442404415 0_vi[6]² + 4.0940743442404415 0_vr[6]*0_vr[11] + 4.0940743442404415 0_vi[6]*0_vi[11] + 1.9550285631772604 0_vi[6]*0_vr[11] - 1.9550285631772604 0_vi[11]*0_vr[6] + 0_q[(11, 6, 11)] = 0.0\n",
      " -1.9550285631772604 0_vr[11]² - 1.9550285631772604 0_vi[11]² + 1.9550285631772604 0_vr[6]*0_vr[11] + 1.9550285631772604 0_vi[6]*0_vi[11] + 4.0940743442404415 0_vi[6]*0_vr[11] - 4.0940743442404415 0_vr[6]*0_vi[11] + 0_p[(11, 11, 6)] = 0.0\n",
      " -4.0940743442404415 0_vr[11]² - 4.0940743442404415 0_vi[11]² + 4.0940743442404415 0_vr[6]*0_vr[11] + 4.0940743442404415 0_vi[6]*0_vi[11] - 1.9550285631772604 0_vi[6]*0_vr[11] + 1.9550285631772604 0_vr[6]*0_vi[11] + 0_q[(11, 11, 6)] = 0.0\n",
      " -6.840980661495672 0_vr[4]² - 6.840980661495672 0_vi[4]² + 6.840980661495672 0_vr[4]*0_vr[5] + 6.840980661495672 0_vi[4]*0_vi[5] - 21.578553981691588 0_vi[4]*0_vr[5] + 21.578553981691588 0_vi[5]*0_vr[4] + 0_p[(7, 4, 5)] = 0.0\n",
      " -21.578553981691588 0_vr[4]² - 21.578553981691588 0_vi[4]² + 21.578553981691588 0_vr[4]*0_vr[5] + 21.578553981691588 0_vi[4]*0_vi[5] + 6.840980661495672 0_vi[4]*0_vr[5] - 6.840980661495672 0_vi[5]*0_vr[4] + 0_q[(7, 4, 5)] = 0.0\n",
      " -6.840980661495672 0_vr[5]² - 6.840980661495672 0_vi[5]² + 6.840980661495672 0_vr[4]*0_vr[5] + 6.840980661495672 0_vi[4]*0_vi[5] + 21.578553981691588 0_vi[4]*0_vr[5] - 21.578553981691588 0_vr[4]*0_vi[5] + 0_p[(7, 5, 4)] = 0.0\n",
      " -21.578553981691588 0_vr[5]² - 21.578553981691588 0_vi[5]² + 21.578553981691588 0_vr[4]*0_vr[5] + 21.578553981691588 0_vi[4]*0_vi[5] - 6.840980661495672 0_vi[4]*0_vr[5] + 6.840980661495672 0_vr[4]*0_vi[5] + 0_q[(7, 5, 4)] = 0.0\n",
      " -1.8554995578159006 0_vi[4]*0_vr[9] + 1.8554995578159006 0_vi[9]*0_vr[4] + 0_p[(9, 4, 9)] = 0.0\n",
      " -1.914860224784211 0_vr[4]² - 1.914860224784211 0_vi[4]² + 1.8554995578159006 0_vr[4]*0_vr[9] + 1.8554995578159006 0_vi[4]*0_vi[9] + 0_q[(9, 4, 9)] = 0.0\n",
      " 1.8554995578159006 0_vi[4]*0_vr[9] - 1.8554995578159006 0_vr[4]*0_vi[9] + 0_p[(9, 9, 4)] = 0.0\n",
      " -1.7979790715236075 0_vr[9]² - 1.7979790715236075 0_vi[9]² + 1.8554995578159006 0_vr[4]*0_vr[9] + 1.8554995578159006 0_vi[4]*0_vi[9] + 0_q[(9, 9, 4)] = 0.0\n",
      " -4.257445335253384 0_vi[5]*0_vr[6] + 4.257445335253384 0_vi[6]*0_vr[5] + 0_p[(10, 5, 6)] = 0.0\n",
      " -4.568074394048695 0_vr[5]² - 4.568074394048695 0_vi[5]² + 4.257445335253384 0_vr[5]*0_vr[6] + 4.257445335253384 0_vi[5]*0_vi[6] + 0_q[(10, 5, 6)] = 0.0\n",
      " 4.257445335253384 0_vi[5]*0_vr[6] - 4.257445335253384 0_vr[5]*0_vi[6] + 0_p[(10, 6, 5)] = 0.0\n",
      " -3.967939052456154 0_vr[6]² - 3.967939052456154 0_vi[6]² + 4.257445335253384 0_vr[5]*0_vr[6] + 4.257445335253384 0_vi[5]*0_vi[6] + 0_q[(10, 6, 5)] = 0.0\n",
      " -2.4890245868219187 0_vr[12]² - 2.4890245868219187 0_vi[12]² + 2.4890245868219187 0_vr[12]*0_vr[13] + 2.4890245868219187 0_vi[12]*0_vi[13] - 2.251974626172212 0_vi[12]*0_vr[13] + 2.251974626172212 0_vi[13]*0_vr[12] + 0_p[(19, 12, 13)] = 0.0\n",
      " -2.251974626172212 0_vr[12]² - 2.251974626172212 0_vi[12]² + 2.251974626172212 0_vr[12]*0_vr[13] + 2.251974626172212 0_vi[12]*0_vi[13] + 2.4890245868219187 0_vi[12]*0_vr[13] - 2.4890245868219187 0_vi[13]*0_vr[12] + 0_q[(19, 12, 13)] = 0.0\n",
      " -2.4890245868219187 0_vr[13]² - 2.4890245868219187 0_vi[13]² + 2.4890245868219187 0_vr[12]*0_vr[13] + 2.4890245868219187 0_vi[12]*0_vi[13] + 2.251974626172212 0_vi[12]*0_vr[13] - 2.251974626172212 0_vr[12]*0_vi[13] + 0_p[(19, 13, 12)] = 0.0\n",
      " -2.251974626172212 0_vr[13]² - 2.251974626172212 0_vi[13]² + 2.251974626172212 0_vr[12]*0_vr[13] + 2.251974626172212 0_vi[12]*0_vi[13] - 2.4890245868219187 0_vi[12]*0_vr[13] + 2.4890245868219187 0_vr[12]*0_vi[13] + 0_q[(19, 13, 12)] = 0.0\n",
      " -1.4240054870199312 0_vr[9]² - 1.4240054870199312 0_vi[9]² + 1.4240054870199312 0_vr[9]*0_vr[14] + 1.4240054870199312 0_vi[9]*0_vi[14] - 3.0290504569306034 0_vi[9]*0_vr[14] + 3.0290504569306034 0_vi[14]*0_vr[9] + 0_p[(17, 9, 14)] = 0.0\n",
      " -3.0290504569306034 0_vr[9]² - 3.0290504569306034 0_vi[9]² + 3.0290504569306034 0_vr[9]*0_vr[14] + 3.0290504569306034 0_vi[9]*0_vi[14] + 1.4240054870199312 0_vi[9]*0_vr[14] - 1.4240054870199312 0_vi[14]*0_vr[9] + 0_q[(17, 9, 14)] = 0.0\n",
      " -1.4240054870199312 0_vr[14]² - 1.4240054870199312 0_vi[14]² + 1.4240054870199312 0_vr[9]*0_vr[14] + 1.4240054870199312 0_vi[9]*0_vi[14] + 3.0290504569306034 0_vi[9]*0_vr[14] - 3.0290504569306034 0_vr[9]*0_vi[14] + 0_p[(17, 14, 9)] = 0.0\n",
      " -3.0290504569306034 0_vr[14]² - 3.0290504569306034 0_vi[14]² + 3.0290504569306034 0_vr[9]*0_vr[14] + 3.0290504569306034 0_vi[9]*0_vi[14] - 1.4240054870199312 0_vi[9]*0_vr[14] + 1.4240054870199312 0_vr[9]*0_vi[14] + 0_q[(17, 14, 9)] = 0.0\n",
      " -4.889512660317341 0_vi[4]*0_vr[7] + 4.889512660317341 0_vi[7]*0_vr[4] + 0_p[(8, 4, 7)] = 0.0\n",
      " -4.999501697665993 0_vr[4]² - 4.999501697665993 0_vi[4]² + 4.889512660317341 0_vr[4]*0_vr[7] + 4.889512660317341 0_vi[4]*0_vi[7] + 0_q[(8, 4, 7)] = 0.0\n",
      " 4.889512660317341 0_vi[4]*0_vr[7] - 4.889512660317341 0_vr[4]*0_vi[7] + 0_p[(8, 7, 4)] = 0.0\n",
      " -4.781943381790359 0_vr[7]² - 4.781943381790359 0_vi[7]² + 4.889512660317341 0_vr[4]*0_vr[7] + 4.889512660317341 0_vi[4]*0_vi[7] + 0_q[(8, 7, 4)] = 0.0\n",
      " -1.9859757099255606 0_vr[3]² - 1.9859757099255606 0_vi[3]² + 1.9859757099255606 0_vr[3]*0_vr[4] + 1.9859757099255606 0_vi[3]*0_vi[4] - 5.0688169775939205 0_vi[3]*0_vr[4] + 5.0688169775939205 0_vi[4]*0_vr[3] + 0_p[(6, 3, 4)] = 0.0\n",
      " -5.06241697759392 0_vr[3]² - 5.06241697759392 0_vi[3]² + 5.0688169775939205 0_vr[3]*0_vr[4] + 5.0688169775939205 0_vi[3]*0_vi[4] + 1.9859757099255606 0_vi[3]*0_vr[4] - 1.9859757099255606 0_vi[4]*0_vr[3] + 0_q[(6, 3, 4)] = 0.0\n",
      " -1.9859757099255606 0_vr[4]² - 1.9859757099255606 0_vi[4]² + 1.9859757099255606 0_vr[3]*0_vr[4] + 1.9859757099255606 0_vi[3]*0_vi[4] + 5.0688169775939205 0_vi[3]*0_vr[4] - 5.0688169775939205 0_vr[3]*0_vi[4] + 0_p[(6, 4, 3)] = 0.0\n",
      " -5.06241697759392 0_vr[4]² - 5.06241697759392 0_vi[4]² + 5.0688169775939205 0_vr[3]*0_vr[4] + 5.0688169775939205 0_vi[3]*0_vi[4] - 1.9859757099255606 0_vi[3]*0_vr[4] + 1.9859757099255606 0_vr[3]*0_vi[4] + 0_q[(6, 4, 3)] = 0.0\n",
      " -1.686033150614943 0_vr[2]² - 1.686033150614943 0_vi[2]² + 1.686033150614943 0_vr[2]*0_vr[4] + 1.686033150614943 0_vi[2]*0_vi[4] - 5.115838325872083 0_vi[2]*0_vr[4] + 5.115838325872083 0_vi[4]*0_vr[2] + 0_p[(4, 2, 4)] = 0.0\n",
      " -5.098838325872083 0_vr[2]² - 5.098838325872083 0_vi[2]² + 5.115838325872083 0_vr[2]*0_vr[4] + 5.115838325872083 0_vi[2]*0_vi[4] + 1.686033150614943 0_vi[2]*0_vr[4] - 1.686033150614943 0_vi[4]*0_vr[2] + 0_q[(4, 2, 4)] = 0.0\n",
      " -1.686033150614943 0_vr[4]² - 1.686033150614943 0_vi[4]² + 1.686033150614943 0_vr[2]*0_vr[4] + 1.686033150614943 0_vi[2]*0_vi[4] + 5.115838325872083 0_vi[2]*0_vr[4] - 5.115838325872083 0_vr[2]*0_vi[4] + 0_p[(4, 4, 2)] = 0.0\n",
      " -5.098838325872083 0_vr[4]² - 5.098838325872083 0_vi[4]² + 5.115838325872083 0_vr[2]*0_vr[4] + 5.115838325872083 0_vi[2]*0_vi[4] - 1.686033150614943 0_vi[2]*0_vr[4] + 1.686033150614943 0_vr[2]*0_vi[4] + 0_q[(4, 4, 2)] = 0.0\n",
      " -1.1350191923073958 0_vr[2]² - 1.1350191923073958 0_vi[2]² + 1.1350191923073958 0_vr[2]*0_vr[3] + 1.1350191923073958 0_vi[2]*0_vi[3] - 4.781863151757718 0_vi[2]*0_vr[3] + 4.781863151757718 0_vi[3]*0_vr[2] + 0_p[(3, 2, 3)] = 0.0\n",
      " -4.759963151757718 0_vr[2]² - 4.759963151757718 0_vi[2]² + 4.781863151757718 0_vr[2]*0_vr[3] + 4.781863151757718 0_vi[2]*0_vi[3] + 1.1350191923073958 0_vi[2]*0_vr[3] - 1.1350191923073958 0_vi[3]*0_vr[2] + 0_q[(3, 2, 3)] = 0.0\n",
      " -1.1350191923073958 0_vr[3]² - 1.1350191923073958 0_vi[3]² + 1.1350191923073958 0_vr[2]*0_vr[3] + 1.1350191923073958 0_vi[2]*0_vi[3] + 4.781863151757718 0_vi[2]*0_vr[3] - 4.781863151757718 0_vr[2]*0_vi[3] + 0_p[(3, 3, 2)] = 0.0\n",
      " -4.759963151757718 0_vr[3]² - 4.759963151757718 0_vi[3]² + 4.781863151757718 0_vr[2]*0_vr[3] + 4.781863151757718 0_vi[2]*0_vi[3] - 1.1350191923073958 0_vi[2]*0_vr[3] + 1.1350191923073958 0_vr[2]*0_vi[3] + 0_q[(3, 3, 2)] = 0.0\n",
      " -1.7011396670944048 0_vr[2]² - 1.7011396670944048 0_vi[2]² + 1.7011396670944048 0_vr[2]*0_vr[5] + 1.7011396670944048 0_vi[2]*0_vi[5] - 5.193927397969713 0_vi[2]*0_vr[5] + 5.193927397969713 0_vi[5]*0_vr[2] + 0_p[(5, 2, 5)] = 0.0\n",
      " -5.176627397969713 0_vr[2]² - 5.176627397969713 0_vi[2]² + 5.193927397969713 0_vr[2]*0_vr[5] + 5.193927397969713 0_vi[2]*0_vi[5] + 1.7011396670944048 0_vi[2]*0_vr[5] - 1.7011396670944048 0_vi[5]*0_vr[2] + 0_q[(5, 2, 5)] = 0.0\n",
      " -1.7011396670944048 0_vr[5]² - 1.7011396670944048 0_vi[5]² + 1.7011396670944048 0_vr[2]*0_vr[5] + 1.7011396670944048 0_vi[2]*0_vi[5] + 5.193927397969713 0_vi[2]*0_vr[5] - 5.193927397969713 0_vr[2]*0_vi[5] + 0_p[(5, 5, 2)] = 0.0\n",
      " -5.176627397969713 0_vr[5]² - 5.176627397969713 0_vi[5]² + 5.193927397969713 0_vr[2]*0_vr[5] + 5.193927397969713 0_vi[2]*0_vi[5] - 1.7011396670944048 0_vi[2]*0_vr[5] + 1.7011396670944048 0_vr[2]*0_vi[5] + 0_q[(5, 5, 2)] = 0.0\n",
      " -1.1369941578063267 0_vr[13]² - 1.1369941578063267 0_vi[13]² + 1.1369941578063267 0_vr[13]*0_vr[14] + 1.1369941578063267 0_vi[13]*0_vi[14] - 2.314963475105352 0_vi[13]*0_vr[14] + 2.314963475105352 0_vi[14]*0_vr[13] + 0_p[(20, 13, 14)] = 0.0\n",
      " -2.314963475105352 0_vr[13]² - 2.314963475105352 0_vi[13]² + 2.314963475105352 0_vr[13]*0_vr[14] + 2.314963475105352 0_vi[13]*0_vi[14] + 1.1369941578063267 0_vi[13]*0_vr[14] - 1.1369941578063267 0_vi[14]*0_vr[13] + 0_q[(20, 13, 14)] = 0.0\n",
      " -1.1369941578063267 0_vr[14]² - 1.1369941578063267 0_vi[14]² + 1.1369941578063267 0_vr[13]*0_vr[14] + 1.1369941578063267 0_vi[13]*0_vi[14] + 2.314963475105352 0_vi[13]*0_vr[14] - 2.314963475105352 0_vr[13]*0_vi[14] + 0_p[(20, 14, 13)] = 0.0\n",
      " -2.314963475105352 0_vr[14]² - 2.314963475105352 0_vi[14]² + 2.314963475105352 0_vr[13]*0_vr[14] + 2.314963475105352 0_vi[13]*0_vi[14] - 1.1369941578063267 0_vi[13]*0_vr[14] + 1.1369941578063267 0_vr[13]*0_vi[14] + 0_q[(20, 14, 13)] = 0.0\n",
      " -3.0989274038379877 0_vr[6]² - 3.0989274038379877 0_vi[6]² + 3.0989274038379877 0_vr[6]*0_vr[13] + 3.0989274038379877 0_vi[6]*0_vi[13] - 6.102755448193116 0_vi[6]*0_vr[13] + 6.102755448193116 0_vi[13]*0_vr[6] + 0_p[(13, 6, 13)] = 0.0\n",
      " -6.102755448193116 0_vr[6]² - 6.102755448193116 0_vi[6]² + 6.102755448193116 0_vr[6]*0_vr[13] + 6.102755448193116 0_vi[6]*0_vi[13] + 3.0989274038379877 0_vi[6]*0_vr[13] - 3.0989274038379877 0_vi[13]*0_vr[6] + 0_q[(13, 6, 13)] = 0.0\n",
      " -3.0989274038379877 0_vr[13]² - 3.0989274038379877 0_vi[13]² + 3.0989274038379877 0_vr[6]*0_vr[13] + 3.0989274038379877 0_vi[6]*0_vi[13] + 6.102755448193116 0_vi[6]*0_vr[13] - 6.102755448193116 0_vr[6]*0_vi[13] + 0_p[(13, 13, 6)] = 0.0\n",
      " -6.102755448193116 0_vr[13]² - 6.102755448193116 0_vi[13]² + 6.102755448193116 0_vr[6]*0_vr[13] + 6.102755448193116 0_vi[6]*0_vi[13] - 3.0989274038379877 0_vi[6]*0_vr[13] + 3.0989274038379877 0_vr[6]*0_vi[13] + 0_q[(13, 13, 6)] = 0.0\n",
      " -5.676979846721544 0_vi[7]*0_vr[8] + 5.676979846721544 0_vi[8]*0_vr[7] + 0_p[(14, 7, 8)] = 0.0\n",
      " -5.676979846721544 0_vr[7]² - 5.676979846721544 0_vi[7]² + 5.676979846721544 0_vr[7]*0_vr[8] + 5.676979846721544 0_vi[7]*0_vi[8] + 0_q[(14, 7, 8)] = 0.0\n",
      " 5.676979846721544 0_vi[7]*0_vr[8] - 5.676979846721544 0_vr[7]*0_vi[8] + 0_p[(14, 8, 7)] = 0.0\n",
      " -5.676979846721544 0_vr[8]² - 5.676979846721544 0_vi[8]² + 5.676979846721544 0_vr[7]*0_vr[8] + 5.676979846721544 0_vi[7]*0_vi[8] + 0_q[(14, 8, 7)] = 0.0\n",
      " -9.09008271975275 0_vi[7]*0_vr[9] + 9.09008271975275 0_vi[9]*0_vr[7] + 0_p[(15, 7, 9)] = 0.0\n",
      " -9.09008271975275 0_vr[7]² - 9.09008271975275 0_vi[7]² + 9.09008271975275 0_vr[7]*0_vr[9] + 9.09008271975275 0_vi[7]*0_vi[9] + 0_q[(15, 7, 9)] = 0.0\n",
      " 9.09008271975275 0_vi[7]*0_vr[9] - 9.09008271975275 0_vr[7]*0_vi[9] + 0_p[(15, 9, 7)] = 0.0\n",
      " -9.09008271975275 0_vr[9]² - 9.09008271975275 0_vi[9]² + 9.09008271975275 0_vr[7]*0_vr[9] + 9.09008271975275 0_vi[7]*0_vi[9] + 0_q[(15, 9, 7)] = 0.0\n",
      " -1.525967440450974 0_vr[6]² - 1.525967440450974 0_vi[6]² + 1.525967440450974 0_vr[6]*0_vr[12] + 1.525967440450974 0_vi[6]*0_vi[12] - 3.1759639650294003 0_vi[6]*0_vr[12] + 3.1759639650294003 0_vi[12]*0_vr[6] + 0_p[(12, 6, 12)] = 0.0\n",
      " -3.1759639650294003 0_vr[6]² - 3.1759639650294003 0_vi[6]² + 3.1759639650294003 0_vr[6]*0_vr[12] + 3.1759639650294003 0_vi[6]*0_vi[12] + 1.525967440450974 0_vi[6]*0_vr[12] - 1.525967440450974 0_vi[12]*0_vr[6] + 0_q[(12, 6, 12)] = 0.0\n",
      " -1.525967440450974 0_vr[12]² - 1.525967440450974 0_vi[12]² + 1.525967440450974 0_vr[6]*0_vr[12] + 1.525967440450974 0_vi[6]*0_vi[12] + 3.1759639650294003 0_vi[6]*0_vr[12] - 3.1759639650294003 0_vr[6]*0_vi[12] + 0_p[(12, 12, 6)] = 0.0\n",
      " -3.1759639650294003 0_vr[12]² - 3.1759639650294003 0_vi[12]² + 3.1759639650294003 0_vr[6]*0_vr[12] + 3.1759639650294003 0_vi[6]*0_vi[12] - 1.525967440450974 0_vi[6]*0_vr[12] + 1.525967440450974 0_vr[6]*0_vi[12] + 0_q[(12, 12, 6)] = 0.0\n",
      " -1.8808847537003996 0_vr[10]² - 1.8808847537003996 0_vi[10]² + 1.8808847537003996 0_vr[10]*0_vr[11] + 1.8808847537003996 0_vi[10]*0_vi[11] - 4.402943749460521 0_vi[10]*0_vr[11] + 4.402943749460521 0_vi[11]*0_vr[10] + 0_p[(18, 10, 11)] = 0.0\n",
      " -4.402943749460521 0_vr[10]² - 4.402943749460521 0_vi[10]² + 4.402943749460521 0_vr[10]*0_vr[11] + 4.402943749460521 0_vi[10]*0_vi[11] + 1.8808847537003996 0_vi[10]*0_vr[11] - 1.8808847537003996 0_vi[11]*0_vr[10] + 0_q[(18, 10, 11)] = 0.0\n",
      " -1.8808847537003996 0_vr[11]² - 1.8808847537003996 0_vi[11]² + 1.8808847537003996 0_vr[10]*0_vr[11] + 1.8808847537003996 0_vi[10]*0_vi[11] + 4.402943749460521 0_vi[10]*0_vr[11] - 4.402943749460521 0_vr[10]*0_vi[11] + 0_p[(18, 11, 10)] = 0.0\n",
      " -4.402943749460521 0_vr[11]² - 4.402943749460521 0_vi[11]² + 4.402943749460521 0_vr[10]*0_vr[11] + 4.402943749460521 0_vi[10]*0_vi[11] - 1.8808847537003996 0_vi[10]*0_vr[11] + 1.8808847537003996 0_vr[10]*0_vi[11] + 0_q[(18, 11, 10)] = 0.0\n",
      " -0_vr[2]² - 0_vi[2]² ≥ -1.1236000000000002\n",
      " -0_vr[11]² - 0_vi[11]² ≥ -1.1236000000000002\n",
      " -0_vr[7]² - 0_vi[7]² ≥ -1.1236000000000002\n",
      " -0_vr[9]² - 0_vi[9]² ≥ -1.1236000000000002\n",
      " -0_vr[10]² - 0_vi[10]² ≥ -1.1236000000000002\n",
      " -0_vr[8]² - 0_vi[8]² ≥ -1.1236000000000002\n",
      " -0_vr[6]² - 0_vi[6]² ≥ -1.1236000000000002\n",
      " -0_vr[4]² - 0_vi[4]² ≥ -1.1236000000000002\n",
      " -0_vr[3]² - 0_vi[3]² ≥ -1.1236000000000002\n",
      " -0_vr[5]² - 0_vi[5]² ≥ -1.1236000000000002\n",
      " -0_vr[13]² - 0_vi[13]² ≥ -1.1236000000000002\n",
      " -0_vr[14]² - 0_vi[14]² ≥ -1.1236000000000002\n",
      " -0_vr[12]² - 0_vi[12]² ≥ -1.1236000000000002\n",
      " -0_vr[1]² - 0_vi[1]² ≥ -1.1236000000000002\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[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[9]*0_vr[10] - 0_vi[10]*0_vr[9] + 0.5773502691896257 0_vr[9]*0_vr[10] + 0.5773502691896257 0_vi[9]*0_vi[10] ≥ 0.0\n",
      " 0_vi[6]*0_vr[11] - 0_vi[11]*0_vr[6] + 0.5773502691896257 0_vr[6]*0_vr[11] + 0.5773502691896257 0_vi[6]*0_vi[11] ≥ 0.0\n",
      " 0_vi[4]*0_vr[5] - 0_vi[5]*0_vr[4] + 0.5773502691896257 0_vr[4]*0_vr[5] + 0.5773502691896257 0_vi[4]*0_vi[5] ≥ 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[5]*0_vr[6] - 0_vi[6]*0_vr[5] + 0.5773502691896257 0_vr[5]*0_vr[6] + 0.5773502691896257 0_vi[5]*0_vi[6] ≥ 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[9]*0_vr[14] - 0_vi[14]*0_vr[9] + 0.5773502691896257 0_vr[9]*0_vr[14] + 0.5773502691896257 0_vi[9]*0_vi[14] ≥ 0.0\n",
      " 0_vi[4]*0_vr[7] - 0_vi[7]*0_vr[4] + 0.5773502691896257 0_vr[4]*0_vr[7] + 0.5773502691896257 0_vi[4]*0_vi[7] ≥ 0.0\n",
      " 0_vi[3]*0_vr[4] - 0_vi[4]*0_vr[3] + 0.5773502691896257 0_vr[3]*0_vr[4] + 0.5773502691896257 0_vi[3]*0_vi[4] ≥ 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[2]*0_vr[3] - 0_vi[3]*0_vr[2] + 0.5773502691896257 0_vr[2]*0_vr[3] + 0.5773502691896257 0_vi[2]*0_vi[3] ≥ 0.0\n",
      " 0_vi[2]*0_vr[5] - 0_vi[5]*0_vr[2] + 0.5773502691896257 0_vr[2]*0_vr[5] + 0.5773502691896257 0_vi[2]*0_vi[5] ≥ 0.0\n",
      " 0_vi[13]*0_vr[14] - 0_vi[14]*0_vr[13] + 0.5773502691896257 0_vr[13]*0_vr[14] + 0.5773502691896257 0_vi[13]*0_vi[14] ≥ 0.0\n",
      " 0_vi[6]*0_vr[13] - 0_vi[13]*0_vr[6] + 0.5773502691896257 0_vr[6]*0_vr[13] + 0.5773502691896257 0_vi[6]*0_vi[13] ≥ 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[7]*0_vr[9] - 0_vi[9]*0_vr[7] + 0.5773502691896257 0_vr[7]*0_vr[9] + 0.5773502691896257 0_vi[7]*0_vi[9] ≥ 0.0\n",
      " 0_vi[6]*0_vr[12] - 0_vi[12]*0_vr[6] + 0.5773502691896257 0_vr[6]*0_vr[12] + 0.5773502691896257 0_vi[6]*0_vi[12] ≥ 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_vr[2]² - 0_vi[2]² ≤ -0.8835999999999999\n",
      " -0_vr[11]² - 0_vi[11]² ≤ -0.8835999999999999\n",
      " -0_vr[7]² - 0_vi[7]² ≤ -0.8835999999999999\n",
      " -0_vr[9]² - 0_vi[9]² ≤ -0.8835999999999999\n",
      " -0_vr[10]² - 0_vi[10]² ≤ -0.8835999999999999\n",
      " -0_vr[8]² - 0_vi[8]² ≤ -0.8835999999999999\n",
      " -0_vr[6]² - 0_vi[6]² ≤ -0.8835999999999999\n",
      " -0_vr[4]² - 0_vi[4]² ≤ -0.8835999999999999\n",
      " -0_vr[3]² - 0_vi[3]² ≤ -0.8835999999999999\n",
      " -0_vr[5]² - 0_vi[5]² ≤ -0.8835999999999999\n",
      " -0_vr[13]² - 0_vi[13]² ≤ -0.8835999999999999\n",
      " -0_vr[14]² - 0_vi[14]² ≤ -0.8835999999999999\n",
      " -0_vr[12]² - 0_vi[12]² ≤ -0.8835999999999999\n",
      " -0_vr[1]² - 0_vi[1]² ≤ -0.8835999999999999\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 ≤ 22.278399999999998\n",
      " 0_p[(1, 2, 1)]² + 0_q[(1, 2, 1)]² ≤ 22.278399999999998\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[(2, 1, 5)]² + 0_q[(2, 1, 5)]² ≤ 1.6384\n",
      " 0_p[(2, 5, 1)]² + 0_q[(2, 5, 1)]² ≤ 1.6384\n",
      " 0_vi[9]*0_vr[10] - 0_vi[10]*0_vr[9] - 0.5773502691896257 0_vr[9]*0_vr[10] - 0.5773502691896257 0_vi[9]*0_vi[10] ≤ 0.0\n",
      " 0_p[(16, 9, 10)]² + 0_q[(16, 9, 10)]² ≤ 10.5625\n",
      " 0_p[(16, 10, 9)]² + 0_q[(16, 10, 9)]² ≤ 10.5625\n",
      " 0_vi[6]*0_vr[11] - 0_vi[11]*0_vr[6] - 0.5773502691896257 0_vr[6]*0_vr[11] - 0.5773502691896257 0_vi[6]*0_vi[11] ≤ 0.0\n",
      " 0_p[(11, 6, 11)]² + 0_q[(11, 6, 11)]² ≤ 1.7956000000000003\n",
      " 0_p[(11, 11, 6)]² + 0_q[(11, 11, 6)]² ≤ 1.7956000000000003\n",
      " 0_vi[4]*0_vr[5] - 0_vi[5]*0_vr[4] - 0.5773502691896257 0_vr[4]*0_vr[5] - 0.5773502691896257 0_vi[4]*0_vi[5] ≤ 0.0\n",
      " 0_p[(7, 4, 5)]² + 0_q[(7, 4, 5)]² ≤ 44.0896\n",
      " 0_p[(7, 5, 4)]² + 0_q[(7, 5, 4)]² ≤ 44.0896\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[(9, 4, 9)]² + 0_q[(9, 4, 9)]² ≤ 0.28090000000000004\n",
      " 0_p[(9, 9, 4)]² + 0_q[(9, 9, 4)]² ≤ 0.28090000000000004\n",
      " 0_vi[5]*0_vr[6] - 0_vi[6]*0_vr[5] - 0.5773502691896257 0_vr[5]*0_vr[6] - 0.5773502691896257 0_vi[5]*0_vi[6] ≤ 0.0\n",
      " 0_p[(10, 5, 6)]² + 0_q[(10, 5, 6)]² ≤ 1.3688999999999998\n",
      " 0_p[(10, 6, 5)]² + 0_q[(10, 6, 5)]² ≤ 1.3688999999999998\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[(19, 12, 13)]² + 0_q[(19, 12, 13)]² ≤ 0.9801\n",
      " 0_p[(19, 13, 12)]² + 0_q[(19, 13, 12)]² ≤ 0.9801\n",
      " 0_vi[9]*0_vr[14] - 0_vi[14]*0_vr[9] - 0.5773502691896257 0_vr[9]*0_vr[14] - 0.5773502691896257 0_vi[9]*0_vi[14] ≤ 0.0\n",
      " 0_p[(17, 9, 14)]² + 0_q[(17, 9, 14)]² ≤ 0.9801\n",
      " 0_p[(17, 14, 9)]² + 0_q[(17, 14, 9)]² ≤ 0.9801\n",
      " 0_vi[4]*0_vr[7] - 0_vi[7]*0_vr[4] - 0.5773502691896257 0_vr[4]*0_vr[7] - 0.5773502691896257 0_vi[4]*0_vi[7] ≤ 0.0\n",
      " 0_p[(8, 4, 7)]² + 0_q[(8, 4, 7)]² ≤ 1.9880999999999998\n",
      " 0_p[(8, 7, 4)]² + 0_q[(8, 7, 4)]² ≤ 1.9880999999999998\n",
      " 0_vi[3]*0_vr[4] - 0_vi[4]*0_vr[3] - 0.5773502691896257 0_vr[3]*0_vr[4] - 0.5773502691896257 0_vi[3]*0_vi[4] ≤ 0.0\n",
      " 0_p[(6, 3, 4)]² + 0_q[(6, 3, 4)]² ≤ 2.5600000000000005\n",
      " 0_p[(6, 4, 3)]² + 0_q[(6, 4, 3)]² ≤ 2.5600000000000005\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 ≤ 2.4964000000000004\n",
      " 0_p[(4, 4, 2)]² + 0_q[(4, 4, 2)]² ≤ 2.4964000000000004\n",
      " 0_vi[2]*0_vr[3] - 0_vi[3]*0_vr[2] - 0.5773502691896257 0_vr[2]*0_vr[3] - 0.5773502691896257 0_vi[2]*0_vi[3] ≤ 0.0\n",
      " 0_p[(3, 2, 3)]² + 0_q[(3, 2, 3)]² ≤ 2.1025\n",
      " 0_p[(3, 3, 2)]² + 0_q[(3, 3, 2)]² ≤ 2.1025\n",
      " 0_vi[2]*0_vr[5] - 0_vi[5]*0_vr[2] - 0.5773502691896257 0_vr[2]*0_vr[5] - 0.5773502691896257 0_vi[2]*0_vi[5] ≤ 0.0\n",
      " 0_p[(5, 2, 5)]² + 0_q[(5, 2, 5)]² ≤ 2.5921000000000003\n",
      " 0_p[(5, 5, 2)]² + 0_q[(5, 5, 2)]² ≤ 2.5921000000000003\n",
      " 0_vi[13]*0_vr[14] - 0_vi[14]*0_vr[13] - 0.5773502691896257 0_vr[13]*0_vr[14] - 0.5773502691896257 0_vi[13]*0_vi[14] ≤ 0.0\n",
      " 0_p[(20, 13, 14)]² + 0_q[(20, 13, 14)]² ≤ 0.5776\n",
      " 0_p[(20, 14, 13)]² + 0_q[(20, 14, 13)]² ≤ 0.5776\n",
      " 0_vi[6]*0_vr[13] - 0_vi[13]*0_vr[6] - 0.5773502691896257 0_vr[6]*0_vr[13] - 0.5773502691896257 0_vi[6]*0_vi[13] ≤ 0.0\n",
      " 0_p[(13, 6, 13)]² + 0_q[(13, 6, 13)]² ≤ 4.040099999999999\n",
      " 0_p[(13, 13, 6)]² + 0_q[(13, 13, 6)]² ≤ 4.040099999999999\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[(14, 7, 8)]² + 0_q[(14, 7, 8)]² ≤ 2.7889\n",
      " 0_p[(14, 8, 7)]² + 0_q[(14, 8, 7)]² ≤ 2.7889\n",
      " 0_vi[7]*0_vr[9] - 0_vi[9]*0_vr[7] - 0.5773502691896257 0_vr[7]*0_vr[9] - 0.5773502691896257 0_vi[7]*0_vi[9] ≤ 0.0\n",
      " 0_p[(15, 7, 9)]² + 0_q[(15, 7, 9)]² ≤ 7.1289\n",
      " 0_p[(15, 9, 7)]² + 0_q[(15, 9, 7)]² ≤ 7.1289\n",
      " 0_vi[6]*0_vr[12] - 0_vi[12]*0_vr[6] - 0.5773502691896257 0_vr[6]*0_vr[12] - 0.5773502691896257 0_vi[6]*0_vi[12] ≤ 0.0\n",
      " 0_p[(12, 6, 12)]² + 0_q[(12, 6, 12)]² ≤ 1.0816000000000001\n",
      " 0_p[(12, 12, 6)]² + 0_q[(12, 12, 6)]² ≤ 1.0816000000000001\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[(18, 10, 11)]² + 0_q[(18, 10, 11)]² ≤ 1.9880999999999998\n",
      " 0_p[(18, 11, 10)]² + 0_q[(18, 11, 10)]² ≤ 1.9880999999999998\n",
      " 0_vr[2] ≥ -1.06\n",
      " 0_vr[11] ≥ -1.06\n",
      " 0_vr[7] ≥ -1.06\n",
      " 0_vr[9] ≥ -1.06\n",
      " 0_vr[10] ≥ -1.06\n",
      " 0_vr[8] ≥ -1.06\n",
      " 0_vr[6] ≥ -1.06\n",
      " 0_vr[4] ≥ -1.06\n",
      " 0_vr[3] ≥ -1.06\n",
      " 0_vr[5] ≥ -1.06\n",
      " 0_vr[13] ≥ -1.06\n",
      " 0_vr[14] ≥ -1.06\n",
      " 0_vr[12] ≥ -1.06\n",
      " 0_vr[1] ≥ -1.06\n",
      " 0_vi[2] ≥ -1.06\n",
      " 0_vi[11] ≥ -1.06\n",
      " 0_vi[7] ≥ -1.06\n",
      " 0_vi[9] ≥ -1.06\n",
      " 0_vi[10] ≥ -1.06\n",
      " 0_vi[8] ≥ -1.06\n",
      " 0_vi[6] ≥ -1.06\n",
      " 0_vi[4] ≥ -1.06\n",
      " 0_vi[3] ≥ -1.06\n",
      " 0_vi[5] ≥ -1.06\n",
      " 0_vi[13] ≥ -1.06\n",
      " 0_vi[14] ≥ -1.06\n",
      " 0_vi[12] ≥ -1.06\n",
      " 0_vi[1] ≥ -1.06\n",
      " 0_pg[4] ≥ 0.0\n",
      " 0_pg[2] ≥ 0.0\n",
      " 0_pg[3] ≥ 0.0\n",
      " 0_pg[5] ≥ 0.0\n",
      " 0_pg[1] ≥ 0.0\n",
      " 0_qg[4] ≥ -0.564\n",
      " 0_qg[2] ≥ -1.15\n",
      " 0_qg[3] ≥ -0.72\n",
      " 0_qg[5] ≥ -0.3\n",
      " 0_qg[1] ≥ -1.99\n",
      " 0_p[(1, 1, 2)] ≥ -4.72\n",
      " 0_p[(2, 1, 5)] ≥ -1.28\n",
      " 0_p[(16, 9, 10)] ≥ -3.25\n",
      " 0_p[(11, 6, 11)] ≥ -1.34\n",
      " 0_p[(7, 4, 5)] ≥ -6.64\n",
      " 0_p[(9, 4, 9)] ≥ -0.53\n",
      " 0_p[(10, 5, 6)] ≥ -1.17\n",
      " 0_p[(19, 12, 13)] ≥ -0.99\n",
      " 0_p[(17, 9, 14)] ≥ -0.99\n",
      " 0_p[(8, 4, 7)] ≥ -1.41\n",
      " 0_p[(6, 3, 4)] ≥ -1.6\n",
      " 0_p[(4, 2, 4)] ≥ -1.58\n",
      " 0_p[(3, 2, 3)] ≥ -1.45\n",
      " 0_p[(5, 2, 5)] ≥ -1.61\n",
      " 0_p[(20, 13, 14)] ≥ -0.76\n",
      " 0_p[(13, 6, 13)] ≥ -2.01\n",
      " 0_p[(14, 7, 8)] ≥ -1.67\n",
      " 0_p[(15, 7, 9)] ≥ -2.67\n",
      " 0_p[(12, 6, 12)] ≥ -1.04\n",
      " 0_p[(18, 10, 11)] ≥ -1.41\n",
      " 0_p[(1, 2, 1)] ≥ -4.72\n",
      " 0_p[(2, 5, 1)] ≥ -1.28\n",
      " 0_p[(16, 10, 9)] ≥ -3.25\n",
      " 0_p[(11, 11, 6)] ≥ -1.34\n",
      " 0_p[(7, 5, 4)] ≥ -6.64\n",
      " 0_p[(9, 9, 4)] ≥ -0.53\n",
      " 0_p[(10, 6, 5)] ≥ -1.17\n",
      " 0_p[(19, 13, 12)] ≥ -0.99\n",
      " 0_p[(17, 14, 9)] ≥ -0.99\n",
      " 0_p[(8, 7, 4)] ≥ -1.41\n",
      " 0_p[(6, 4, 3)] ≥ -1.6\n",
      " 0_p[(4, 4, 2)] ≥ -1.58\n",
      " 0_p[(3, 3, 2)] ≥ -1.45\n",
      " 0_p[(5, 5, 2)] ≥ -1.61\n",
      " 0_p[(20, 14, 13)] ≥ -0.76\n",
      " 0_p[(13, 13, 6)] ≥ -2.01\n",
      " 0_p[(14, 8, 7)] ≥ -1.67\n",
      " 0_p[(15, 9, 7)] ≥ -2.67\n",
      " 0_p[(12, 12, 6)] ≥ -1.04\n",
      " 0_p[(18, 11, 10)] ≥ -1.41\n",
      " 0_q[(1, 1, 2)] ≥ -4.72\n",
      " 0_q[(2, 1, 5)] ≥ -1.28\n",
      " 0_q[(16, 9, 10)] ≥ -3.25\n",
      " 0_q[(11, 6, 11)] ≥ -1.34\n",
      " 0_q[(7, 4, 5)] ≥ -6.64\n",
      " 0_q[(9, 4, 9)] ≥ -0.53\n",
      " 0_q[(10, 5, 6)] ≥ -1.17\n",
      " 0_q[(19, 12, 13)] ≥ -0.99\n",
      " 0_q[(17, 9, 14)] ≥ -0.99\n",
      " 0_q[(8, 4, 7)] ≥ -1.41\n",
      " 0_q[(6, 3, 4)] ≥ -1.6\n",
      " 0_q[(4, 2, 4)] ≥ -1.58\n",
      " 0_q[(3, 2, 3)] ≥ -1.45\n",
      " 0_q[(5, 2, 5)] ≥ -1.61\n",
      " 0_q[(20, 13, 14)] ≥ -0.76\n",
      " 0_q[(13, 6, 13)] ≥ -2.01\n",
      " 0_q[(14, 7, 8)] ≥ -1.67\n",
      " 0_q[(15, 7, 9)] ≥ -2.67\n",
      " 0_q[(12, 6, 12)] ≥ -1.04\n",
      " 0_q[(18, 10, 11)] ≥ -1.41\n",
      " 0_q[(1, 2, 1)] ≥ -4.72\n",
      " 0_q[(2, 5, 1)] ≥ -1.28\n",
      " 0_q[(16, 10, 9)] ≥ -3.25\n",
      " 0_q[(11, 11, 6)] ≥ -1.34\n",
      " 0_q[(7, 5, 4)] ≥ -6.64\n",
      " 0_q[(9, 9, 4)] ≥ -0.53\n",
      " 0_q[(10, 6, 5)] ≥ -1.17\n",
      " 0_q[(19, 13, 12)] ≥ -0.99\n",
      " 0_q[(17, 14, 9)] ≥ -0.99\n",
      " 0_q[(8, 7, 4)] ≥ -1.41\n",
      " 0_q[(6, 4, 3)] ≥ -1.6\n",
      " 0_q[(4, 4, 2)] ≥ -1.58\n",
      " 0_q[(3, 3, 2)] ≥ -1.45\n",
      " 0_q[(5, 5, 2)] ≥ -1.61\n",
      " 0_q[(20, 14, 13)] ≥ -0.76\n",
      " 0_q[(13, 13, 6)] ≥ -2.01\n",
      " 0_q[(14, 8, 7)] ≥ -1.67\n",
      " 0_q[(15, 9, 7)] ≥ -2.67\n",
      " 0_q[(12, 12, 6)] ≥ -1.04\n",
      " 0_q[(18, 11, 10)] ≥ -1.41\n",
      " 0_vr[2] ≤ 1.06\n",
      " 0_vr[11] ≤ 1.06\n",
      " 0_vr[7] ≤ 1.06\n",
      " 0_vr[9] ≤ 1.06\n",
      " 0_vr[10] ≤ 1.06\n",
      " 0_vr[8] ≤ 1.06\n",
      " 0_vr[6] ≤ 1.06\n",
      " 0_vr[4] ≤ 1.06\n",
      " 0_vr[3] ≤ 1.06\n",
      " 0_vr[5] ≤ 1.06\n",
      " 0_vr[13] ≤ 1.06\n",
      " 0_vr[14] ≤ 1.06\n",
      " 0_vr[12] ≤ 1.06\n",
      " 0_vr[1] ≤ 1.06\n",
      " 0_vi[2] ≤ 1.06\n",
      " 0_vi[11] ≤ 1.06\n",
      " 0_vi[7] ≤ 1.06\n",
      " 0_vi[9] ≤ 1.06\n",
      " 0_vi[10] ≤ 1.06\n",
      " 0_vi[8] ≤ 1.06\n",
      " 0_vi[6] ≤ 1.06\n",
      " 0_vi[4] ≤ 1.06\n",
      " 0_vi[3] ≤ 1.06\n",
      " 0_vi[5] ≤ 1.06\n",
      " 0_vi[13] ≤ 1.06\n",
      " 0_vi[14] ≤ 1.06\n",
      " 0_vi[12] ≤ 1.06\n",
      " 0_vi[1] ≤ 1.06\n",
      " 0_pg[4] ≤ 0.0\n",
      " 0_pg[2] ≤ 2.3\n",
      " 0_pg[3] ≤ 0.0\n",
      " 0_pg[5] ≤ 0.0\n",
      " 0_pg[1] ≤ 3.98\n",
      " 0_qg[4] ≤ 0.564\n",
      " 0_qg[2] ≤ 1.15\n",
      " 0_qg[3] ≤ 0.72\n",
      " 0_qg[5] ≤ 0.3\n",
      " 0_qg[1] ≤ 1.99\n",
      " 0_p[(1, 1, 2)] ≤ 4.72\n",
      " 0_p[(2, 1, 5)] ≤ 1.28\n",
      " 0_p[(16, 9, 10)] ≤ 3.25\n",
      " 0_p[(11, 6, 11)] ≤ 1.34\n",
      " 0_p[(7, 4, 5)] ≤ 6.64\n",
      " 0_p[(9, 4, 9)] ≤ 0.53\n",
      " 0_p[(10, 5, 6)] ≤ 1.17\n",
      " 0_p[(19, 12, 13)] ≤ 0.99\n",
      " 0_p[(17, 9, 14)] ≤ 0.99\n",
      " 0_p[(8, 4, 7)] ≤ 1.41\n",
      " 0_p[(6, 3, 4)] ≤ 1.6\n",
      " 0_p[(4, 2, 4)] ≤ 1.58\n",
      " 0_p[(3, 2, 3)] ≤ 1.45\n",
      " 0_p[(5, 2, 5)] ≤ 1.61\n",
      " 0_p[(20, 13, 14)] ≤ 0.76\n",
      " 0_p[(13, 6, 13)] ≤ 2.01\n",
      " 0_p[(14, 7, 8)] ≤ 1.67\n",
      " 0_p[(15, 7, 9)] ≤ 2.67\n",
      " 0_p[(12, 6, 12)] ≤ 1.04\n",
      " 0_p[(18, 10, 11)] ≤ 1.41\n",
      " 0_p[(1, 2, 1)] ≤ 4.72\n",
      " 0_p[(2, 5, 1)] ≤ 1.28\n",
      " 0_p[(16, 10, 9)] ≤ 3.25\n",
      " 0_p[(11, 11, 6)] ≤ 1.34\n",
      " 0_p[(7, 5, 4)] ≤ 6.64\n",
      " 0_p[(9, 9, 4)] ≤ 0.53\n",
      " 0_p[(10, 6, 5)] ≤ 1.17\n",
      " 0_p[(19, 13, 12)] ≤ 0.99\n",
      " 0_p[(17, 14, 9)] ≤ 0.99\n",
      " 0_p[(8, 7, 4)] ≤ 1.41\n",
      " 0_p[(6, 4, 3)] ≤ 1.6\n",
      " 0_p[(4, 4, 2)] ≤ 1.58\n",
      " 0_p[(3, 3, 2)] ≤ 1.45\n",
      " 0_p[(5, 5, 2)] ≤ 1.61\n",
      " 0_p[(20, 14, 13)] ≤ 0.76\n",
      " 0_p[(13, 13, 6)] ≤ 2.01\n",
      " 0_p[(14, 8, 7)] ≤ 1.67\n",
      " 0_p[(15, 9, 7)] ≤ 2.67\n",
      " 0_p[(12, 12, 6)] ≤ 1.04\n",
      " 0_p[(18, 11, 10)] ≤ 1.41\n",
      " 0_q[(1, 1, 2)] ≤ 4.72\n",
      " 0_q[(2, 1, 5)] ≤ 1.28\n",
      " 0_q[(16, 9, 10)] ≤ 3.25\n",
      " 0_q[(11, 6, 11)] ≤ 1.34\n",
      " 0_q[(7, 4, 5)] ≤ 6.64\n",
      " 0_q[(9, 4, 9)] ≤ 0.53\n",
      " 0_q[(10, 5, 6)] ≤ 1.17\n",
      " 0_q[(19, 12, 13)] ≤ 0.99\n",
      " 0_q[(17, 9, 14)] ≤ 0.99\n",
      " 0_q[(8, 4, 7)] ≤ 1.41\n",
      " 0_q[(6, 3, 4)] ≤ 1.6\n",
      " 0_q[(4, 2, 4)] ≤ 1.58\n",
      " 0_q[(3, 2, 3)] ≤ 1.45\n",
      " 0_q[(5, 2, 5)] ≤ 1.61\n",
      " 0_q[(20, 13, 14)] ≤ 0.76\n",
      " 0_q[(13, 6, 13)] ≤ 2.01\n",
      " 0_q[(14, 7, 8)] ≤ 1.67\n",
      " 0_q[(15, 7, 9)] ≤ 2.67\n",
      " 0_q[(12, 6, 12)] ≤ 1.04\n",
      " 0_q[(18, 10, 11)] ≤ 1.41\n",
      " 0_q[(1, 2, 1)] ≤ 4.72\n",
      " 0_q[(2, 5, 1)] ≤ 1.28\n",
      " 0_q[(16, 10, 9)] ≤ 3.25\n",
      " 0_q[(11, 11, 6)] ≤ 1.34\n",
      " 0_q[(7, 5, 4)] ≤ 6.64\n",
      " 0_q[(9, 9, 4)] ≤ 0.53\n",
      " 0_q[(10, 6, 5)] ≤ 1.17\n",
      " 0_q[(19, 13, 12)] ≤ 0.99\n",
      " 0_q[(17, 14, 9)] ≤ 0.99\n",
      " 0_q[(8, 7, 4)] ≤ 1.41\n",
      " 0_q[(6, 4, 3)] ≤ 1.6\n",
      " 0_q[(4, 4, 2)] ≤ 1.58\n",
      " 0_q[(3, 3, 2)] ≤ 1.45\n",
      " 0_q[(5, 5, 2)] ≤ 1.61\n",
      " 0_q[(20, 14, 13)] ≤ 0.76\n",
      " 0_q[(13, 13, 6)] ≤ 2.01\n",
      " 0_q[(14, 8, 7)] ≤ 1.67\n",
      " 0_q[(15, 9, 7)] ≤ 2.67\n",
      " 0_q[(12, 12, 6)] ≤ 1.04\n",
      " 0_q[(18, 11, 10)] ≤ 1.41\n"
     ]
    }
   ],
   "source": [
    "pm = instantiate_model(file, ACRPowerModel, PowerModels.build_opf)\n",
    "print(pm.model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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