Iteration final - SOLVER_EXECUTION
Sequence: 8
Timestamp: 2025-07-27 23:56:55

Prompt:
Generated solver codes with templates and executed them with retry logic

Response:
{
  "gurobipy": {
    "solver_type": "gurobipy",
    "execution_time": 0.1884167194366455,
    "return_code": 1,
    "stdout": "Restricted license - for non-production use only - expires 2026-11-23\n",
    "stderr": "Traceback (most recent call last):\n  File \"/tmp/tmpw6dojmm4.py\", line 83, in <module>\n    optimize_student_engagement()\n  File \"/tmp/tmpw6dojmm4.py\", line 38, in optimize_student_engagement\n    gp.quicksum(game_engagement_scores[StuID, GameID] * x[StuID, GameID] for StuID in students for GameID in games) +\n  File \"src/gurobipy/_helpers.pyx\", line 41, in gurobipy._helpers.quicksum\n  File \"/tmp/tmpw6dojmm4.py\", line 38, in <genexpr>\n    gp.quicksum(game_engagement_scores[StuID, GameID] * x[StuID, GameID] for StuID in students for GameID in games) +\nKeyError: (1, 102)\n",
    "status": "error",
    "optimal_value": null,
    "error_message": "Traceback (most recent call last):\n  File \"/tmp/tmpw6dojmm4.py\", line 83, in <module>\n    optimize_student_engagement()\n  File \"/tmp/tmpw6dojmm4.py\", line 38, in optimize_student_engagement\n    gp.quicksum(game_engagement_scores[StuID, GameID] * x[StuID, GameID] for StuID in students for GameID in games) +\n  File \"src/gurobipy/_helpers.pyx\", line 41, in gurobipy._helpers.quicksum\n  File \"/tmp/tmpw6dojmm4.py\", line 38, in <genexpr>\n    gp.quicksum(game_engagement_scores[StuID, GameID] * x[StuID, GameID] for StuID in students for GameID in games) +\nKeyError: (1, 102)\n",
    "decision_variables": {}
  },
  "docplex": {
    "solver_type": "docplex",
    "execution_time": 1.0647978782653809,
    "return_code": 0,
    "stdout": "Optimal value: 116.5\nx[1, 101] = 15.000\ny[1, Basketball] = 5.000\nx[2, 102] = 15.000\ny[2, Soccer] = 5.000\ny[3, Tennis] = 20.000\n",
    "stderr": "",
    "status": "optimal",
    "optimal_value": 116.5,
    "error_message": null,
    "decision_variables": {}
  },
  "pyomo": {
    "solver_type": "pyomo",
    "execution_time": 1.0211200714111328,
    "return_code": 0,
    "stdout": "Read LP format model from file /tmp/tmplsg9r43n.pyomo.lp\nReading time = 0.00 seconds\nx1: 9 rows, 18 columns, 36 nonzeros\nGurobi Optimizer version 12.0.2 build v12.0.2rc0 (linux64 - \"Red Hat Enterprise Linux 9.4 (Plow)\")\n\nCPU model: AMD EPYC 7513 32-Core Processor, instruction set [SSE2|AVX|AVX2]\nThread count: 64 physical cores, 128 logical processors, using up to 32 threads\n\nOptimize a model with 9 rows, 18 columns and 36 nonzeros\nModel fingerprint: 0xa1bfa0c0\nCoefficient statistics:\n  Matrix range     [1e+00, 1e+00]\n  Objective range  [2e+00, 2e+00]\n  Bounds range     [0e+00, 0e+00]\n  RHS range        [5e+00, 2e+01]\nPresolve removed 9 rows and 18 columns\nPresolve time: 0.00s\nPresolve: All rows and columns removed\nIteration    Objective       Primal Inf.    Dual Inf.      Time\n       0    1.1650000e+02   0.000000e+00   0.000000e+00      0s\n\nSolved in 0 iterations and 0.00 seconds (0.00 work units)\nOptimal objective  1.165000000e+02\nOptimal solution found!\nOptimal value: 116.500\n\nVariable values:\nx[1, 101] = 15.000\ny[1, Basketball] = 5.000\nx[2, 102] = 15.000\ny[2, Soccer] = 5.000\ny[3, Tennis] = 20.000\n",
    "stderr": "",
    "status": "optimal",
    "optimal_value": 116.5,
    "error_message": null,
    "decision_variables": {}
  }
}
