Iteration final - SOLVER_EXECUTION
Sequence: 6
Timestamp: 2025-07-27 23:21:31

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

Response:
{
  "gurobipy": {
    "solver_type": "gurobipy",
    "execution_time": 0.19152045249938965,
    "return_code": 0,
    "stdout": "Restricted license - for non-production use only - expires 2026-11-23\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 6 rows, 5 columns and 10 nonzeros\nModel fingerprint: 0xd414cdaf\nVariable types: 0 continuous, 5 integer (5 binary)\nCoefficient statistics:\n  Matrix range     [1e+00, 1e+00]\n  Objective range  [4e-01, 8e-01]\n  Bounds range     [1e+00, 1e+00]\n  RHS range        [1e+00, 1e+00]\nFound heuristic solution: objective 2.1000000\nPresolve removed 6 rows and 5 columns\nPresolve time: 0.00s\nPresolve: All rows and columns removed\n\nExplored 0 nodes (0 simplex iterations) in 0.00 seconds (0.00 work units)\nThread count was 1 (of 128 available processors)\n\nSolution count 1: 2.1 \n\nOptimal solution found (tolerance 1.00e-04)\nBest objective 2.100000000000e+00, best bound 2.100000000000e+00, gap 0.0000%\nOptimal value: 2.0999999999999996\nTeacher 201 assigned to Course 101 with expected improvement 0.8\nTeacher 202 assigned to Course 102 with expected improvement 0.6\nTeacher 203 assigned to Course 103 with expected improvement 0.7\n",
    "stderr": "",
    "status": "optimal",
    "optimal_value": 2.0999999999999996,
    "error_message": null,
    "decision_variables": {}
  },
  "docplex": {
    "solver_type": "docplex",
    "execution_time": 4.752462387084961,
    "return_code": 1,
    "stdout": "",
    "stderr": "Traceback (most recent call last):\n  File \"/tmp/tmpfki_jsmx.py\", line 62, in <module>\n    optimize_teacher_course_assignment()\n  File \"/tmp/tmpfki_jsmx.py\", line 25, in optimize_teacher_course_assignment\n    assert all((course, teacher) in expected_improvements for course in courses for teacher in teachers), \"Data inconsistency detected\"\nAssertionError: Data inconsistency detected\n",
    "status": "error",
    "optimal_value": null,
    "error_message": "Traceback (most recent call last):\n  File \"/tmp/tmpfki_jsmx.py\", line 62, in <module>\n    optimize_teacher_course_assignment()\n  File \"/tmp/tmpfki_jsmx.py\", line 25, in optimize_teacher_course_assignment\n    assert all((course, teacher) in expected_improvements for course in courses for teacher in teachers), \"Data inconsistency detected\"\nAssertionError: Data inconsistency detected\n",
    "decision_variables": {}
  },
  "pyomo": {
    "solver_type": "pyomo",
    "execution_time": 5.369604825973511,
    "return_code": 0,
    "stdout": "Read LP format model from file /tmp/tmpb5_92ip1.pyomo.lp\nReading time = 0.00 seconds\nx1: 6 rows, 9 columns, 18 nonzeros\nSet parameter TimeLimit to value 300\nSet parameter MIPGap to value 0.01\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\nNon-default parameters:\nTimeLimit  300\nMIPGap  0.01\n\nOptimize a model with 6 rows, 9 columns and 18 nonzeros\nModel fingerprint: 0x2e972041\nVariable types: 0 continuous, 9 integer (9 binary)\nCoefficient statistics:\n  Matrix range     [1e+00, 1e+00]\n  Objective range  [4e-01, 8e-01]\n  Bounds range     [1e+00, 1e+00]\n  RHS range        [1e+00, 1e+00]\nFound heuristic solution: objective 2.1000000\nPresolve removed 6 rows and 9 columns\nPresolve time: 0.00s\nPresolve: All rows and columns removed\n\nExplored 0 nodes (0 simplex iterations) in 0.00 seconds (0.00 work units)\nThread count was 1 (of 128 available processors)\n\nSolution count 1: 2.1 \n\nOptimal solution found (tolerance 1.00e-02)\nBest objective 2.100000000000e+00, best bound 2.100000000000e+00, gap 0.0000%\nOptimal solution found!\nOptimal value: 2.100\n\nAssignment values:\nx[101,201] = 1\nx[102,202] = 1\nx[103,203] = 1\n",
    "stderr": "",
    "status": "optimal",
    "optimal_value": 2.1,
    "error_message": null,
    "decision_variables": {}
  }
}
