Iteration final - SOLVER_EXECUTION
Sequence: 10
Timestamp: 2025-07-25 22:42:04

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

Response:
{
  "gurobipy": {
    "solver_type": "gurobipy",
    "execution_time": 0.1921546459197998,
    "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 16 rows, 3 columns and 6 nonzeros\nModel fingerprint: 0x8c616fdf\nCoefficient statistics:\n  Matrix range     [1e+00, 1e+00]\n  Objective range  [5e+00, 7e+00]\n  Bounds range     [0e+00, 0e+00]\n  RHS range        [3e+03, 2e+05]\nPresolve removed 16 rows and 3 columns\nPresolve time: 0.00s\nPresolve: All rows and columns removed\nIteration    Objective       Primal Inf.    Dual Inf.      Time\n       0    9.9039300e+05   0.000000e+00   0.000000e+00      0s\n\nSolved in 0 iterations and 0.00 seconds (0.00 work units)\nOptimal objective  9.903930000e+05\nOptimal value: 990392.9999999999\nScholarship for student 1: $143300.00\nScholarship for student 2: $3500.00\nScholarship for student 3: $3200.00\n",
    "stderr": "",
    "status": "optimal",
    "optimal_value": 990392.9999999999,
    "error_message": null,
    "decision_variables": {}
  },
  "docplex": {
    "solver_type": "docplex",
    "execution_time": 1.0656778812408447,
    "return_code": 1,
    "stdout": "",
    "stderr": "Traceback (most recent call last):\n  File \"/tmp/tmppa83ouwb.py\", line 73, in <module>\n    scholarship_allocation_optimization()\n  File \"/tmp/tmppa83ouwb.py\", line 34, in scholarship_allocation_optimization\n    objective = mdl.sum((weights['w1'] * gpa[i] + weights['w2'] * sports_hours[i] + weights['w3'] * gaming_hours[i]) * scholarship_amount[i] for i in safe_range)\n  File \"/dccstor/nl2opt/miniforge3/envs/nl2opt_optim/lib/python3.10/site-packages/docplex/mp/model.py\", line 3342, in sum\n    return self._aggregator.sum(args)\n  File \"/dccstor/nl2opt/miniforge3/envs/nl2opt_optim/lib/python3.10/site-packages/docplex/mp/aggregator.py\", line 198, in sum\n    sum_res = self._sum_with_iter(sum_args)\n  File \"/dccstor/nl2opt/miniforge3/envs/nl2opt_optim/lib/python3.10/site-packages/docplex/mp/aggregator.py\", line 221, in _sum_with_iter\n    for item in args:\n  File \"/tmp/tmppa83ouwb.py\", line 34, in <genexpr>\n    objective = mdl.sum((weights['w1'] * gpa[i] + weights['w2'] * sports_hours[i] + weights['w3'] * gaming_hours[i]) * scholarship_amount[i] for i in safe_range)\nKeyError: 0\n",
    "status": "error",
    "optimal_value": null,
    "error_message": "Traceback (most recent call last):\n  File \"/tmp/tmppa83ouwb.py\", line 73, in <module>\n    scholarship_allocation_optimization()\n  File \"/tmp/tmppa83ouwb.py\", line 34, in scholarship_allocation_optimization\n    objective = mdl.sum((weights['w1'] * gpa[i] + weights['w2'] * sports_hours[i] + weights['w3'] * gaming_hours[i]) * scholarship_amount[i] for i in safe_range)\n  File \"/dccstor/nl2opt/miniforge3/envs/nl2opt_optim/lib/python3.10/site-packages/docplex/mp/model.py\", line 3342, in sum\n    return self._aggregator.sum(args)\n  File \"/dccstor/nl2opt/miniforge3/envs/nl2opt_optim/lib/python3.10/site-packages/docplex/mp/aggregator.py\", line 198, in sum\n    sum_res = self._sum_with_iter(sum_args)\n  File \"/dccstor/nl2opt/miniforge3/envs/nl2opt_optim/lib/python3.10/site-packages/docplex/mp/aggregator.py\", line 221, in _sum_with_iter\n    for item in args:\n  File \"/tmp/tmppa83ouwb.py\", line 34, in <genexpr>\n    objective = mdl.sum((weights['w1'] * gpa[i] + weights['w2'] * sports_hours[i] + weights['w3'] * gaming_hours[i]) * scholarship_amount[i] for i in safe_range)\nKeyError: 0\n",
    "decision_variables": {}
  },
  "pyomo": {
    "solver_type": "pyomo",
    "execution_time": 0.9299788475036621,
    "return_code": 0,
    "stdout": "Read LP format model from file /tmp/tmp8_lzv96w.pyomo.lp\nReading time = 0.00 seconds\nx1: 16 rows, 4 columns, 6 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 16 rows, 4 columns and 6 nonzeros\nModel fingerprint: 0x4cf9fcfa\nCoefficient statistics:\n  Matrix range     [1e+00, 1e+00]\n  Objective range  [5e+00, 7e+00]\n  Bounds range     [1e+00, 1e+00]\n  RHS range        [2e+00, 2e+05]\nPresolve removed 16 rows and 4 columns\nPresolve time: 0.00s\nPresolve: All rows and columns removed\nIteration    Objective       Primal Inf.    Dual Inf.      Time\n       0    9.9039300e+05   0.000000e+00   0.000000e+00      0s\n\nSolved in 0 iterations and 0.00 seconds (0.00 work units)\nOptimal objective  9.903930000e+05\nOptimal solution found!\nOptimal value: 990392.9999999999\n\nScholarship Allocation:\nStudent 1: $143300.00\nStudent 2: $3500.00\nStudent 3: $3200.00\n",
    "stderr": "",
    "status": "optimal",
    "optimal_value": 990392.9999999999,
    "error_message": null,
    "decision_variables": {}
  }
}
