run-solver

# 运行求解器 在本地环境执行数模求解代码: $ARGUMENTS ## Workflow ### Step 1: 检测环境 Read the project's `CLAUDE.md` to determine the compute environment: - **Python**: Look for Python version, virtualenv/conda info, required packages - **MATLAB**: Look for MATLAB installation path and license info If no environment info is found in `CLAUDE.md`, auto-detect: ```bash # Check Python python3 --version 2>/dev/null || python --version 2>/dev/null pip list 2>/dev/null | grep -E "numpy|scipy|matplotlib|pandas|sklearn|pulp|cvxpy" # Check MATLAB which matlab 2>/dev/null && matlab -batch "disp('MATLAB OK')" 2>/dev/null ``` ### Step 2: 预检查 Check compute environment readiness: ```bash # Check available memory python3 -c "import psutil; print(f'RAM: {psutil.virtual_memory().total/1e9:.1f} GB, Available: {psutil.virtual_memory().available/1e9:.1f} GB')" # Check required packages python3 -c " import importlib, sys required = ['numpy', 'scipy', 'matplotlib', 'pandas'] missing = [p for p in required if importlib.util.find_spec(p) is None] if missing: print(f'Missing: {missing}'); sys.exit(1) print('All required packages available') " ``` If packages are missing, install them: ```bash pip install numpy scipy matplotlib pandas scikit-learn ``` ### Step 3: 准备求解脚本 1. **Read the solve plan**: Check `SOLVE_PLAN.md` or `refine-logs/SOLVE_PLAN.md` for the execution order 2. **Locate scripts**: Find solver scripts in the project directory ``` Glob: **/*.py, **/*.m ``` 3. **Validate scripts**: Quick syntax check before running ```bash python3 -m py_compile <script> ``` ### Step 3.5: 日志配置 Ensure the solver scripts have proper logging: 1. **Check if logging is already in the script** — look for `import logging` or `print` statements with progress info. If present, skip to Step 4. 2. **If not present, add basic logging** to the solver script: ```python import logging, time logging.basicConfig(filename='solve_log.txt', level=logging.INFO, format='%(asctime)s - %(message)s') # Inside solving loop: logging.info(f"Iteration {i}: objective={obj_value:.6f}, residual={residual:.6e}") # After completion: logging.info(f"Solving complete. Total time: {time.time()-start:.1f}s") ``` 3. **Metrics to log** (add whichever apply to the solver): - `iteration` — current iteration number - `objective` — objective function value - `residual` — convergence residual - `elapsed_time` — time since start - `R²`, `RMSE`, or other relevant metrics - Any custom metrics the solver already computes > Log files are saved to the project directory for later inspection by `/monitor-solver` and `/analyze-results`. ### Step 4: 执行求解 #### Python ```bash # Run solver with timeout protection timeout 3600 python3 <script> <args> 2>&1 | tee solve_log.txt # For long-running solvers, use background execution python3 <script> <args> > solve_log.txt 2>&1 & echo $! > solver_pid.txt ``` #### MATLAB ```bash # Run MATLAB script timeout 3600 matlab -batch "run('<script>')" 2>&1 | tee solve_log.txt ``` For long-running jobs, use `run_in_background: true` to keep the conversation responsive. ### Step 5: 验证启动 Check process is running and producing output: ```bash # Check if process is still running ps aux | grep -E "python|matlab" | grep -v grep # Check if log file is growing ls -la solve_log.txt tail -5 solve_log.txt ``` ### Step 6: 结果收集 After solver completes: 1. Check exit code for errors 2. Collect output files (results, figures, data) 3. Save a brief execution summary ```bash echo "=== 求解完成 ===" echo "退出码: $?" echo "耗时: $(cat solve_log.txt | grep 'Total time' | tail -1)" echo "输出文件:" ls -la results/ output/ figures/ 2>/dev/null ``` ## Key Rules - **Large file handling**: If the Write tool fails due to file size, immediately retry using Bash (`cat << 'EOF' > file`) to write in chunks. Do NOT ask the user for permission — just do it silently. - ALWAYS check environment readiness first — never blindly run scripts - Each solver gets its own background process with timeout protection - Use `tee` to save logs for later inspection - Run long commands with `run_in_background: true` to keep conversation responsive - Report back: which script, what arguments, estimated time, PID - If multiple sub-problems, run independent solvers in parallel - Default timeout: 60 minutes. Warn if solver is expected to take longer. - If solver fails, capture error output and suggest fixes ## CLAUDE.md Example Users should add their environment info to their project's `CLAUDE.md`: ```markdown ## 计算环境 - Python: 3.10+ with numpy, scipy, matplotlib, pandas, scikit-learn - MATLAB: R2024a (optional) - 额外包: cvxpy (for optimization), networkx (for graph problems) - 超时限制: 60 minutes per solver - 数据目录: data/ - 结果目录: results/ ``` > **环境配置**: Ensure all required packages are installed before running. The skill will auto-detect and install missing Python packages, but MATLAB toolboxes must be pre-installed.