Good luck, and may your matrices be well-conditioned.
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You will likely face a question comparing vs. Spline interpolation . Good luck, and may your matrices be well-conditioned
| Pitfall | Why It Happens | Quick Fix | |---------|----------------|-----------| | – Large or tiny numbers lead to overflow/underflow. | Floating‑point limits. | Normalize inputs; work in dimensionless form when possible. | | Stopping iterations too early – Residual appears small but error remains high. | Improper convergence criteria. | Check both absolute and relative tolerances, and monitor solution norms. | | Mismatching matrix storage – Using dense solvers on sparse matrices. | Overlooking sparsity patterns. | Switch to scipy.sparse.linalg or MATLAB’s sparse functions. | | Hard‑coding dimensions – Functions fail when the problem size changes. | Lack of vectorized code. | Write functions that accept n as an argument and use loops only when unavoidable. | | Copy‑pasting code without understanding – Errors propagate silently. | Time pressure. | Walk through each line with a debugger or print statements. | Spline interpolation