rseng-numerical-accuracy

Covers floating-point correctness in research code: why 0.1 + 0.2 != 0.3, choosing absolute vs relative tolerances in tests, accumulation error and safe summation, precision choices (float32 vs float64), catastrophic cancellation, NaN and infinity handling, and cross-platform or cross-library result drift. Use PROACTIVELY when floating-point comparisons fail mysteriously, when writing numerical tests or choosing…