Add per-problem option tuning guide Measured guidance for choosing POUNCE solver options, from a study of ten non-default options across all 47 problems of the Mittelmann ampl-nlp suite (595 solves; every claimed win re-timed and independently verified). Covers: why there is no globally good setting, how to diagnose a model before picking options, a symptom-to-lever table, what each option measurably did across the suite, four worked examples predicted from the diagnosis before being measured, and one case where near-identical structure gave the opposite result. Links the defect and documentation findings to the repository rather than restating them here.