sim-numerics/results_audit_chandrupatla_library.txt at 2b0ee228b49e9b10c388f8aafd31c4c98e558dfd - sim-numerics - Gitea: Git with a cup of tea

vvs/sim-numerics

Files

T

vvsandClaude Opus 5 de90c7a089 Audit on Valeri's challenge: our Chandrupatla implementation stops below 2*xtol (tl = tol/span > 1/2) while every other method, SciPy's included, stops at xtol; the protocol corrected that factor for ITP and not here, and best_of let the looser run win. Corrected it costs Chandrupatla 2 evaluations on APS (2785 -> 2787) and 18 on CHA (2423 -> 2441): real, in the competitor's favour, and not what changes the picture. The public scipy.optimize.elementwise.find_root, the private _chandrupatla and ours corrected agree on all 199 problems exactly.

What the tables' totals hid: problem by problem the scheduled projection beats Chandrupatla on APS 87/24/42, exact sign test p = 9e-5, while trailing by 0.7% in total; on CHA Chandrupatla is genuinely better (26 to 9, p = 0.006). The earned projection ties him on APS problems (79/3/71, p = 0.57). Benchmark not yet switched to the library baseline - awaiting Valeri's decision together with the framing of the proposed method.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

2026-09-21 10:14:42 +01:00

7 lines

323 B

Plaintext

Raw Blame History

 public find_root             APS  2787 (153/154)  CHA  2441 (40/45)  both 5228
 private _chandrupatla        APS  2787 (153/154)  CHA  2441 (40/45)  both 5228
 ours, tolerance corrected    APS  2787 (153/154)  CHA  2441 (40/45)  both 5228
 problems where public != private: 0 []
 problems where public != ours corrected: 0 []