vvsandClaude Opus 5
1b6c626f67
(2) The proposed method is again the scheduled projection (2806/3220/6026), its budget multiplier DECLARED as the one free constant: the canonical total is the same for every multiplier 1.25-4, the wide-bracket one is not. The earned projection is the constant-free variant (3568/3020/6588). What the old tables hid: problem by problem the proposed rule beats Chandrupatla on the canonical set 87/24/42, exact sign test p = 9e-5, while trailing him by 0.7% in total; on the wide-bracket set he is genuinely stronger (26 to 9, p = 0.006). Gap decomposition recomputed with the library: 244 (was 262), 160 of it on the flat families (two thirds), fun1 58 (a quarter, not a third as the abstract said). Also corrected on the way: the 'subsumed by Chandrupatla' pin claimed he is better on the worst single problem - true only with the looser tolerance (84 against the credit rule's 83 once fixed); §8.3 still called zero slack degenerate, which Table 1a refutes; §5.2's Brent figure is our pure-Python implementation and now says so. verify_article.py 286/286, 802 tests pass twice. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
66 lines
3.3 KiB
Python
66 lines
3.3 KiB
Python
"""Tables 2-3 of article/sim_numerics.md regenerated from sweep_full.json.
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The published tables predate Chandrupatla's own method as a baseline (it is first on
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Alefeld-Potra-Shi) and name the projection variant as proposed, while the code and
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the proposed method is the scheduled projection with its multiplier declared (Valeri,
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2026-09-21, variant a), the earned projection the constant-free variant. Baselines are
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the library alone (Chandrupatla = scipy elementwise.find_root, Brent = brentq). This
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prints the rows and head-to-head counts from the data, with the SAME ranking function the
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verifier uses, so nothing in the new tables is transcribed by hand.
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python -u tables_for_md.py
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"""
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from __future__ import annotations
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from report_sweep import CREDIT, PROPOSED, TUNED, head_to_head, load, place
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NONE = "pure.ratio.derived3.none"
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NAMES = {PROPOSED: "**proposed: tent, 3-point, projection ($c=2$)**",
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CREDIT: "constant-free variant: earned projection",
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NONE: "the same rule with no safeguard at all",
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"chandrupatla": "Chandrupatla", "toms748": "TOMS 748", "brent": "Brent",
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"dekker": "Dekker", "itp_oracle": "ITP, per-problem oracle",
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"ridders": "Ridders", "itp": "ITP, recommended constants",
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"illinois": "Illinois", "bisection": "bisection",
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"regula_falsi": "false position",
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"pure.sqrt.bracket.project": "similarity, $1-\\sqrt{|z|}$, bracket scale, projection",
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"pure.exp_abs.bracket.both": "similarity, $e^{-|z|}$, bracket scale, both guards"}
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APS_ROWS = ("chandrupatla", CREDIT, NONE, "toms748", "brent", "dekker", "itp_oracle",
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"ridders", PROPOSED, "itp", "illinois", "bisection", "regula_falsi")
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CHA_ROWS = ("pure.sqrt.bracket.project", "chandrupatla", "itp_oracle",
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"pure.exp_abs.bracket.both", "itp", PROPOSED, "bisection", CREDIT, NONE,
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"toms748", "brent")
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OPPONENTS = ("chandrupatla", "toms748", "brent", "itp", "itp_oracle", "bisection")
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def main():
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rows = load()
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by = {r["label"]: r for r in rows}
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print(f"configurations in the sweep: {len(rows)}")
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for setname, keys in (("APS", APS_ROWS), ("CHA", CHA_ROWS)):
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n = by[PROPOSED][setname]["n"]
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print(f"\n{setname}, {n} problems")
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print("| # | configuration | tuned | solved | total evals | $>2\\times$ |")
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print("|---|---|---|---|---|---|")
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for k in sorted(keys, key=lambda k: place(rows, setname, k)):
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d = by[k][setname]
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tuned = "yes" if k in TUNED else "no"
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over = "—" if k in ("regula_falsi",) else str(d["over"])
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print(f"| {place(rows, setname, k)} | {NAMES[k]} | {tuned} | "
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f"{d['solved']}/{n} | {d['total']} | {over} |")
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for lbl in (PROPOSED, CREDIT):
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print(f"\nhead to head, {lbl} (wins/ties/losses on problems both solve)")
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for setname in ("APS", "CHA"):
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cells = []
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for opp in OPPONENTS:
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w, t, l, _ = head_to_head(rows, lbl, opp, setname)
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cells.append(f"{opp} {w}/{t}/{l}")
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print(f" {setname}: " + ", ".join(cells))
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for lbl in (PROPOSED, CREDIT, "chandrupatla", "toms748", "brent", "itp_oracle"):
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d = by[lbl]
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print(f" both sets {lbl:34s} {d['APS']['total'] + d['CHA']['total']}")
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if __name__ == "__main__":
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main()
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