vvsandClaude Opus 5
6a1c264910
2: 'pre-registered' -> 'recorded in advance'; Appendix D states the order is attested only by the author's git history. 3: Dekker, Illinois and false position exhaust the budget on aps.13.00 under either criterion (stalled where |f|<1e-62, no sign change within 1e-6) - hence unchanged totals. 4: exact-zero stops in Table 11 (CHA: Brent 15/20, Chandrupatla 4/20, A' 2/20; corpus 1: none, transformation still 3.4x cheaper). 5: Table 12, the multiplicity estimated during the run (A' ahead; the analogy order inside Chandrupatla closes 31-59% of the gap on multiple roots and pays on simple ones; Lagouanelle blind on side-alternating triples); 6.6, conclusion, future work. 6: Algorithms 1-2 - lo/hi, return on a stall, L_x/L_f and the empty memory, the step pattern as a formula, O(n^2). 7: Table 3's two bracket-scaled rows, dashes in Table 5, the abstract's last sentence (250 words). verify_revision2: 17 checks. Server: verify 544 OK / 0 BAD, regression x2 1238 passed, PDF 32 pages, 0 errors, 0 overfull. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
91 lines
3.6 KiB
Python
91 lines
3.6 KiB
Python
"""Revision 2, points 3 and 4 - facts only, no new method.
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Point 3: Dekker, Illinois and false position show the same APS totals under both criteria. Which
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problems do they fail, and why do they fail aps.13.00 under 'sign change or f = 0' as well?
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Point 4: how do the runs on g = sign(f)|f|^(1/m*) (Table 11) end - by the bracket width, or on an
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exact zero of g (which every method, A' included, accepts)? Counted per method on the
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multiple/fractional problems, next to A' on the same problems.
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python -u diag_revision2.py
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"""
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from __future__ import annotations
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import json
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import math
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import random
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import scipy.optimize._tstutils as T
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from corpus_roots_jl import solved
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from rescore_uniform import load_uniform
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from simroot.baselines import brent_scipy, chandrupatla_lib
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from simroot.pure import pure_root
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from sweep_controls import k1_problems, m_star, nonsimple
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from sweep_full import MAXFEV, XTOL, make_method
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APRIME = ("pure", "ratio", "derived3", "project", "x", "contra+flatanalogmempt+exp+half+close+sub",
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"recent", "distance", "none", "pattern")
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def point3():
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U = {r["label"]: r for r in load_uniform("sweep_full.json")}
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O = {r["label"]: r for r in json.load(open("sweep_full.json", encoding="utf8"))}
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probs = {pid: (f, a, b) for s, pid, f, a, b, _r in __import__("sweep_full").load_problems()}
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for lb in ("dekker", "illinois", "regula_falsi"):
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fails_u = [p for p, (n, ok) in U[lb]["APS"]["per"].items() if not ok]
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fails_o = [p for p, (n, ok) in O[lb]["APS"]["per"].items() if not ok]
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print(f"{lb}: APS failures uniform {len(fails_u)}, harness {len(fails_o)}, "
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f"same set {sorted(fails_u) == sorted(fails_o)}; uniform total {U[lb]['APS']['total']}, "
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f"harness {O[lb]['APS']['total']}")
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f, a, b = probs["aps.13.00"]
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x, n, conv = make_method(("base", lb))(f, a, b, XTOL, MAXFEV)
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print(f" aps.13.00: returned x={float(x):.3g}, nfev={n}, converged={conv}, "
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f"f(x)={f(float(x))!r}, sign-change criterion {solved(f, float(x))}")
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def ends_on_zero(method, f, g, a, b):
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"""(nfev, stopped on an exact zero of the function it saw, bracket-width stop)."""
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seen = []
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def w(x):
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v = g(x)
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seen.append(v)
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return v
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T._f6_cache.clear()
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random.seed(20260921)
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x, n, conv = method(w, a, b, XTOL, MAXFEV)
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return int(n), bool(seen and seen[-1] == 0.0), bool(conv)
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def point4():
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"""Returns {corpus: {method: [problems, exact-zero stops, evaluations]}} and prints it."""
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out = {}
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for s in ("CHA", "NEW", "D"):
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tally = {"chandrupatla_g": [0, 0, 0], "brent_g": [0, 0, 0], "Aprime": [0, 0, 0]}
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for pid, f, a, b, r in k1_problems(s):
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m = m_star(f, r)
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if not nonsimple(m):
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continue
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inv = 1.0 / m
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def g(x, _f=f, _i=inv):
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v = _f(x)
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return 0.0 if v == 0 else math.copysign(abs(v) ** _i, v)
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for name, meth, fun in (("chandrupatla_g", chandrupatla_lib, g), ("brent_g", brent_scipy, g),
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("Aprime", make_method(APRIME), f)):
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n, zero, conv = ends_on_zero(meth, f, fun, a, b)
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t = tally[name]
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t[0] += 1
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t[1] += zero
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t[2] += n
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print(f"{s}: problems {tally['Aprime'][0]}; runs ending on an exact zero / total evaluations: " +
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"; ".join(f"{k} {v[1]}/{v[2]}" for k, v in tally.items()))
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out[s] = tally
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return out
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if __name__ == "__main__":
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point3()
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point4()
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print("revision2 diag done")
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