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>
242 lines
9.5 KiB
Python
242 lines
9.5 KiB
Python
"""Stage 19 - the objection that the repairs compared against are from 1973.
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Claim A as it stood was: the credit projection repairs regula falsi better
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than Illinois (1971), Pegasus (1972) and Anderson-Bjorck (1973). A reader
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answers that the field has moved on, and is right to: TOMS 748 (1995),
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Chandrupatla (1997) and ITP (2021) all beat credit-repaired regula falsi.
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Repairing a three-century-old interpolation better than the 1970s did is not
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a modern result.
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The credit projection is not tied to regula falsi, though. It is a safeguard
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for ANY step rule. So the strong form of the claim is not "our repair of
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their old rule beats their old repair" but
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take the best interpolation in the field and replace its own acceptance
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test with the credit projection.
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Chandrupatla's method is exactly that pairing: an inverse quadratic
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interpolation plus an acceptance test derived from where that interpolation
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stays inside the bracket. Swapping the test for the credit projection, and
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changing nothing else, is the sharpest test available of whether the
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safeguard is worth anything against the modern state of the art.
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Two further modern repairs of the false-position family are included so that
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the 1970s are not the only comparison: Ford's Illinois-type variant (1995)
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and Galdino's (2011).
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"""
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from __future__ import annotations
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import math
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from scipy.optimize._tstutils import (_APS_TESTS_DICTS,
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_CHANDRUPATLA_TESTS_DICTS)
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from simroot.baselines import Counter, _Budget, chandrupatla
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from simroot.pure import _Safeguard
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from stage18_bulletproof import falsi_driver, load_roots
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XTOL, MAXFEV, ACC = 1e-12, 200, 1e-6
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def chandrupatla_guard(f, a, b, xtol, maxfev, guard="own"):
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"""Chandrupatla's method with its safeguard pluggable.
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guard="own" the published acceptance test on xi and phi
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guard="credit" always interpolate, and project the result onto the
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interval the earned bisection budget allows
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guard="none" always interpolate, clamp to the bracket only
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Everything else - the interpolation, the bracket bookkeeping, the
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termination test - is identical, so a difference can only be the guard.
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"""
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g = Counter(f, maxfev)
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x1, x2 = b, a
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f1 = f2 = 0.0
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try:
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f1, f2 = g(x1), g(x2)
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if f1 == 0.0:
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return x1, g.n, True
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if f2 == 0.0:
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return x2, g.n, True
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x3, f3 = x2, f2
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t = 0.5
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sg = _Safeguard("credit", abs(b - a), xtol)
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while True:
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x = x1 + t * (x2 - x1)
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w_before = abs(x2 - x1)
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fx = g(x)
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if (fx > 0.0) == (f1 > 0.0):
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x3, f3 = x1, f1
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x1, f1 = x, fx
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else:
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x3, f3 = x2, f2
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x2, f2 = x1, f1
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x1, f1 = x, fx
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if guard == "credit":
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sg.note_shrink(w_before, abs(x2 - x1))
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xm, fm = (x1, f1) if abs(f1) < abs(f2) else (x2, f2)
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if fm == 0.0:
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return xm, g.n, True
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tol = 2.0 * 2.22e-16 * abs(xm) + 0.5 * xtol # 2026-09-21 fix, see baselines.chandrupatla
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span = abs(x2 - x1)
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if span == 0.0:
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return xm, g.n, True
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tl = tol / span
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if tl > 0.5:
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return xm, g.n, True
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t = 0.5
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if x3 != x2 and f3 != f2:
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xi = (x1 - x2) / (x3 - x2)
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ph = (f1 - f2) / (f3 - f2)
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d1, d2, d3 = f1 - f2, f1 - f3, f2 - f3
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usable = d1 != 0.0 and d2 != 0.0 and d3 != 0.0
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if usable:
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t_iqi = ((f1 / d1) * (f3 / (-d3))
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+ ((x3 - x1) / (x2 - x1)) * (f1 / d2) * (f2 / d3))
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if guard == "own":
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if (0.0 < xi < 1.0
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and 1.0 - math.sqrt(1.0 - xi) < ph
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< math.sqrt(xi)):
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t = t_iqi
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else:
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t = t_iqi
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if guard == "credit" and math.isfinite(t):
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lo = min(x1, x2)
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hi = max(x1, x2)
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cand = x1 + t * (x2 - x1)
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proj = sg.project_credit(cand, lo, hi)
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if x2 != x1:
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t = (proj - x1) / (x2 - x1)
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if not math.isfinite(t):
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t = 0.5
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t = min(max(t, tl), 1.0 - tl)
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except _Budget:
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return (x1 if abs(f1) < abs(f2) else x2), g.n, False
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# --------------------------------------------------------------------------
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# two modern repairs of the false-position family, so the 1970s are not the
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# only comparison
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# --------------------------------------------------------------------------
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def falsi_modern(f, a, b, xtol, maxfev, variant):
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"""variant="ford" Ford 1995, Illinois-type with the Anderson-Bjorck
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factor bounded away from zero
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variant="galdino" Galdino 2011, the factor f_moved/(f_moved + fx)
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applied to both ordinates
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"""
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g = Counter(f, maxfev)
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x = 0.5 * (a + b)
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try:
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fa, fb = g(a), g(b)
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if fa == 0.0:
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return a, g.n, True
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if fb == 0.0:
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return b, g.n, True
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if fa * fb > 0.0:
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raise ValueError("no sign change")
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while abs(b - a) > xtol:
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lo, hi = (a, b) if a < b else (b, a)
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denom = fb - fa
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x_new = 0.5 * (a + b) if denom == 0.0 else (a * fb - b * fa) / denom
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if not math.isfinite(x_new) or not (lo < x_new < hi):
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x_new = 0.5 * (a + b)
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if x_new in (a, b):
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x_new = 0.5 * (a + b)
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if x_new in (a, b):
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return x_new, g.n, abs(b - a) <= xtol
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fx = g(x_new)
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if fx == 0.0:
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return x_new, g.n, True
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def factor(f_stale, f_moved):
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if variant == "ford":
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m = 1.0 - fx / f_moved if f_moved != 0.0 else 0.5
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return m if m > 1e-3 else 0.5
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m = f_moved / (f_moved + fx) if (f_moved + fx) != 0.0 else 0.5
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return m if 0.0 < m < 1.0 else 0.5
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if fa * fx < 0.0:
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fa *= factor(fa, fb)
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b, fb = x_new, fx
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else:
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fb *= factor(fb, fa)
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a, fa = x_new, fx
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x = x_new
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return 0.5 * (a + b), g.n, True
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except _Budget:
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return x, g.n, False
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def score(method, probs):
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total = solved = worst = 0
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for f, a, b, root in probs:
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try:
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x, n, conv = method(f, a, b, XTOL, MAXFEV)
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except Exception:
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x, n, conv = float("nan"), MAXFEV, False
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ok = bool(conv and math.isfinite(x) and abs(x - root) <= ACC)
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total += n if ok else MAXFEV
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solved += ok
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if ok:
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worst = max(worst, n)
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return total, solved, worst
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def main():
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probs = load_roots()
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print("=" * 82)
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print("A. the false-position family, 1971 to 2011, plus the credit "
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"projection")
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print("=" * 82)
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print(f"{'repair':22s}{'year':>7s}{'evals':>10s}{'solved':>10s}"
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f"{'worst':>8s}")
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print("-" * 82)
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rows = []
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for label, year, m in (
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("Illinois", "1971",
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lambda f, a, b, xt, mx: falsi_driver(f, a, b, xt, mx, "illinois")),
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("Pegasus", "1972",
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lambda f, a, b, xt, mx: falsi_driver(f, a, b, xt, mx, "pegasus")),
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("Anderson-Bjorck", "1973",
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lambda f, a, b, xt, mx: falsi_driver(f, a, b, xt, mx, "anderson")),
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("Ford (Illinois-type)", "1995",
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lambda f, a, b, xt, mx: falsi_modern(f, a, b, xt, mx, "ford")),
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("Galdino", "2011",
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lambda f, a, b, xt, mx: falsi_modern(f, a, b, xt, mx, "galdino")),
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("credit projection", "here",
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lambda f, a, b, xt, mx: falsi_driver(f, a, b, xt, mx, "credit"))):
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t, s, w = score(m, probs)
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rows.append((label, t, s))
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print(f"{label:22s}{year:>7s}{t:>10d}{s:>7d}/199{w:>8d}", flush=True)
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print("\n" + "=" * 82)
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print("B. the sharp test: Chandrupatla's interpolation, its guard swapped")
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print("=" * 82)
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print("same interpolation, same bookkeeping, same termination; only the "
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"guard differs\n")
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print(f"{'guard':30s}{'evals':>10s}{'solved':>10s}{'worst':>8s}")
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print("-" * 82)
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res = {}
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for label, guard in (("Chandrupatla's own test", "own"),
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("none (clamp to bracket only)", "none"),
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("the credit projection", "credit")):
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t, s, w = score(lambda f, a, b, xt, mx, _g=guard:
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chandrupatla_guard(f, a, b, xt, mx, _g), probs)
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res[guard] = (t, s, w)
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print(f"{label:30s}{t:>10d}{s:>7d}/199{w:>8d}", flush=True)
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t_pub, s_pub, _w = score(lambda f, a, b, xt, mx: chandrupatla(f, a, b, xt,
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mx), probs)
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print(f"{'(published implementation)':30s}{t_pub:>10d}{s_pub:>7d}/199")
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drift = abs(res['own'][0] - t_pub) / t_pub
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print(f"\n control drift {drift * 100:.2f}% - the pluggable version "
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f"must reproduce the published one")
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better = res["credit"][0] < res["own"][0]
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print(f" credit {res['credit'][0]} against its own guard "
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f"{res['own'][0]} => "
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f"{'the safeguard beats the state of the art' if better else 'IT DOES NOT'}")
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if __name__ == "__main__":
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main()
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