vvsandClaude Opus 5
68df1d25db
Impact, every sweep regenerated: the paper's key configurations are unchanged to the evaluation (proposed 2806/3220, credit 3568/3020, Chandrupatla, TOMS 748, Brent, bisection); 46 stated numbers move (ITP CHA 2857 -> 2896, ITP oracle 2611 -> 2647, record CHA figures by 14-18, the grid means by ~1%) and one finding weakens: the measured-order exponent of section 7.1 was cheaper in 29 of 60 far cells and 7 of 7 safeguards; with the fix 12 of 61 and 6 of 7, and on the wide-bracket set it now loses in every safeguard and drops 5 problems. PREREG G1-G3 outcome on the fixed code: G2 (repeating step pattern, period from the data) PASSES Y1-Y4 and is ACCEPTED by the stop rule - proposed rule APS 2806 -> 2782 (below Chandrupatla's 2787), CHA 3220 -> 3169, both 6026 -> 5951, per problem vs Chandrupatla on APS 95/20/38 p = 8e-7. G1 fails Y1/Y2 (its Y3 miss was the sign bug), G3 fails Y2, A fails Y2. Regression 887 pass; the two red tests are the paper-number check (awaiting the paper update) and test_blend (fixed since, copy in stage16). Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
240 lines
10 KiB
Python
240 lines
10 KiB
Python
"""SIM root finding - the similarity-induced step, in the formulation of
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"Parameter-Free Similarity Control" (Sitnikov).
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Mapping of the article's machinery onto root finding
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----------------------------------------------------
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article here
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------- ----
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input x the target value y* = 0 ("the expectation")
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prototype c_k an evaluated value f(x_k) (value space)
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prototype memory y_k the abscissa x_k that produced it
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similarity s(x, c_k) similarity of f(x_k) to zero
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softmax weights w_k eq. (2)
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prediction sum w_k y_k the root estimate, eq. (3)
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So a root finder IS the article's controller run backwards: it is asked "which
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x is associated with the outcome 0?" and answers by normalized similarity
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aggregation over its memory of evaluated points.
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Two levels of prototype are implemented:
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POINT prototypes x_hat = sum_k w_k x_k
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Direct transcription of eq. (3). Exactness note: on a linear f this
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recovers the root exactly only for the inverse-distance similarity;
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every other similarity function biases it. Measured, not assumed.
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PAIR prototypes x_hat = sum_ij w_ij r_ij , r_ij = secant root of (i,j)
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Each bracketing PAIR of memory points is a local rule ("if the outcome
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changes sign between x_i and x_j then the root is near r_ij"), exactly
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parallel to a fuzzy rule table entry. This is the level at which the
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method departs from anything classical: regula falsi is the special
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case of a single pair, and the stale-endpoint failure of regula falsi
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disappears because a pair containing a large-|f| stale endpoint is by
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construction dissimilar to the target and is down-weighted.
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Parameter freedom
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-----------------
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Similarity needs a scale in value space. It is taken from the data in play -
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the largest |f| currently in memory - never from a chosen constant. There is
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no sigma, no temperature, no threshold, no tuned coefficient anywhere below.
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"""
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from __future__ import annotations
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from .baselines import _opposite, _same_sign # 2026-09-21 fix
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import math
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from .baselines import Counter, _Budget
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EPS = 2.220446049250313e-16
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# --------------------------------------------------------------------------
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# similarity functions on the value axis, all taking already-normalised
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# residuals z = f / scale and returning a similarity in a comparable range
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# --------------------------------------------------------------------------
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def sim_neg_sq(z):
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"""Negative squared Euclidean distance to the target, then softmax.
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This is the article's default (eq. 6) and its benchmark choice."""
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return -z * z
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def sim_neg_abs(z):
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"""Negative distance to the target, then softmax."""
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return -abs(z)
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def sim_inv(z):
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"""Inverse distance. Returned as a log-similarity so the caller's softmax
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reproduces plain inverse-distance weights. This is the unique member of
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the family that is exact on a linear f (see module docstring)."""
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return -math.log(abs(z) + EPS)
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def sim_inv_sq(z):
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return -2.0 * math.log(abs(z) + EPS)
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def sim_cosine(z):
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"""Cosine similarity between the 2-vectors (1, f_k) and (1, 0), i.e. the
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article's eq. (4) applied to the value axis lifted to 2-D."""
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return 1.0 / math.sqrt(1.0 + z * z)
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SIMILARITIES = {
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"neg_sq": sim_neg_sq,
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"neg_abs": sim_neg_abs,
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"inv": sim_inv,
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"inv_sq": sim_inv_sq,
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"cosine": sim_cosine,
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}
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def _softmax(scores):
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m = max(scores)
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ex = [math.exp(s - m) for s in scores]
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tot = sum(ex)
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if tot == 0.0 or not math.isfinite(tot):
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n = len(ex)
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return [1.0 / n] * n
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return [e / tot for e in ex]
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# --------------------------------------------------------------------------
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# SIM root finder
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# --------------------------------------------------------------------------
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def sim_root(f, a, b, xtol, maxfev, similarity="neg_sq", prototypes="pair",
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memory=None, guard=False):
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"""Similarity-induced bracketed root finder.
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similarity : key into SIMILARITIES
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prototypes : "point" - eq. (3) verbatim, prototypes are evaluated points
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"pair" - rule-level prototypes, similarity on the value axis
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"pair2d" - rule-level prototypes, similarity on the value axis
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AND the abscissa axis (support tightness)
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memory : None = unbounded memory of every evaluated point;
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an int k = keep only the k most recent points.
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Unbounded is the default; the truncation is an ablation knob,
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not a tuned parameter.
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guard : enable the parameter-free safeguard. A similarity step is
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taken only while the bracket keeps halving; the moment a
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whole halving has failed to happen, one bisection step is
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forced. The only constant involved is 1/2, which is the
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definition of bisection rather than a tuned value, and it
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bounds the worst case at twice bisection. Compare: Brent,
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Ridders, TOMS748 and ITP all need a safeguard here too, and
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ITP's carries three tuning constants (kappa1, kappa2, n0).
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"""
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sfun = SIMILARITIES[similarity]
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two_d = prototypes == "pair2d"
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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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# memory of every evaluated point
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mem = [(a, fa), (b, fb)]
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guard_width = abs(b - a) # width at the last acknowledged halving
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pending = False # a similarity step has been tried since then
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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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width = hi - lo
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if guard:
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if width <= 0.5 * guard_width:
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guard_width = width # the halving happened; nothing owed
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pending = False
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elif pending:
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# a similarity step was already spent without halving the
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# bracket -> pay it back with one bisection step
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x = 0.5 * (a + b)
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fx = g(x)
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mem.append((x, fx))
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pending = False
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if fx == 0.0:
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return x, g.n, True
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if _opposite(fa, fx):
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b, fb = x, fx
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else:
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a, fa = x, fx
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continue
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pending = True
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# the memory is the WHOLE evaluation history, including points that
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# have fallen outside the current bracket: a stale point is not
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# deleted, it is merely dissimilar to the target and therefore
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# carries little weight. (Restricting the memory to the bracket
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# leaves exactly the two endpoints and collapses the method onto
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# regula falsi - verified in stage 1.)
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live = mem if memory is None else mem[-memory:]
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scale = max(abs(fi) for (_, fi) in live)
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if scale == 0.0:
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return live[0][0], g.n, True
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if prototypes == "point":
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scores = [sfun(fi / scale) for (_, fi) in live]
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w = _softmax(scores)
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x_new = sum(wi * xi for wi, (xi, _) in zip(w, live))
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else: # pair prototypes
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cand, scores = [], []
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for i in range(len(live)):
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xi, fi = live[i]
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for j in range(i + 1, len(live)):
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xj, fj = live[j]
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if fj == fi:
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continue
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r = (xi * fj - xj * fi) / (fj - fi)
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if not math.isfinite(r) or not (lo < r < hi):
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# a rule whose conclusion falls outside the proven
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# bracket is inadmissible, not merely unlikely
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continue
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cand.append(r)
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# a pair is similar to the target when BOTH of its
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# endpoints produced outcomes similar to the target
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sc = sfun(fi / scale) + sfun(fj / scale)
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if two_d:
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# ...and when its support is tight. Without this
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# term a flat tail (|f| ~ 1e-16 at 39 units from
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# the root) looks maximally similar to the target
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# and the method stalls there - measured, not
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# assumed. Both axes are normalised by the data
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# in play (largest |f| in memory; current bracket
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# width), exactly as the article normalises its
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# input space so that each axis contributes
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# equally.
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sc += sfun(abs(xi - xj) / width)
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scores.append(sc)
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if not cand:
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x_new = 0.5 * (a + b)
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else:
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w = _softmax(scores)
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x_new = sum(wi * ri for wi, ri in zip(w, cand))
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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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# never propose a point already in memory (would waste the budget)
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if any(x_new == xi for (xi, _) in mem):
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x_new = 0.5 * (a + b)
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if any(x_new == xi for (xi, _) in mem):
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# the bracket can no longer be split in floating point.
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# Report success only if it is actually inside the
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# tolerance; otherwise this is stagnation and must be
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# reported as a failure, not counted as a win.
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return x_new, g.n, abs(b - a) <= xtol
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x = x_new
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fx = g(x)
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mem.append((x, fx))
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if fx == 0.0:
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return x, g.n, True
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if _opposite(fa, fx):
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b, fb = x, fx
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else:
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a, fa = x, fx
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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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