Title: 'Root Finding by Similarity and Analogy: A Bracketing Method with a Data-Selected Decision Layer'. Abstract 246 words. Main text 10.4k words (was 17.4k): search reports moved to Appendices A (old 6-7), B (wall-clock), C (negative results); revision history removed; pronouns for Chandrupatla's method fixed; tables numbered in order. New: Algorithm 2 (complete A'), the decision layer counted honestly in 6.5, Code and data availability, Appendix D (30 pre-registrations against the first commit of their own results, read from git by check_prereg_order.py: 25 before, 4 same commit, 1 decided on the smoke; A' frozen at 8027d7f between corpus 4's definition 657df10 and its results 6d2531e). Literature: Bus & Dekker 1975, Fernandez-Diaz & Menendez-Perez 2023 (two dimensionless parameters; SFRFm on sign(f)|f|^(1/m) with known multiplicity - the nearest precursor of the split), Cordero-Neta-Torregrosa 2021; Illinois and Ridders cited. verify_article: explicit check that every head-to-head row sums to its set. check_new_numbers.py: no number new to the text except headings, references, hashes. Server: verify 527 OK / 0 BAD, regression x2 1232 passed, PDF 30 pages, 0 errors, 0 undefined, 0 overfull.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
"""The abstract within 250 words (revision): same claims and numbers, fewer words.
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PATH="article/sim_numerics.md"
NEW="""We replace bisection's midpoint rule by a similarity rule — the step grows with how dissimilar $f(x)$ is to the target 0, under a similarity $S$ with $S(0)=1$ — and derive its contraction factor in closed form. The derivation fixes a base rule before any benchmark is consulted: with the tent similarity and a three-point divided-difference scale it is a Newton step of Muller's order 1.839, clipped to the bracket and projected onto a budget of twice bisection whose multiplier is its one numerical constant. On the 154 problems of Alefeld, Potra and Shi it is level with TOMS 748 and 1.5% behind Chandrupatla's method; on wide brackets it is clearly behind. The gap is a decision, when to give the model up, and we take it by analogy: at a flat root the dimensionless configuration of the last three points recurs under zoom, at a simple root it tends to a line, and the analogous pair gives the order that says whether and where to split. This decision layer, about ten discrete rules selected on two development sets, gives the method A′. On 240 problems fixed before its code was frozen it needs 3656 evaluations against 5717 for Chandrupatla, 5535 for ModAB and 4488 with Lagouanelle's multiplicity estimate in place of the analogy; the lead lies in multiple and fractional roots, present in two of the six families. With the multiplicity known, the classical transformation $\\operatorname{sign}(f)|f|^{1/m}$ is 2.4 to 6.8 times cheaper on multiple roots."""
"""Revision 1, step 3b: the main text shortened. Each paragraph below is replaced by a
shorter one that keeps its claim and every number it keeps verbatim (verify_article.py checks the
data; nothing new is claimed). Exact anchors, each found exactly once, or nothing is written.
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from__future__importannotations
PATH="article/sim_numerics.md"
R=[]
defE(old,new):
R.append((old,new))
E("That is the false-position formula. Two things must be said about how far it reaches, because the obvious generalisation is false.\n\nIt is specific to that similarity. On $f=x^2-2$ over $[1,4]$, where false position gives 1.200000, the same construction gives 1.206812 for $e^{-|z|}$, 1.015228 for $1/(1+z^2)$, 1.007624 for $1/\\sqrt{1+z^2}$ and 1.214286 for the truncated linear form: one of five reproduces it. So \"a similarity rule scaled by the bracket is not a new method\" would be too broad a conclusion, and this paper's own Table 3 refutes it — bracket-scaled configurations take third, fourth and sixth place on Chandrupatla's set, where false position solves 6 of 45.",
"That is the false-position formula, and it is specific to that similarity: on $f=x^2-2$ over $[1,4]$, where false position gives 1.200000, $e^{-|z|}$ gives 1.206812, $1/(1+z^2)$ 1.015228, $1/\\sqrt{1+z^2}$ 1.007624 and the truncated linear form 1.214286. Bracket-scaled similarity configurations take third, fourth and sixth place on Chandrupatla's set (Table 3), where false position solves 6 of 45.")
E("Together the two say where the boundary of novelty runs, and it is not where one would guess: it runs through the words *most recent* in the definition of $\\hat f'$ in §3. A divided difference over the bracket ends is three centuries old; the same difference over the most recent iterates is the secant.",
"Together the two say that the boundary of novelty runs through the words *most recent* in the definition of $\\hat f'$ in §3: a divided difference over the bracket ends is false position, the same difference over the most recent iterates is the secant.")
E("The last row is not a slow rate but an immediate failure, and it matters because that similarity is the one this paper goes on to propose. At $x_0=0$ the residual is $z=-1$, so $S=\\max(0,1-1)=0$, the step is the whole bracket, the iterate lands exactly on $x_\\mathrm{far}$ and never leaves: the error is 0.5858 after one step and after two hundred. **The failure is a property of the chosen scale, not of the similarity.** Under the derived scale of §3 the residual $|f|/\\sigma$ stays below 1 — on this problem the clip does not engage on a single one of the seven steps the method takes — the truncation never fires, and the same function is the best of the nine (Table 4). This is the paper's own thesis, that the content sits in the scale and not in the shape of $S$, showing up as a discontinuity in the behaviour of one function.",
"The last row fails at once, and it matters because that similarity is the one this paper proposes: at $x_0=0$, $z=-1$, so $S=0$, the step is the whole bracket and the iterate stays at $x_\\mathrm{far}$ (error 0.5858 after one step and after two hundred). The failure belongs to the chosen scale, not to the similarity: under the derived scale of §3 the clip never engages on this problem, and the same function is the best of the nine (Table 4).")
E("Corollaries 1–3 fix every choice in the method before any benchmark is consulted. We record them here as a prediction, and Section 5.2 tests it against the full grid. One number in the safeguard is not fixed by them, and §3.1 declares it as the method's one free constant.",
"Corollaries 1–3 fix every choice before any benchmark is consulted; Section 5.2 tests the prediction against the full grid. One number in the safeguard is not fixed by them, and §3.1 declares it.")
E(" It is not the ratio form $1/(1+|z|)$, which is a separate row of every table here; the two are distinguished throughout because Proposition 1 applies to the ratio form and Table 1's last row to the tent.",
" It is not the ratio form $1/(1+|z|)$ of Proposition 1.")
E("a Newton step with a three-point divided-difference derivative, clipped to the bracket and then projected as in §3.1. That the tent turns the similarity step into Newton's is a property of the similarity chosen, not a sign that the similarity has dropped out: which $S$ stands in the step decides the step, and it is measured, not assumed — Table 4 ranks the nine functions over the whole grid, the readings of §A.2 pay most with the tent in the step and some of them lose with other functions (§A.2), and in the proposed method replacing the tent by any other function costs 19% to 425% (Table 7).\n\nWe state the reduction ourselves rather than leave it to be discovered, because it locates the contribution precisely. The base rule is not a new family of methods. It is (i) a derivation that fixes every choice — which similarity, which scale, which safeguard — before any benchmark is consulted, and predicts which similarity functions come first; (ii) a safeguard of ITP's projection type with ITP's truncation stage, and hence all three of its constants, removed; (iii) the negative results of Appendix C; (iv) the measurements. We call it the **base rule**; the method this paper proposes builds on it in Section 6.",
"a Newton step with a three-point divided-difference derivative, clipped to the bracket and then projected as in §3.1. That the tent turns the similarity step into Newton's is a property of the chosen similarity, and which $S$ stands in the step is measured rather than assumed: in the proposed method, replacing the tent by any other function costs 19% to 425% (Table 7). The base rule is therefore not a new family of methods; what is ours is a derivation that fixes every choice before any benchmark is consulted, and a safeguard of ITP's projection type with ITP's truncation stage — and all three of its constants — removed. The proposed method builds on it in Section 6.")
E("On the canonical set the projection binds on one problem only once $c\\ge1.25$ — `aps.13.00`, whose $f$ underflows to zero around the root (20, 30 and 28 evaluations at $c=1.25$, 1.5 and 2) — so the total barely moves. On the wide-bracket set it moves monotonically with $c$ from 1.25 on, and 2 is not the best value. Nor is zero slack ($c=1$) a degenerate case under which the guard behaves as bisection: it costs 4625 and 2039, far from bisection's 7338 and 2136, and on the wide-bracket set it beats $c=2$. The multiplicative family has no member that the bound singles out, so its multiplier is a genuinely free constant.",
"On the canonical set the projection binds on one problem only once $c\\ge1.25$ — `aps.13.00`, whose $f$ underflows to zero around the root (20, 30 and 28 evaluations at $c=1.25$, 1.5 and 2). On the wide-bracket set the cost rises monotonically with $c$ from 1.25 on, and 2 is not the best value; zero slack ($c=1$) is not degenerate either (4625 and 2039, against bisection's 7338 and 2136). No member of the family is singled out by the bound: the multiplier is a free constant.")
E("A looser promise buys evaluations, as it must; within one promise the cheapest endowment is determined. The constant-free variant is therefore the earned projection with $C_0=1$, whose guarantee — $\\lceil\\log_2(w_0/\\varepsilon_x)\\rceil+4$ evaluations, bisection plus one step with one more for the floating-point boundary — is verified on all 199 problems.\n\nWe nonetheless propose the scheduled projection with $c=2$ and declare its multiplier. On the canonical set it is 743 evaluations cheaper than the earned variant (2634 against 3377), and there its total barely depends on the multiplier; on the wide-bracket set the earned variant is the cheaper (2073 against 2357). The two are the ends of a trade stated in numbers: one declared numerical constant and the better canonical result, or none, a tighter bound and the better wide-bracket result. The value 2 was fixed before any benchmark was consulted.",
"Within one promise the cheapest endowment is determined, so the constant-free variant is the earned projection with $C_0=1$; its guarantee, $\\lceil\\log_2(w_0/\\varepsilon_x)\\rceil+4$ evaluations, is verified on all 199 problems. We nonetheless propose the scheduled projection with $c=2$, fixed before any benchmark was consulted: it is 743 evaluations cheaper on the canonical set (2634 against 3377), the earned variant is cheaper on the wide-bracket set (2073 against 2357) — one declared numerical constant and the better canonical result, or none, a tighter bound and the better wide-bracket result.")
E("**Fairness to the competitors.** Three deliberate concessions.\n\n1. ITP is natively written against a half-width $\\epsilon$ and terminates on $b-a\\le2\\epsilon$; feeding the common tolerance in as $\\epsilon$ would let it stop at twice its competitors' bracket width, so $\\epsilon=\\varepsilon_x/2$ is used.\n2. ITP is reported both at the authors' recommended constants and as a *per-problem oracle*, the best of a $(\\kappa_1,n_0)$ grid chosen separately for every problem — not an achievable method, included so that the state of the art cannot be said to have been handicapped.\n3. Our implementation of ITP is validated against the method's own theorem, that it never needs more than $n_{1/2}+n_0$ iterations, on all 154 problems for $n_0\\in\\{0,1,2,5\\}$.\n\n4. Where SciPy ships the method, the competitor **is the library**: Chandrupatla is `scipy.optimize.elementwise.find_root`, Brent is `brentq`, TOMS 748 is `toms748`, each run with the same stopping rule as every other method — a bracket no wider than $\\varepsilon_x$, the relative term at machine precision, no stopping on $|f|$ — and each evaluation counted by the same wrapper around $f$. Our own implementations of Chandrupatla's and Brent's methods agree with the libraries; the tables use the libraries. ITP has no library implementation, which is why it is checked against its own theorem instead; bisection and the false-position family are not in SciPy either and are ours.",
"**Competitors.** Where SciPy ships the method, the competitor is the library — Chandrupatla is `scipy.optimize.elementwise.find_root`, Brent `brentq`, TOMS 748 `toms748` — run with the stopping rule of every method (a bracket no wider than $\\varepsilon_x$, the relative term at machine precision, no stopping on $|f|$) and counted by the same wrapper around $f$. ITP, which has no library implementation, is ours, run with $\\epsilon=\\varepsilon_x/2$ because it terminates on $b-a\\le2\\epsilon$, checked against its own theorem ($n_{1/2}+n_0$ iterations at most, on all 154 problems for $n_0\\in\\{0,1,2,5\\}$), and reported both at the authors' constants and as a *per-problem oracle* over a $(\\kappa_1,n_0)$ grid — not an achievable method, included so that it cannot be said to have been handicapped. Bisection and the false-position family are ours.")
E("It wins twice as many problems as it loses against Chandrupatla and trails that method in total because its losses are the larger ones; the problems are not independent (§7), and taken by function family the comparison is 6/1/8 of 15 families, a tie. Dekker and Illinois solve 153 of 154 and are ranked after every method that solves all. The constant-free variant goes 79/3/72 against Chandrupatla ($p=0.63$, a tie in problems), 78/9/67 against TOMS 748 and 79/5/70 against Brent, and loses on the total to all three.",
"It trails Chandrupatla in total because its losses are the larger ones; by function family, the unit in which these problems are independent (§7), the comparison is 6/1/8 of 15 families, a tie. The constant-free variant goes 79/3/72 against Chandrupatla ($p=0.63$), 78/9/67 against TOMS 748 and 79/5/70 against Brent.")
E("### 5.1 What the guarantee costs\n\nIt depends on which bound is bought. Against the identical rule with no safeguard at all:\n\n| | solved | total evals | runs over the $2\\times$ bound |\n|---|---|---|---|\n| APS, no safeguard | 154/154 | 2634 | 0 |\n| APS, projection — base rule ($2\\times$ bisection) | 154/154 | 2634 | 0 |\n| APS, earned projection — constant-free (bisection + 1 step) | 154/154 | 3377 | 0 |\n| Chandrupatla, no safeguard | 39/45 | 3391 | 10 |\n| Chandrupatla, projection — base rule | 45/45 | 2357 | 0 |\n| Chandrupatla, earned projection — constant-free | **45/45** | **2073** | **0** |\n\nThe projection of the base rule never binds on the canonical set, so its totals are identical to the last evaluation; on the wide-bracket set it solves six more problems for 30% fewer evaluations. The earned projection promises twice as much — a worst case of bisection plus one step instead of twice bisection — and pays for it on the canonical set, 743 evaluations or 28%, while on the wide-bracket set it is the cheapest of the three. Both are the opposite of the usual trade on at least one set, and that is the point of projecting rather than replacing: the two safeguards that replace cost 7068 and 7046 on Alefeld–Potra–Shi, against 2634.",
"### 5.1 What the guarantee costs\n\nAgainst the same rule with no safeguard (Tables 2 and 3), the projection costs nothing on the canonical set and on the wide-bracket set solves six more problems for 30% fewer evaluations (2357 against 3391; unguarded, 10 runs break the bound). The earned projection, promising bisection plus one step, pays 743 evaluations (28%) on the canonical set and is the cheapest of the three on the wide-bracket set. That is the point of projecting rather than replacing: the two safeguards that replace cost 7068 and 7046 on Alefeld–Potra–Shi, against 2634.")
E("Table 4 reports it. For every data-derived scale the three similarities with $c=1$ occupy the top three places and the three with $c=0$ the bottom three. That $c=0$ is slow is the unsurprising half — a step proportional to $z^2$ is damped quadratically. For the chosen scale the two groups are indistinguishable (14983 against 14228), which is consistent — with a chosen scale nothing converges well and the ordering carries no information.",
"For every data-derived scale the three similarities with $c=1$ occupy the top three places of Table 4 and the three with $c=0$ the bottom three (that $c=0$ is slow is the unsurprising half: a step proportional to $z^2$ is damped quadratically). With the chosen scale the two groups are indistinguishable (14983 against 14228), as they should be.")
E(" The group order and the order of the scales are those the law predicts and were predicted before the grid was run; the order inside each group is measured, not predicted.",
" The group order and the order of the scales were predicted before the grid was run; the order inside each group is measured.")
E("§A.6 left one decision open: *when to give the model up*. On the pure powers `fun3`–`fun6` the right move is to halve, Chandrupatla's method halves on up to all of its steps, and every reading of a single state measured so far confuses \"the model is useless here\" with something else. This section closes that decision, and the reading that closes it is not a similarity of the current state to a prototype but a similarity of the run to *its own past* — an analogy in Pólya's sense [8], two situations agreeing in more than one relation. Every rule below was pre-registered before its code and its run, with a stop rule; the rejected ones are listed in Appendix C with their numbers.",
"The base rule's gap on wide brackets is a decision, *when to give the model up* (§A.6): on the pure powers `fun3`–`fun6` the right move is to halve, Chandrupatla's method halves on up to all of its steps, and no reading of a single state measured takes that decision correctly. This section takes it by a similarity of the run to *its own past* — an analogy in Pólya's sense [8], two situations agreeing in more than one relation. Every rule below was pre-registered with a stop rule (Appendix D); the rejected ones are in Appendix C.")
E(" The constant is bisection's $\\tfrac12$, carried through the formula. The relation between the order and the contraction, $e(1-1/m)$, is the one behind the classical estimates of multiplicity from successive Newton iterates [14, 15]; here it is not used to estimate $m$ but to set the point at which a model step has lost to a halving.",
" The constant is bisection's $\\tfrac12$, carried through the formula; the contraction $e(1-1/m)$ is the one behind the classical multiplicity estimates from successive Newton iterates [14, 15], used here to set the point at which a model step has lost to a halving.")
E("The flag sees the regime — on the flat families it rises on most model steps — but acted upon step by step it does not pay. Replacing the one flagged step by a halving costs the canonical set 2634 → 2839, because a simple root far from convergence also breaks its promises (the flag rises at least once on 142 of the 154 canonical problems). Every refinement of *when* to halve that reads the moment alone — twice in a row, the promise together with a creep in one direction, a halving that sticks once begun, a judgment of the evaluated point against the line through the bracket — trades one set for the other (Appendix C, items 12–14).",
"Acted upon step by step the flag does not pay: replacing each flagged step by a halving costs the canonical set 2634 → 2839, because a simple root far from convergence also breaks its promises (the flag rises at least once on 142 of the 154 canonical problems). Every refinement that reads the moment alone trades one set for the other (Appendix C, items 12–14).")
E("Formula (6) is the log–log slope by which any power-law exponent is estimated, and has the shape of the computational order of convergence and its approximation from differences of iterates [18, 19]. What is not classical is *which* two states it compares: not consecutive iterates, but a pair the similarity of their configurations has found. Under an exact zoom a pure power of order $m$ reads exactly $m$; along a real run the analogous pairs are only nearly homothetic and the reading scatters about the order, but the regime asks only whether it reaches 2, the order from which a tangent step loses to a halving (§6.1) — no new constant.",
"Formula (6) is the log–log slope of a power law, with the shape of the computational order of convergence [18, 19]; what it compares is not consecutive iterates but a pair the similarity of their configurations has found. A real run's analogous pairs are only nearly homothetic and the reading scatters about the order, but the regime asks only whether it reaches 2, the order from which a tangent step loses to a halving (§6.1).")
E("**The analogy belongs to the run, the flag to the moment.** In the cycle the base rule runs on a pure power — undershoot, undershoot, crossing, a nearly empty step — the flag rises in the phases whose configuration does *not* recur (the empty step grows emptier every cycle), while the recurring phases have the flag down. Requiring both in one state delayed the entry on the flat problems to their thirteenth state on average. Once established, the analogy is therefore remembered for the run, and the regime is entered at the next unkept promise (eighth state on average).",
"**The analogy belongs to the run, the flag to the moment.** On a pure power the flag rises in the phases of the base rule's cycle whose configuration does *not* recur, so requiring both in one state delayed the entry to the thirteenth state on average. Once established, the analogy is remembered for the run and the regime is entered at the next unkept promise (eighth state on average).")
E(" A high power far from its root is locally an exponential, $x^m=e^{m\\ln x}$, and it was there, not on the flank of `fun9` it was built for, that the limit paid.",
" It pays on high powers far from their root, which are locally exponential, $x^m=e^{m\\ln x}$.")
E(" The order read from a *failed promise*, $(1-1/m)^m=r$, was tried first and gave $m=\\infty$ in 163 of 174 entries, because the base step is not a pure tangent; the order read from the *analogy* is the one that works.",
" (The order read from a failed promise, $(1-1/m)^m=r$, gives $m=\\infty$ in 163 of 174 entries, because the base step is not a pure tangent.)")
E(" It is a relative of Brent's acceptance condition [2], which admits an interpolant only between the best point and $(3a+b)/4$: both refuse a model that points towards the worse end of the bracket.",
" It is a relative of Brent's acceptance condition [2]: both refuse a model that points towards the worse end of the bracket.")
E(" The exit judgment of §6.2 separates a flat root from a simple one; at a root of order below one $f$ is far from 0 on both sides and the exit fires falsely, so the memory of this branch is *not* cleared by it; clearing it was measured on the development corpora and was worse (recorded in the pre-registration amendment of this branch).",
" At a root of order below one $f$ is far from 0 on both sides and the exit fires falsely, so the memory of this branch is *not* cleared by the exit (clearing it was worse on the development corpora).")
E(" Dekker [9] and Brent [2] step at least the tolerance; we do the same, $\\varepsilon_x/2$ towards the far end, when the model's step is shorter — but only after the model has *kept* its previous promise ($r<\\tfrac14$), because at the start of a run a slope dominated by a huge far value promises the root at an end. This element is not ours and is named as such.",
" Dekker [9] and Brent [2] step at least the tolerance; A′ steps $\\varepsilon_x/2$ towards the far end when the model's step is shorter, but only after the model has *kept* its previous promise ($r<\\tfrac14$). This element is borrowed.")
E(" The earned-budget form keeps its constant-free guarantee of bisection plus one step and pays for it as in §3.1.","")
E("Every component is positive in sum; the regime and the split carry the method. Two are marginal and we say so: the step pattern earns on two corpora of six and loses on four, and the sign contradiction is within noise (+28 over 855 problems). Removing both would change A′'s total by under one per cent (117 of 13032 evaluations); they are kept because the pre-registered rule removes only a component that earns nowhere.",
"Every component is positive in sum; the regime and the split carry the method. The step pattern (earns on two corpora of six) and the sign contradiction (+28 over 855 problems) are marginal — together under one per cent, 117 of 13032 evaluations — and are kept because the pre-registered rule removes only a component that earns nowhere.")
E("Several mechanisms of A′ coincide with classical ones, and the coincidence is worth stating, because they were not taken from the literature but reached from the principles of the paper — similarity to a prototype and analogy as similarity of relations — and recognised afterwards:",
"Several mechanisms of A′ coincide with classical ones, reached from similarity and analogy and recognised afterwards:")
E("1. **New corpus 1** — 206 problems: SciPy's `original` set (6) and five generated classes of 40 (seed 20260921): smooth simple roots, transcendental simple roots, multiple roots $|x-r|^p$ with $p\\sim U(2,20)$, exponential flanks, and near-cancelling terms of the `fun9` kind.\n2. **Roots.jl / ModAB** — the published bracketing benchmark of the Julia package Roots.jl [12], taken from the ModAB benchmark of Ganchovski and Traykov [10]: 91 problems, of which 13 coincide with APS or Chandrupatla functions (a seven-point identity test) and are excluded, leaving 78, many of them discontinuous or pathological.\n3. **Applied and GSL** — the 10 bracketed problems of the GNU Scientific Library's root-finding test suite [11] and 122 equations from physics and engineering on grids fixed beforehand: Kepler's equation (eccentricity up to 0.999), Colebrook–White, Lambert's W, Planck/Wien, van der Waals for CO₂ (including near-critical), internal rate of return, the finite square well.",
"1. **New corpus 1** — 206 problems: SciPy's `original` set (6) and five generated classes of 40 (seed 20260921): smooth and transcendental simple roots, multiple roots $|x-r|^p$ with $p\\sim U(2,20)$, exponential flanks, near-cancelling terms.\n2. **Roots.jl / ModAB** — the bracketing benchmark of Roots.jl [12] used by ModAB's authors [10]: 91 problems, 13 of which coincide with APS or Chandrupatla functions and are excluded, leaving 78.\n3. **Applied and GSL** — the 10 bracketed problems of the GNU Scientific Library's test suite [11] and 122 equations from physics and engineering on grids fixed beforehand (Kepler, Colebrook–White, Lambert's W, Planck/Wien, van der Waals, internal rate of return, the finite square well).")
E("ModAB, the most recent bracketing method we know of, is run from its authors' package (`pymodab` 1.0.9, a wrapper of their C code) with the stopping rule of every other method — a bracket no wider than $\\varepsilon_x$, the relative term at machine precision, no stopping on $|f|$ — and counted by the same wrapper around $f$, whose count agrees with the package's own. Where roots are not known in closed form, and for uniformity on all six corpora, a run is solved when $f$ changes sign within $10^{-6}$ of its answer; the $x$-tolerance, the budget and the charge for a failure are those of §4.",
"ModAB, the most recent bracketing method we know of, is run from its authors' package (`pymodab` 1.0.9) with the stopping rule and the counting of every other method. The criterion, tolerance, budget and charging are those of §4.")
E("**The history is part of the result.** The variant carried to corpora 1–3 was A′ without the tolerance step and the overshoot branch. It transferred to corpus 1 by every pre-registered criterion (total 10% below Chandrupatla, per problem 105/34/67, $p=0.005$), did *not* transfer to Roots.jl (total 6.5% below, but per problem 38/10/30, $p=0.4$) and lost on the applied corpus (14% above). The losses there were traced to two gaps of the base step — no closing of the bracket once the root is reached, and no recognition of roots of order below one — which became the last two readings of §6.4; they were developed on the seen corpora and tested once, on corpus 4.",
"The variant carried to corpora 1–3 was A′ without the tolerance step and the overshoot branch. It transferred to corpus 1 by every pre-registered criterion (per problem 105/34/67 against Chandrupatla, $p=0.005$), did *not* transfer to Roots.jl (38/10/30, $p=0.4$) and lost on the applied corpus; the two readings that closed those gaps were developed on the seen corpora and tested once, on corpus 4.")
E("Against ModAB it is ahead on every corpus, in total and problem by problem ($p\\le0.0013$), on the one test of corpus 4 by 3656 to 5535 and 183 problems to 34 ($p=7\\cdot10^{-26}$), and on ModAB's own benchmark as well — all 91 problems of it, 1507 against 1588, 56/13/22 ($p=1.5\\cdot10^{-4}$); ModAB exceeds that bound, which it does not promise either, on 9 problems. Against Chandrupatla it is ahead in total on five corpora and level on the sixth, and ahead problem by problem on all six, significantly on five. The size of the lead is not uniform: it is large on multiple, fractional and nearly flat roots — the regime §6 recognises — and small on simple roots, where it is a matter of per cent. Where it is behind is named: `fun9` (65 against 55), the near-cancelling class of corpus 1 (491 against 391), random polynomials with a simple root in total (433 against 419, though ahead 20 to 12 in problems), and the smooth equations of the applied corpus, where the two are level.",
"Against ModAB it is ahead on every corpus, in total and problem by problem ($p\\le0.0013$) — on corpus 4 by 3656 to 5535 and 183 problems to 34 ($p=7\\cdot10^{-26}$), on all 91 problems of ModAB's own benchmark by 1507 to 1588 (56/13/22, $p=1.5\\cdot10^{-4}$); ModAB, which does not promise the bound either, exceeds it on 9 problems. Against Chandrupatla it is ahead in total on five corpora and level on the sixth. The lead is large on multiple, fractional and nearly flat roots and small on simple roots. It is behind on `fun9` (65 against 55), the near-cancelling class of corpus 1 (491 against 391) and random polynomials with a simple root in total (433 against 419), and level on the smooth equations of the applied corpus.")
E("A root finder can be derived from a similarity function alone, and the derivation shows where the content of such a method actually sits. It is not in the shape of $S$ — nine shapes were tried and which group of them comes first is dictated by a single number, the slope of $1-S$ at the origin. It is not in making the similarity change faster — that overshoots exactly as readily as flatness stagnates. It is in the *scale* against which similarity is judged: chosen, the method is brittle and merely linear; taken from the bracket, it is false position and three centuries old; read off the data, it is superlinear. And the safeguard that makes such a method safe must project the step, not replace it: replacing costs the order, projecting costs nothing on one benchmark and improves the method on the other. The projection's budget multiplier is a free constant, and we declare it: it does not move the canonical result and it does move the wide-bracket one; projecting onto a budget the steps earn instead removes it, at a measured price.",
"A root finder can be derived from a similarity function alone, and the derivation shows where its content sits: not in the shape of $S$, whose slope at the origin decides which group of functions comes first, but in the *scale* against which similarity is judged — chosen, the method is linear and brittle; taken from the bracket, it is false position; read off the most recent evaluations, it is Newton's step with Muller's order. The safeguard must project the step rather than replace it; the projection's multiplier is a free constant, declared, and a budget the steps earn removes it at a measured price.")
E("The base rule has one declared numerical constant, converges with Muller's order 1.839 and carries a worst case of twice bisection plus one step. On the canonical Alefeld–Potra–Shi set it is level with TOMS 748 in total and 1.5% behind Chandrupatla's method, while winning more problems than it loses against both; on the wide-bracket set Chandrupatla is genuinely stronger (1505 against 2357). §A.6 locates the cause: not the order of the model but the decision to give it up, which Chandrupatla takes by halving and which no reading of a single state takes correctly for our step.",
"The base rule is level with TOMS 748 and 1.5% behind Chandrupatla's method on the canonical set, and clearly behind Chandrupatla on wide brackets (1505 against 2357); the cause is not the order of the model but the decision to give it up.")
E("That decision is a similarity too — of the run to its own past. A pure power repeats its configuration under zoom, an exponential under translation, a simple root tends to the line; the analogous pair reads the local order, the order decides between the model and a halving by bisection's own $\\tfrac12$, and it says where to split. The method built on it, A′, is ahead of Chandrupatla on both canonical sets in total and problem by problem, and on a corpus fixed after its code was frozen it needs 36% fewer evaluations and wins 177 problems to 28.",
"That decision is a similarity too, of the run to its own past: a pure power repeats its configuration under zoom, an exponential under translation, a simple root tends to the line, and the analogous pair reads the order that decides between the model and a split. The method built on it, A′, is ahead of Chandrupatla on both canonical sets, and on a corpus fixed before its code was frozen it needs 36% fewer evaluations and wins 177 problems to 28.")
E("This is the same movement as in parameter-free similarity control [6], where membership functions are eliminated by normalising the input space from the data rather than by choosing widths. Here the object normalised is the value axis, and the price of choosing it by hand is measured rather than asserted.\n\n","")
E("Three questions remain, all narrower than the one this section used to pose.\n\n","")
# Root Finding by Similarity and Analogy: A Bracketing Method with One Declared Constant
# Root Finding by Similarity and Analogy: A Bracketing Method with a Data-Selected Decision Layer
V. Sitnikov
---
## Abstract
Bisection places the next iterate at the midpoint because it uses no information about the function's value. We replace that rule with a *similarity* rule: the step is proportional to how *dissimilar* the current outcome $f(x)$ is to the expectation $0$, measured by a similarity function $S:\mathbb{R}\to[0,1]$ with $S(0)=1$. We derive the contraction factor of the resulting iteration in closed form; it predicts a linear rate to four digits when $1-S$ has a non-zero slope at the origin, and a sublinear $e_n\sim C/n$ with a predicted $C$ when it does not, both confirmed. Three consequences follow. First, with the *ratio* similarity scaled by the far end — and with that combination only, of the five tested — the rule reproduces the false-position formula identically; so does the truncated linear similarity scaled by a divided difference over the *bracket ends*, which shows that the boundary of novelty runs through which points the difference is taken over. Second, any similarity fixed in advance with the scale also fixed yields at best linear convergence at a rate that is a property of the problem rather than of the method. Third, the rate vanishes exactly when the scale equals the local scale of the value axis; reading that scale off the data — a divided difference, not a fitted coefficient — turns the step into $|f|/|f'|$ and the convergence superlinear.
The derivation fixes a *base rule* before any benchmark is consulted: a similarity whose approach to 1 is linear, the finest available data-derived scale, and a safeguard that projects the step onto the admissible interval rather than replacing it. It is, explicitly, a Newton step with a three-point divided-difference derivative, clipped to the bracket and projected onto the bisection budget. The similarity sets that step, and its form is decisive: with the tent the step is Newton's, with any of the other eight functions the proposed method costs from 19% to five times more. Its order is Muller's, 1.839. It carries a proven worst case of twice bisection plus one step and one free numerical constant, the multiplier of that budget, which we declare. On the 154 problems of Alefeld, Potra and Shi it needs 2634 evaluations of $f$ against 2636 for TOMS 748, 2707 for Brent and 2595 for Chandrupatla's method; it wins more problems than it loses against Chandrupatla, 87 to 43, and needs 1.5% more in total because its losses are the larger ones. On the 45 wide-bracket problems of Chandrupatla it beats Brent and TOMS 748 and loses to ITP and to Chandrupatla (2357 against 1946 and 1505). For every data-derived scale the three similarity functions whose $1-S$ has a non-zero slope at the origin come before the three whose slope is zero, as the derived law predicts.
The wide-bracket gap turns out to be not the order of the model but a decision — when to give the model up — and the decision is a similarity after all, of an unfamiliar kind: the similarity of a run to its own past. A pure power is self-similar under zoom, so the dimensionless configuration of the last three points recurs, while at a simple root it tends to the linear prototype; the analogous pair yields the local order, $m=\ln L_f/\ln L_x$, and an exponential, self-similar by translation, is its limit $m=\infty$. Entering a halving regime when that analogy holds with $m\ge2$ — the condition under which a tangent step loses to a halving — splitting at the point the order predicts, and leaving when the split looks linear, together with four smaller readings of the same kind and one borrowed element, gives the method proposed here, A′. On the two sets it needs 2259 and 1074 evaluations against Chandrupatla's 2595 and 1505 and wins more problems than it loses on both, 103 to 31 and 31 to 10. The chain of decisions that produced it was selected on those sets; tested once, after its code was frozen, on 240 problems from six new families, it needs 3656 evaluations against Chandrupatla's 5717 and wins 177 problems to 28 — a corpus the author composed knowing where the method is strong: two of its six families have multiple or fractional roots, and without them the lead is 2403 against 3569, on the simple-root families alone 737 against 747. Against simpler detectors put in the place of the analogy — Lagouanelle's multiplicity estimate with the same memory, Chandrupatla's acceptance band, or none — it wins on that corpus in total and problem by problem (3656 against 4488, 6236 and 6165); against the classical transformation $\operatorname{sign}(f)|f|^{1/m}$ with the multiplicity given in advance it loses every multiple or fractional root, by a factor of 2.4 to 6.8 — the price of estimating the order. Over 855 problems in six corpora it is ahead of Brent, TOMS 748, ITP and the recent ModAB everywhere and stays within its own bound of twice bisection on every problem, a bound that Brent, TOMS 748 and ModAB, which do not promise it, exceed on 118, 84 and 9 problems; against Chandrupatla it is ahead in total on five corpora and level on the sixth. The lead is large on multiple, fractional and nearly flat roots and a matter of per cent on simple ones. Several of the resulting mechanisms coincide with classical ones — Lagouanelle's multiplicity estimate, false position on $|f|^{1/m}$ — reached here from similarity and analogy and recognised afterwards; what we found no counterpart for is the decision layer, where every choice — when to give the model up, where to split, when to return — is a judgment of similarity or of analogy, and the similarity that sets the step, whose form is decisive (replacing it costs 19% to 425%).
We replace bisection's midpoint rule by a similarity rule — the step grows with how dissimilar $f(x)$ is to the target 0, under a similarity $S$ with $S(0)=1$ — and derive its contraction factor in closed form. The derivation fixes a base rule before any benchmark is consulted: with the tent similarity and a three-point divided-difference scale it is a Newton step of Muller's order 1.839, clipped to the bracket and projected onto a budget of twice bisection whose multiplier is its one numerical constant. On the 154 problems of Alefeld, Potra and Shi it is level with TOMS 748 and 1.5% behind Chandrupatla's method; on wide brackets it is clearly behind. The gap is a decision, when to give the model up, and we take it by analogy: at a flat root the dimensionless configuration of the last three points recurs under zoom, at a simple root it tends to a line, and the analogous pair gives the order that says whether and where to split. This decision layer, about ten discrete rules selected on two development sets, gives the method A′. On 240 problems fixed before its code was frozen it needs 3656 evaluations against 5717 for Chandrupatla, 5535 for ModAB and 4488 with Lagouanelle's multiplicity estimate in place of the analogy; the lead lies in multiple and fractional roots, present in two of the six families. With the multiplicity known, the classical transformation $\operatorname{sign}(f)|f|^{1/m}$ is 2.4 to 6.8 times cheaper on multiple roots.
---
## 1. Introduction
A bracketing root finder must decide, at every step, where inside the bracket to evaluate next. Bisection answers "in the middle", which is the correct answer when nothing is known. Every improvement since has answered "where a local model of $f$ says the root is": a secant in false position, an inverse quadratic in Brent [2], a cubic in TOMS 748 [1], an interpolate–truncate–project cycle in ITP [4], an Anderson–Björck step behind a linearity check in ModAB [10, 20]. Each of those answers carries constants: Illinois halves the stale ordinate, Brent accepts an interpolation subject to three empirical inequalities, ITP is governed by $\kappa_1,\kappa_2,n_0$.
A bracketing root finder must decide, at every step, where inside the bracket to evaluate next. Bisection answers "in the middle", which is the correct answer when nothing is known. Every improvement since has answered "where a local model of $f$ says the root is": a secant in false position, an inverse quadratic in Brent [2], a cubic in TOMS 748 [1], an interpolate–truncate–project cycle in ITP [4], an Anderson–Björck step behind a linearity check in ModAB [10, 20]. Each of those answers carries constants: Illinois [7] halves the stale ordinate, Brent accepts an interpolation subject to three empirical inequalities, ITP is governed by $\kappa_1,\kappa_2,n_0$.
This paper asks a different question. Suppose the only thing available is a *similarity function* — a map $S$ from an outcome to $[0,1]$, where 1 means "identical to what was wanted" and 0 means "not similar at all", in the sense used for parameter-free control in [6]. Can a root finder be built out of $S$ alone, and how does it compare with methods built out of polynomial models and tuned constants?
The answer is sharp in both directions, and the negative half is as informative as the positive half.
The paper makes four claims, each measured.
The paper proceeds in two stages. Sections 2–7 derive a base rule from the similarity alone, measure it, and locate what it lacks: not a better model but a decision, when to stop trusting the model it has. Section 8 supplies that decision with a similarity of the run to its own past, and Section 9 tests it on four further corpora: three of them exposed two last gaps of the base step and were used to close them, and the fourth was fixed after the method's code was frozen and run once.
1. A contraction law for the similarity step (§2) fixes a *base rule* — tent similarity, three-point scale, a projection safeguard — before any benchmark is consulted (§3); its order is Muller's.
2. Projecting the step onto the bisection budget, instead of replacing it, keeps the order and a worst case of twice bisection plus one step; the budget's multiplier is the base rule's one numerical constant, and a constant-free variant costs a measured price (§3.1, §5).
3. The base rule's gap on wide brackets is a decision — when to give the model up — and a similarity of the run to its own past takes it (§6). The resulting method, A′, is tested once on a corpus fixed before its code was frozen, against simpler detectors put in its place, and against the classical transformation with the multiplicity known in advance (§7).
4. Every table uses one criterion, a sign change within $10^{-6}$ of the answer, because the benchmarks' published roots score six problems unsolved for every method (§4, §A.5).
The search that produced these results — readings of the similarity that did not pay, the decomposition of the wide-bracket gap, wall-clock time, and the rejected rules — is reported in Appendices A–C. The code, the corpora and the pre-registrations are released (Appendix D and the availability statement).
---
@@ -41,13 +40,11 @@ The most obvious way to make (1) scale-free is to normalise the similarity by th
**Proposition 1.** For the ratio similarity $S(z)=1/(1+|z|)$ with $\sigma=|f(x_\mathrm{far})|$, equivalently for the endpoint weights above,
$$S(a)\,a + S(b)\,b \;=\; \frac{a f(b) - b f(a)}{f(b)-f(a)}.$$
That is the false-position formula. Two things must be said about how far it reaches, because the obvious generalisation is false.
It is specific to that similarity. On $f=x^2-2$ over $[1,4]$, where false position gives 1.200000, the same construction gives 1.206812 for $e^{-|z|}$, 1.015228 for $1/(1+z^2)$, 1.007624 for $1/\sqrt{1+z^2}$ and 1.214286 for the truncated linear form: one of five reproduces it. So "a similarity rule scaled by the bracket is not a new method" would be too broad a conclusion, and this paper's own Table 3 refutes it — bracket-scaled configurations take third, fourth and sixth place on Chandrupatla's set, where false position solves 6 of 45.
That is the false-position formula, and it is specific to that similarity: on $f=x^2-2$ over $[1,4]$, where false position gives 1.200000, $e^{-|z|}$ gives 1.206812, $1/(1+z^2)$ 1.015228, $1/\sqrt{1+z^2}$ 1.007624 and the truncated linear form 1.214286. Bracket-scaled similarity configurations take third, fourth and sixth place on Chandrupatla's set (Table 3), where false position solves 6 of 45.
**Proposition 1'.** It is, however, specific to the *bracket ends* rather than to the ratio form. Take the truncated linear similarity $S(z)=\max(0,1-|z|)$ and the divided difference over the two ends, $\sigma=|f[a,b]|\cdot|b-a|=|f(b)-f(a)|=|f(a)|+|f(b)|$. Then $1-S=|f(a)|/(|f(a)|+|f(b)|)$, which is false position again, and the agreement is exact to the last bit.
Together the two say where the boundary of novelty runs, and it is not where one would guess: it runs through the words *most recent* in the definition of $\hat f'$ in §3. A divided difference over the bracket ends is three centuries old; the same difference over the most recent iterates is the secant. In our measurements plain false position solves 127 of the 154 Alefeld–Potra–Shi problems, against 154 for bisection.
Together the two say that the boundary of novelty runs through the words *most recent* in the definition of $\hat f'$ in §3: a divided difference over the bracket ends is false position, the same difference over the most recent iterates is the secant. In our measurements plain false position solves 127 of the 154 Alefeld–Potra–Shi problems, against 154 for bisection.
### 2.2 The contraction law
@@ -63,12 +60,6 @@ Table 1 checks (2) against measurement on $f(x)=x^2-2$ over $[0,2]$ with $\sigma
**Table 1.** The contraction law against measurement, all rows at the *chosen* scale $\sigma=|f(x_0)|$. $c$ is the slope of $1-S$ at the origin and $c_2$ the coefficient of $z^2$ when $c=0$.
A similarity with $c=0$ does not converge at a rate at all. Expanding $1-S=c_2z^2+O(z^4)$ in the same way as (2) gives $e_{n+1}=e_n-c_2|f'(r)|^2|D|e_n^2/\sigma^2+O(e_n^3)$, hence
sublinear rather than stagnant. Reporting a "measured rate" for these rows would report the value of $e_{n+1}/e_n$ at whatever $n$ the window happened to end — it creeps to 1 monotonically (0.925 at $n=10$, 0.991 at $n=100$, 0.9998 at $n=4000$) — so the prediction tested is the coefficient in (2a) instead.
The last row is not a slow rate but an immediate failure, and it matters because that similarity is the one this paper goes on to propose. At $x_0=0$ the residual is $z=-1$, so $S=\max(0,1-1)=0$, the step is the whole bracket, the iterate lands exactly on $x_\mathrm{far}$ and never leaves: the error is 0.5858 after one step and after two hundred. **The failure is a property of the chosen scale, not of the similarity.** Under the derived scale of §3 the residual $|f|/\sigma$ stays below 1 — on this problem the clip does not engage on a single one of the seven steps the method takes — the truncation never fires, and the same function is the best of the nine (Table 4). This is the paper's own thesis, that the content sits in the scale and not in the shape of $S$, showing up as a discontinuity in the behaviour of one function.
A similarity with $c=0$ does not converge at a rate at all. Expanding $1-S=c_2z^2+O(z^4)$ in the same way as (2) gives $e_{n+1}=e_n-c_2|f'(r)|^2|D|e_n^2/\sigma^2+O(e_n^3)$, hence
sublinear rather than stagnant. Reporting a "measured rate" for these rows would report the value of $e_{n+1}/e_n$ at whatever $n$ the window happened to end — it creeps to 1 monotonically (0.925 at $n=10$, 0.991 at $n=100$, 0.9998 at $n=4000$) — so the prediction tested is the coefficient in (2a) instead.
The last row fails at once, and it matters because that similarity is the one this paper proposes: at $x_0=0$, $z=-1$, so $S=0$, the step is the whole bracket and the iterate stays at $x_\mathrm{far}$ (error 0.5858 after one step and after two hundred). The failure belongs to the chosen scale, not to the similarity: under the derived scale of §3 the clip never engages on this problem, and the same function is the best of the nine (Table 4).
**Corollary 1 (A chosen similarity is not enough).** For fixed $S$ and fixed $\sigma$ the rate in (2) depends on $|f'(r)|$, $D$ and $\sigma$, none of which is under the method's control. Convergence is at best linear and occurs only when the product lands in $(0,2)$.
@@ -92,9 +89,9 @@ Empirically, with $\sigma$ taken as $|f|$ at the starting point, five to eight o
## 3. The method
Corollaries 1–3 fix every choice in the method before any benchmark is consulted. We record them here as a prediction, and Section 5.3 tests it against the full grid. One number in the safeguard is not fixed by them, and §3.1 declares it as the method's one free constant.
Corollaries 1–3 fix every choice before any benchmark is consulted; Section 5.2 tests the prediction against the full grid. One number in the safeguard is not fixed by them, and §3.1 declares it.
**Similarity: any with $c\neq0$.** The law makes the shape of $S$ almost irrelevant and its slope at the origin decisive. We take the plainest member, the **truncated linear** or tent form $S(z)=\max(0,1-|z|)$. It is not the ratio form $1/(1+|z|)$, which is a separate row of every table here; the two are distinguished throughout because Proposition 1 applies to the ratio form and Table 1's last row to the tent.
**Similarity: any with $c\neq0$.** The law makes the shape of $S$ almost irrelevant and its slope at the origin decisive. We take the plainest member, the **truncated linear** or tent form $S(z)=\max(0,1-|z|)$. It is not the ratio form $1/(1+|z|)$ of Proposition 1.
**Scale: the finest data-derived one.** We set $\sigma = |\hat f'|\cdot|x_\mathrm{far}-x|$ with $\hat f'$ a divided difference of the **most recent evaluations** — not of the bracket ends, which by Proposition 1' would give false position. A divided difference is a computation, not a fitted coefficient: nothing is chosen, regressed or tuned. The two-point difference has slope error $O(h)$ and gives the secant order $\varphi\approx1.618$; the three-point difference — the derivative at $x_n$ of the parabola through the last three evaluations, $\hat f' = f[x_{n-1},x_n] + f[x_{n-2},x_{n-1},x_n](x_n-x_{n-1})$ — has error $\tfrac16 f'''(\eta)\,(x_n-x_{n-1})(x_n-x_{n-2})$, that is $O(e_{n-1}e_{n-2})$. The Newton step with that derivative satisfies $e_{n+1}=\tfrac{f''}{2f'}e_n^2+\tfrac{f'''}{6f'}\,e_n e_{n-1}e_{n-2}+\dots$; the second term dominates, and the order is the root of $p^3=p^2+p+1$, $p\approx1.839$ — **Muller's order**, as it should be, since the step uses the same parabola as Muller's method, through its derivative rather than its root. Only where the parabola is exact ($f'''=0$) does the order rise to Newton's 2. Carried to $10^{-2900}$ in 3000-digit arithmetic (`order_high_precision.py`), the computational order per step settles at 1.840 on $\cos x-x$, $e^x-2$, $x^3-x-2$ and $\sin x-\tfrac12$ and at 2.000 on $x^2-2$; the two-point scale settles at 1.618. In double precision a run takes three or four superlinear steps before it reaches the tolerance, too few to tell 1.84 from 2, and we report no order measured there.
@@ -102,9 +99,7 @@ Corollaries 1–3 fix every choice in the method before any benchmark is consult
$$x' \;=\; x + \operatorname{sign}(x_\mathrm{far}-x)\cdot\min\!\Bigl(\frac{|f(x)|}{|\hat f'|},\;|x_\mathrm{far}-x|\Bigr), \tag{4}$$
a Newton step with a three-point divided-difference derivative, clipped to the bracket and then projected as in §3.1. That the tent turns the similarity step into Newton's is a property of the similarity chosen, not a sign that the similarity has dropped out: which $S$ stands in the step decides the step, and it is measured, not assumed — Table 4 ranks the nine functions over the whole grid, thereadings of §7 pay most with the tent in the step and some of them lose with other functions (§7), and in the proposed method replacing the tent by any other function costs 19% to 425% (Table 9a).
We state the reduction ourselves rather than leave it to be discovered, because it locates the contribution precisely. The base rule is not a new family of methods. It is (i) a derivation that fixes every choice — which similarity, which scale, which safeguard — before any benchmark is consulted, and predicts which similarity functions come first; (ii) a safeguard of ITP's projection type with ITP's truncation stage, and hence all three of its constants, removed; (iii) the negative results of §10; (iv) the measurements. We call it the **base rule**; the method this paper proposes builds on it in Section 8.
a Newton step with a three-point divided-difference derivative, clipped to the bracket and then projected as in §3.1. That the tent turns the similarity step into Newton's is a property of the chosen similarity, and which $S$ stands in the step is measured rather than assumed: in the proposed method, replacing the tent by any other function costs 19% to 425% (Table 7). The base rule is therefore not a new family of methods; what is ours is a derivation that fixes every choice before any benchmark is consulted, and a safeguard of ITP's projection type with ITP's truncation stage — and all three of its constants — removed. The proposed method builds on it in Section 6.
**Algorithm.** Given $[a,b]$ with $f(a)f(b)<0$ and tolerance $\varepsilon_x$:
@@ -129,7 +124,7 @@ while |B - A| > xtol:
return midpoint(A,B)
```
Every quantity in it is either an evaluation of $f$, a divided difference of evaluations, or the halving that defines bisection — with one exception, the factor 2 in `n_max`, which §3.1 declares as the method's one free constant. The constant-free variant of §3.1 replaces the two `allow` lines by the earned credit of (3').
Every quantity in it is either an evaluation of $f$, a divided difference of evaluations, or the halving that defines bisection — with one exception, the factor 2 in `n_max`, which §3.1 declares as the base rule's one numerical constant. The constant-free variant of §3.1 replaces the two `allow` lines by the earned credit of (3').
The next bracket is at most $\max(x-\ell,\,h-x)$, so (3) gives $w_{j+1}\le\alpha_j$ directly, and induction bounds the run by $n_{\max}$ steps in exact arithmetic. In floating point the bracket can land exactly on the boundary, and the termination test then asks for one more; the guarantee stated and verified is therefore $2\lceil\log_2(w_0/\varepsilon_x)\rceil+3$ evaluations.
**The multiplier is the method's one free numerical constant.** It is not "the factor already claimed in the bound" — that argument is circular, the bound is twice bisection because two was chosen — and measurement settles it (`measure_budget_factor.py`, Linux):
**The multiplier is the base rule's one numerical constant**, and it is not neutral (`measure_budget_factor.py`, Linux):
**Table 1a.** The scheduled projection at budget multiplier $c$, i.e. $n_{\max}=\lceil c\cdot\log_2(w_0/\varepsilon_x)\rceil$. Total evaluations, failures charged.
@@ -154,7 +149,7 @@ The next bracket is at most $\max(x-\ell,\,h-x)$, so (3) gives $w_{j+1}\le\alpha
| 3 | 2634 | 2936 | 5570 |
| 4 | 2634 | 3229 | 5863 |
On the canonical set the projection binds on one problem only once $c\ge1.25$ — `aps.13.00`, whose $f$ underflows to zero around the root (20, 30 and 28 evaluations at $c=1.25$, 1.5 and 2) — so the total barely moves. On the wide-bracket set it moves monotonically with $c$ from 1.25 on, and 2 is not the best value. Nor is zero slack ($c=1$) a degenerate case under which the guard behaves as bisection: it costs 4625 and 2039, far from bisection's 7338 and 2136, and on the wide-bracket set it beats $c=2$. The multiplicative family has no member that the bound singles out, so its multiplier is a genuinely free constant.
On the canonical set the projection binds on one problem only once $c\ge1.25$ — `aps.13.00`, whose $f$ underflows to zero around the root (20, 30 and 28 evaluations at $c=1.25$, 1.5 and 2). On the wide-bracket set the cost rises monotonically with $c$ from 1.25 on, and 2 is not the best value; zero slack ($c=1$) is not degenerate either (4625 and 2039, against bisection's 7338 and 2136). No member of the family is singled out by the bound: the multiplier is a free constant.
**The budget earned rather than scheduled: a constant-free variant.** The constant can be removed. Let $s_j=\log_2(w_j/w_{j+1})$ be the shrink a step delivers in bits — bisection delivers exactly 1 — and keep a credit
@@ -172,12 +167,11 @@ leaving the step alone while $C\ge1$. A step may under-deliver only by what earl
| 2 | bisection + 2 steps | 3502 | 1757 | 5259 |
| 5 | bisection + 5 steps | 2819 | 1666 | 4485 |
A looser promise buys evaluations, as it must; within one promise the cheapest endowment is determined. The constant-free variant is therefore the earned projection with $C_0=1$, whose guarantee — $\lceil\log_2(w_0/\varepsilon_x)\rceil+4$ evaluations, bisection plus one step with one more for the floating-point boundary — is verified on all 199 problems.
Within one promise the cheapest endowment is determined, so the constant-free variant is the earned projection with $C_0=1$; its guarantee, $\lceil\log_2(w_0/\varepsilon_x)\rceil+4$ evaluations, is verified on all 199 problems. We nonetheless propose the scheduled projection with $c=2$, fixed before any benchmark was consulted: it is 743 evaluations cheaper on the canonical set (2634 against 3377), the earned variant is cheaper on the wide-bracket set (2073 against 2357) — one declared numerical constant and the better canonical result, or none, a tighter bound and the better wide-bracket result.
We nonetheless propose the scheduled projection with $c=2$ and declare its multiplier. On the canonical set it is 743 evaluations cheaper than the earned variant (2634 against 3377), and there its total barely depends on the multiplier; on the wide-bracket set the earned variant is the cheaper (2073 against 2357). The two are the ends of a trade stated in numbers: one declared constant and the better canonical result, or no constant, a tighter bound and the better wide-bracket result. The value 2 was fixed before any benchmark was consulted.
Two remarks apply to both forms.
Three remarks apply to both forms.
- **Guaranteed bounds of this kind are classical.** Bus and Dekker [21] gave bracketing methods whose worst case is four to five times bisection; the scheduled projection guarantees twice bisection plus one step, the earned one bisection plus one step.
- **This is ITP's projection stage without ITP's truncation stage.** All three of ITP's constants live in the truncation, whose purpose is to repair regula falsi; the similarity step needs no repair, so the truncation and every constant with it are dropped.
- **Writing the clamp in the form (3) rather than as a radius $\alpha_j-w_j/2$ about the midpoint is not cosmetic.** That radius is a difference of two nearly equal numbers; the cancellation made the computed radius too large by a relative $8.9\cdot10^{-5}$, which broke the invariant on eleven Chandrupatla problems.
@@ -187,15 +181,9 @@ Two remarks apply to both forms.
**Problem sets.** The 154 problems of Alefeld, Potra and Shi [1] and the 45 of Chandrupatla [3], taken programmatically from SciPy's `scipy.optimize._tstutils` so that no bracket or root is transcribed by hand.
**Cost model.** Evaluations of $f$. A run counts as solved when it reports convergence and $f$ changes sign within $10^{-6}$ of the returned point, or vanishes there — the bracketing notion of a root, the same criterion on every corpus of this paper; $x$-tolerance $10^{-12}$; budget 200 evaluations. Scoring against the published root instead, as the benchmarks' harness does, charges every method the full budget on six problems where $f$ vanishes identically around the root in floating point (§7.3). Head-to-head counts (wins/ties/losses) are taken over every problem of a set with the same charging as the totals. *Failures are charged the full budget*, so no method can buy a low total by giving up early, and totals are reported over all problems rather than over the subset a method happened to solve. Rankings are by failures first, then by total.
**Cost model.** Evaluations of $f$. A run counts as solved when it reports convergence and $f$ changes sign within $10^{-6}$ of the returned point, or vanishes there — the bracketing notion of a root, the same criterion on every corpus of this paper; $x$-tolerance $10^{-12}$; budget 200 evaluations. Scoring against the published root instead, as the benchmarks' harness does, charges every method the full budget on six problems where $f$ vanishes identically around the root in floating point (§A.5). Head-to-head counts (wins/ties/losses) are taken over every problem of a set with the same charging as the totals. *Failures are charged the full budget*, so no method can buy a low total by giving up early, and totals are reported over all problems rather than over the subset a method happened to solve. Rankings are by failures first, then by total.
**Fairness to the competitors.** Three deliberate concessions.
1. ITP is natively written against a half-width $\epsilon$ and terminates on $b-a\le2\epsilon$; feeding the common tolerance in as $\epsilon$ would let it stop at twice its competitors' bracket width, so $\epsilon=\varepsilon_x/2$ is used.
2. ITP is reported both at the authors' recommended constants and as a *per-problem oracle*, the best of a $(\kappa_1,n_0)$ grid chosen separately for every problem — not an achievable method, included so that the state of the art cannot be said to have been handicapped.
3. Our implementation of ITP is validated against the method's own theorem, that it never needs more than $n_{1/2}+n_0$ iterations, on all 154 problems for $n_0\in\{0,1,2,5\}$.
4. Where SciPy ships the method, the competitor **is the library**: Chandrupatla is `scipy.optimize.elementwise.find_root`, Brent is `brentq`, TOMS 748 is `toms748`, each run with the same stopping rule as every other method — a bracket no wider than $\varepsilon_x$, the relative term at machine precision, no stopping on $|f|$ — and each evaluation counted by the same wrapper around $f$. An earlier version gave each competitor the better of the library's implementation and ours, problem by problem. That concession backfired in the competitor's favour: our Chandrupatla stopped on a bracket twice as wide as everybody else's — the factor this protocol corrects for ITP in item 1 and had not corrected there — and the per-problem minimum let the looser run win. With the tolerance corrected our implementation and the library agree on all 199 problems exactly. For Brent the library is slightly dearer than our implementation; we use the library anyway, because it is the implementation a reader can check. ITP has no library implementation, which is why it is checked against its own theorem instead; bisection and the false-position family are not in SciPy either and are ours.
**Competitors.** Where SciPy ships the method, the competitor is the library — Chandrupatla is `scipy.optimize.elementwise.find_root`, Brent `brentq`, TOMS 748 `toms748` — run with the stopping rule of every method (a bracket no wider than $\varepsilon_x$, the relative term at machine precision, no stopping on $|f|$) and counted by the same wrapper around $f$. ITP, which has no library implementation, is ours, run with $\epsilon=\varepsilon_x/2$ because it terminates on $b-a\le2\epsilon$, checked against its own theorem ($n_{1/2}+n_0$ iterations at most, on all 154 problems for $n_0\in\{0,1,2,5\}$), and reported both at the authors' constants and as a *per-problem oracle* over a $(\kappa_1,n_0)$ grid — not an achievable method, included so that it cannot be said to have been handicapped. Bisection and the false-position family are ours.
**Reproducibility.** All numbers come from a single Linux run. The test functions call `libm`, whose last digit differs between platforms; the same SciPy version scores 2635 for TOMS 748 on Windows and 2636 on Linux for the same set. Every number stated in this paper is checked against the run data by a script that is part of the test suite.
@@ -203,7 +191,7 @@ Two remarks apply to both forms.
## 5. Results
The grid is nine similarity functions × four scales × seven safeguards = 252 configurations, plus twelve baselines: 264 in all. The base rule of Section 3 — tent similarity, three-point scale, projection safeguard with its multiplier declared — is one entry of that grid, fixed by the theory rather than chosen from the ranking; the constant-free earned variant of §3.1 is shown beside it. Chandrupatla's own method and Dekker's are among the baselines; an earlier version of these tables was drawn up before either was added.
The grid is nine similarity functions × four scales × seven safeguards = 252 configurations, plus twelve baselines: 264 in all. The base rule of Section 3 — tent similarity, three-point scale, projection safeguard with its multiplier declared — is one entry of that grid, fixed by the theory rather than chosen from the ranking; the constant-free earned variant of §3.1 is shown beside it. Chandrupatla's own method and Dekker's are among the baselines.
**Table 2.** Alefeld–Potra–Shi, 154 problems, out of 264 configurations. Places by failures first, then total.
@@ -215,15 +203,15 @@ The grid is nine similarity functions × four scales × seven safeguards = 252 c
| 244 | false position | no | 127/154 | 10560 | — |
The base rule and the same rule without any safeguard tie at 2634: the projection never binds on this set. By total, Chandrupatla's method is first with 2595, 1.5% fewer; the base rule and TOMS 748 are level (2634 against 2636). Problem by problem the base rule wins against Chandrupatla 87/24/43 (wins/ties/losses; exact two-sided sign test $p=1.4\cdot10^{-4}$), against TOMS 748 86/16/52, Brent 102/11/41, ITP at its recommended constants 132/3/19, the per-problem ITP oracle 121/4/29, and bisection 152/1/1. It wins twice as many problems as it loses against Chandrupatla and trails that method in total because its losses are the larger ones; the problems are not independent (§9), and taken by function family the comparison is 6/1/8 of 15 families, a tie. Dekker and Illinois solve 153 of 154 and are ranked after every method that solves all. The constant-free variant goes 79/3/72 against Chandrupatla ($p=0.63$, a tie in problems), 78/9/67 against TOMS 748 and 79/5/70 against Brent, and loses on the total to all three.
The base rule and the same rule without any safeguard tie at 2634: the projection never binds on this set. By total, Chandrupatla's method is first with 2595, 1.5% fewer; the base rule and TOMS 748 are level (2634 against 2636). Problem by problem the base rule wins against Chandrupatla 87/24/43 (wins/ties/losses; exact two-sided sign test $p=1.4\cdot10^{-4}$), against TOMS 748 86/16/52, Brent 102/11/41, ITP at its recommended constants 132/3/19, the per-problem ITP oracle 121/4/29, and bisection 152/1/1. It trails Chandrupatla in total because its losses are the larger ones; by function family, the unit in which these problems are independent (§7), the comparison is 6/1/8 of 15 families, a tie. The constant-free variant goes 79/3/72 against Chandrupatla ($p=0.63$), 78/9/67 against TOMS 748 and 79/5/70 against Brent.
**Table 3.** Chandrupatla, 45 problems, brackets up to $10^{12}$ wide, out of 264 configurations.
@@ -244,32 +232,11 @@ Here Chandrupatla's method is genuinely the stronger: it beats the base rule 29
### 5.1 What the guarantee costs
It depends on which bound is bought. Against the identical rule with no safeguard at all:
Against the same rule with no safeguard (Tables 2 and 3), the projection costs nothing on the canonical set and on the wide-bracket set solves six more problems for 30% fewer evaluations (2357 against 3391; unguarded, 10 runs break the bound). The earned projection, promising bisection plus one step, pays 743 evaluations (28%) on the canonical set and is the cheapest of the three on the wide-bracket set. That is the point of projecting rather than replacing: the two safeguards that replace cost 7068 and 7046 on Alefeld–Potra–Shi, against 2634.
| | solved | total evals | runs over the $2\times$ bound |
The projection of the base rule never binds on the canonical set, so its totals are identical to the last evaluation; on the wide-bracket set it solves six more problems for 30% fewer evaluations. The earned projection promises twice as much — a worst case of bisection plus one step instead of twice bisection — and pays for it on the canonical set, 743 evaluations or 28%, while on the wide-bracket set it is the cheapest of the three. Both are the opposite of the usual trade on at least one set, and that is the point of projecting rather than replacing: the two safeguards that replace cost 7068 and 7046 on Alefeld–Potra–Shi, against 2634.
### 5.2 Wall-clock time, and why it is not the cost model
Evaluations of $f$ are the cost model because in any real problem $f$ is the expensive part. Wall-clock time measures something narrower — the method's own arithmetic per step — and it is implementation- and language-bound. We report it because a reader will ask, and because what it shows is instructive.
On the 199 problems as they stand, every $f$ being a formula of a few operations, the base rule is the **slowest** of the pure-Python methods compared, at 32.8 ms against 18.4 for Brent, 21.1 for ITP, 14.2 for Ridders and 22.9 for bisection — while using the fewest evaluations of all of them, 4991 against 5932, 5675, 6421 and 9474. Bisection, needing nearly twice the evaluations, finishes in two thirds of our time. (These are the evaluations the timing loop actually spent; for the base rule and bisection, which solve every problem, they equal the totals of §5; Brent here is our pure-Python implementation, which is what is timed.)
That inversion is the point. Making $f$ progressively more expensive by a controlled amount of arithmetic and timing the sweep directly — nothing subtracted, nothing modelled — the base rule overtakes ITP once one call to $f$ costs about 9 µs and Brent at about 20 µs, which on this machine is of the order of a hundred arithmetic operations. Below that the ranking is decided by per-step overhead; above it, by evaluations.
Three caveats, all of them limitations of the measurement rather than of the methods. Timings vary by tens of percent between runs even on an idle machine. An attempt to separate our per-step cost into "algorithm" and "Python call overhead", by timing a flattened rewrite of the same rule, produced a variant that diverged from the original on 42 of the 199 problems and was therefore discarded rather than quoted; the question stays open. And an earlier version of this measurement estimated a per-evaluation overhead by subtracting the measured cost of $f$, which returned *negative* overheads for four of six methods — an impossibility that condemned the estimator, and the reason nothing is subtracted here.
### 5.3 The law across the whole grid
Corollaries 1–3 predict that the similarities with $c=1$ come before those with $c=0$ under a data-derived scale; they say nothing about the order within a group, nor about the functions with unbounded slope. Table 4 reports it. For every data-derived scale the three similarities with $c=1$ occupy the top three places and the three with $c=0$ the bottom three. That $c=0$ is slow is the unsurprising half — a step proportional to $z^2$ is damped quadratically. For the chosen scale the two groups are indistinguishable (14983 against 14228), which is consistent — with a chosen scale nothing converges well and the ordering carries no information.
Corollaries 1–3 predict that the similarities with $c=1$ come before those with $c=0$ under a data-derived scale; they say nothing about the order within a group, nor about the functions with unbounded slope. For every data-derived scale the three similarities with $c=1$ occupy the top three places of Table 4 and the three with $c=0$ the bottom three (that $c=0$ is slow is the unsurprising half: a step proportional to $z^2$ is damped quadratically). With the chosen scale the two groups are indistinguishable (14983 against 14228), as they should be.
**Table 4.** Mean total evaluations over both sets, step guard, grouped by the slope $c$ of $1-S$ at the origin.
@@ -280,11 +247,301 @@ Corollaries 1–3 predict that the similarities with $c=1$ come before those wit
| bracket | 9149 | 18015 |
| start (chosen) | 14983 | 14228 |
Ranked individually and best over scales, the similarities come out $1-|z|$ (5422), $e^{-|z|}$ (5772), $1/(1+|z|)$ (5993), $(1+|z|)^{-2}$ (8455), $1-\sqrt{|z|}$ (9411), $1-|z|^{1/3}$ (10874), $e^{-z^2}$ (13187), $1/(1+z^2)$ (13990), $1/\sqrt{1+z^2}$ (15272); the scales come out 3-point (5422), 2-point (5564), bracket (7972), start (13423). The group order and the order of the scales are those the law predicts and were predicted before the grid was run; the order inside each group is measured, not predicted.
Ranked individually and best over scales, the similarities come out $1-|z|$ (5422), $e^{-|z|}$ (5772), $1/(1+|z|)$ (5993), $(1+|z|)^{-2}$ (8455), $1-\sqrt{|z|}$ (9411), $1-|z|^{1/3}$ (10874), $e^{-z^2}$ (13187), $1/(1+z^2)$ (13990), $1/\sqrt{1+z^2}$ (15272); the scales come out 3-point (5422), 2-point (5564), bracket (7972), start (13423). The group order and the order of the scales were predicted before the grid was run; the order inside each group is measured.
---
## 6. Where the evaluations go
## 6. The flat regime, recognised by analogy
The base rule's gap on wide brackets is a decision, *when to give the model up* (§A.6): on the pure powers `fun3`–`fun6` the right move is to halve, Chandrupatla's method halves on up to all of its steps, and no reading of a single state measured takes that decision correctly. This section takes it by a similarity of the run to *its own past* — an analogy in Pólya's sense [8], two situations agreeing in more than one relation. Every rule below was pre-registered with a stop rule (Appendix D); the rejected ones are in Appendix C.
### 6.1 The flag: a promise that was not kept
A tangent step on an approach of local order $m$ lands at $e(1-1/m)$ and leaves the residual $r=|f_\text{new}/f_\text{old}|=(1-1/m)^m$ on the same side of the root. It beats a halving exactly when $|1-1/m|<\tfrac12$, that is for $\tfrac23<m<2$. At the upper end $r=(1-\tfrac12)^2=\tfrac14$. So a model step that did not cross the root and left $r\ge\tfrac14$ is a *promise not kept*: the model is in the regime where a halving would have done better. The constant is bisection's $\tfrac12$, carried through the formula; the contraction $e(1-1/m)$ is the one behind the classical multiplicity estimates from successive Newton iterates [14, 15], used here to set the point at which a model step has lost to a halving.
Acted upon step by step the flag does not pay: replacing each flagged step by a halving costs the canonical set 2634 → 2839, because a simple root far from convergence also breaks its promises (the flag rises at least once on 142 of the 154 canonical problems). Every refinement that reads the moment alone trades one set for the other (Appendix C, items 12–14).
### 6.2 The analogy: similarity of a run to its own past
What distinguishes a pure power from a simple root is not the shape of one configuration — `fun1` on a wide bracket looks as non-linear as $x^9$ — but what happens under zoom. A pure power $f=c(x-r)^m$ is similar to itself: zooming about the root by $L$ scales $x-r$ by $L$ and $f$ by $L^m$, so the *dimensionless configuration* of the last three points,
recurs as the iteration proceeds. At a simple root, zooming in makes $f$ look like its tangent, and the configuration tends to the *linear prototype* $\varphi=\xi$ instead. Two prototypes, then: the line, and the run's own past. The judgment is ordinal — is the current $(\xi,\varphi)$ nearer to some earlier configuration of this run than to the line? — with the plane's Euclidean distance and the perpendicular to the line, the one geometric choice in this section, declared as such.
Nearness of configurations is one relation. An analogy in the full sense needs a second, and the pair supplies it: if the analogous pair shrank $x$ by $L_x$ and $f$ by $L_f$, the power law requires $L_f=L_x^{\,m}$, so the order is *read from the analogy itself*,
$$m=\frac{\ln L_f}{\ln L_x}.\tag{6}$$
Formula (6) is the log–log slope of a power law, with the shape of the computational order of convergence [18, 19]; what it compares is not consecutive iterates but a pair the similarity of their configurations has found. A real run's analogous pairs are only nearly homothetic and the reading scatters about the order, but the regime asks only whether it reaches 2, the order from which a tangent step loses to a halving (§6.1). On the traces of the base rule, at the moments the flag rises, the three conditions together hold on 3 of the 154 canonical problems, on all 20 flat problems, and on none of `fun2`, `fun8`, `fun9`.
**The analogy belongs to the run, the flag to the moment.** On a pure power the flag rises in the phases of the base rule's cycle whose configuration does *not* recur, so requiring both in one state delayed the entry to the thirteenth state on average. Once established, the analogy is remembered for the run and the regime is entered at the next unkept promise (eighth state on average).
**The translation limit.** The exponential is self-similar by *translation*, $e^{x-1}=e^x/e$: along its flank the step does not shrink while $f$ does, so (6) is undefined or negative. That is the limit $m\to\infty$ of (6), steeper than any power, and it is read as $m=\infty$: when an analogous pair shows $L_f<1\le L_x$. It pays on high powers far from their root, which are locally exponential, $x^m=e^{m\ln x}$.
**Exit.** Inside the regime the step splits the bracket (§6.3). At every split the evaluated $f$ is judged against two predictions: the line through the bracket ends, $F_A(1-t)+F_Bt$ at the split fraction $t$, and 0. Nearer the line, the regime is left and the remembered analogy is cleared. A sticky halving without this exit cost the canonical set 6707; with it, 2935 — the exit is what makes a false entry cheap.
### 6.3 Where to split: the point from the order
Halving is the answer when nothing is known about the shape; inside the regime the order is known. For $|f|\propto|x-r|^m$ the ends of the bracket lie at distances in the ratio $|F_A|^{1/m}:|F_B|^{1/m}$ from the root, so the regime splits at
which is exact for a pure power and becomes the midpoint as $m\to\infty$. It is false position applied to $\operatorname{sign}(f)|f|^{1/m}$ — the transformation that turns a root of multiplicity $m$ into a simple one, classical since Schröder [16] and Traub [17]; what is new here is only where $m$ comes from. With the multiplicity given in advance the transformation is far cheaper still (§7.1): the contribution is the estimate, and its price is measured there. (The order read from a failed promise, $(1-1/m)^m=r$, gives $m=\infty$ in 163 of 174 entries, because the base step is not a pure tangent.) With it the flat families of the wide-bracket set cost 743–847 evaluations against 1044 for pure bisection and 1068 for Chandrupatla.
### 6.4 Five more readings
**The half the step promises.** $S(z)\ge\tfrac12$ means a step of at most half the way to the far end: the model promises the root in the half of the bracket *near* $x$. The line through the bracket ends puts it nearer the end with the smaller $|f|$. When the far end has the smaller $|f|$ the two disagree about the half, and the evaluation halves. On a steep flank — a slope dominated by $f(81)=1.5\cdot10^{35}$ on `fun9.5` — this is the false promise "the root is here", and removing it alone takes the canonical set from 2634 to 2392. It is a relative of Brent's acceptance condition [2]: both refuse a model that points towards the worse end of the bracket.
**The sign contradiction.** The bracket says which way $f$ must go between its ends; the last two points say which way it goes here. Opposite signs — the local picture contradicts the bracket — and the evaluation halves. It is the cause of the `fun9` well: a near-cancelling end reads the root as close while $|f|$ doubles for seventeen steps.
**The repeated step pattern.** Two successive moves the same way, of similar length, mean the iterate creeps along a geometric series whose remainder is $\text{step}\cdot s/(1-s)$; the step is lengthened by $1/(1-s)$, with $s$ the similarity of successive step lengths, but only when the pattern of successive length ratios repeats with a period read from the data (the lag of highest mean similarity) and that similarity exceeds $\tfrac12$.
**The overshoot branch.** §6.1's inequality has a second branch, $m<\tfrac23$: a root of order below one, where the tangent step overshoots and the iterate oscillates about the root ($\sqrt{|x|}\operatorname{sign}x$: 84 evaluations of the base rule). The flag for it is the unkept promise *with* a sign change, $f_\text{new}/f_\text{old}\le-\tfrac14$, and an analogous pair reading $0<m\le\tfrac23$; the split (7) is again exact for a pure power. At a root of order below one $f$ is far from 0 on both sides and the exit fires falsely, so the memory of this branch is *not* cleared by the exit (clearing it was worse on the development corpora).
**Borrowed: the tolerance step.** Once the root is reached from one side the bracket's far end can stay far: on $\sin$ over $[3,4]$ the base rule reaches $|f|\approx10^{-16}$ in 7 evaluations and then halves the remaining $1.3\cdot10^{-7}$ down to the tolerance in 17 more. Dekker [9] and Brent [2] step at least the tolerance; A′ steps $\varepsilon_x/2$ towards the far end when the model's step is shorter, but only after the model has *kept* its previous promise ($r<\tfrac14$). This element is borrowed.
### 6.5 The proposed method
The method this paper proposes, **A′**, is the base rule of §3 — tent similarity, three-point scale, projection with the declared multiplier $c=2$ — with the regime of §6.2–6.3 (the analogy remembered, its translation limit, the split (7), the exit) and the five readings of §6.4. Its constants are bisection's $\tfrac12$ and what follows from it: $\tfrac14=(1-\tfrac12)^2$ and the two branches $m\ge2$, $m\le\tfrac23$ of $|1-1/m|>\tfrac12$. Three choices are declared rather than derived: the multiplier $c$ of the projection (§3.1), the Euclidean geometry of the $(\xi,\varphi)$ plane, and the pattern test of the step lengthening (lags from 2 to half the history, threshold $\tfrac12$). The projection carries the guarantee, $2\lceil\log_2(w_0/\varepsilon_x)\rceil+3$ evaluations; it is verified with no violation on every corpus of this paper and under every safeguard of the grid.
Counting numerical constants does not measure the method's freedom, however. Beyond these numbers A′ is a decision layer of about ten discrete rules — the flag, the analogy and its memory, the entry, the exit, the overshoot branch, the split, the step pattern, the half, the contradiction, the tolerance step — chosen on the two development sets among the many candidates of Appendix C. That selection is why the method was tested once on a corpus fixed before its code was frozen, and against simpler rules put in its place (§7). Algorithm 2 is the complete method; `simroot/pure.py` and the compiled `simbracket` implement exactly this and agree bit for bit.
**Algorithm 2 (A′).** Notation of Algorithm 1; $S(z)=\max(0,1-|z|)$.
# step pattern: the lag 2 .. half the history of highest
# mean similarity of successive length ratios has mean > 1/2,
# and step and x - xp point the same way
if the pattern repeats:
s <- min(|step|,|x-xp|) / max(|step|,|x-xp|)
step <- step / (1 - s), at most |far - x| long
if |step| < xtol/2 and last_model and |fx/fp| < 1/4:
step <- sign(far - x) * xtol/2 # tolerance step
xn <- x + step; model <- true
xn <- clamp(xn, hi - allow, lo + allow) # projection (3)
if xn outside (A,B) or xn in {x, xp}:
xn <- mid; if mid in {x, xp}: stop
last_model <- model and xn is the model's own point
fn <- f(xn)
if in_regime: # exit
t <- (xn - A)/(B - A)
if |fn - (FA(1-t) + FB t)| < |fn|:
in_regime <- false; analogy_seen <- false
allow <- allow/2; (xp2, xp, x) <- (xp, x, xn)
keep the sign change in [A,B]
return midpoint(A,B)
```
**Table 5.** A′ on the two sets of §4 (failures charged 200). A′'s wins/ties/losses against each rival over all problems of the set, exact two-sided sign test.
| | APS-154 | CHA-45 | both | A′ per problem, APS | A′ per problem, CHA |
A′ is 12.9% below Chandrupatla on the canonical set and 28.6% below on the wide-bracket set, and wins more problems than it loses against every rival on both. By function family, the unit in which these problems are independent (§7), it is ahead of Chandrupatla on 8 of the 15 canonical families (6 behind, one level) and on 7 of the 9 wide-bracket families — too few families for either count to be significant ($p=0.79$, $0.18$). The two it is behind on are `fun7`, 69 against 64, and `fun9`, 65 against 55.
**Table 6.** Leave-one-out ablation of A′ on all six corpora of §7 (uniform criterion, a sign change within $10^{-6}$). Δ = total without the component − total with it; positive means the component earns its place.
| component removed | APS | CHA | new-1 | Roots.jl | applied | fourth | sum |
Every component is positive in sum; the regime and the split carry the method. The step pattern (earns on two corpora of six) and the sign contradiction (+28 over 855 problems) are marginal — together under one per cent, 117 of 13032 evaluations — and are kept because the pre-registered rule removes only a component that earns nowhere.
**Table 7.** The similarity in the step of A′: the same method with each of the nine similarity functions of §5.2 in place of the tent, everything else unchanged. Totals on the two sets of §4 and over the six corpora of §7; the guarantee holds with every function.
| $S$ in the step | APS + CHA | six corpora (855) |
|---|---|---|
| **tent $1-|z|$ (A′)** | **3333** | **13032** |
| $e^{-|z|}$ | 3965 | 14142 |
| $1/(1+|z|)$ | 4424 | 14843 |
| $(1+|z|)^{-2}$ | 7249 | 28500 |
| $1-\sqrt{|z|}$ | 11613 | 45552 |
| $1-|z|^{1/3}$ | 16094 | 63031 |
| $e^{-z^2}$ | 17429 | 67701 |
| $1/(1+z^2)$ | 17452 | 67394 |
| $1/\sqrt{1+z^2}$ | 17487 | 67854 |
The nearest other function costs 19% more on the two sets and 9% more over the six corpora, and the functions that approach 1 quadratically cost five times more; the three functions with a linear approach come first and the three with a quadratic approach last, as the contraction law of §2.2 predicts. What this measures should be said plainly: with the tent the step is the undamped Newton step clipped to the bracket, and the other functions damp it, so the table shows that the undamped step is the better one — expected, and evidence for the choice of $S$ within the rule rather than for the similarity framing as such.
### 6.6 What is known, and what is new
Several mechanisms of A′ coincide with classical ones, reached from similarity and analogy and recognised afterwards:
| in A′ | classical counterpart |
|---|---|
| the three-point order estimate of §A.3 | Lagouanelle's multiplicity estimate $m=f'^2/(f'^2-ff'')$ [13] |
| the contraction $e(1-1/m)$ behind the flag of §6.1 | multiplicity estimates from successive Newton iterates [14, 15] |
| the order (6) of an analogous pair | the log–log slope of a power law; the (approximate) computational order of convergence [18, 19] |
| the split (7) | false position on $\operatorname{sign}(f)|f|^{1/m}$ [16, 17]; inside a bracket, with the multiplicity given, the SFRFm method of [22] |
| the dimensionless configuration $(\xi,\varphi)$ of §6.2 | the ratios of Chandrupatla's acceptance band [3]; every scaled regula falsi written in two dimensionless parameters [22] |
| the half the step promises | a relative of Brent's acceptance condition [2] |
| the tolerance step | Dekker [9], Brent [2] — borrowed and named as such |
| the exit judgment of §6.2 (the value at a split against the line) | ModAB's linearity check at the midpoint [10, 20], $|y_m-y_3|<k\,(|y_3|+|y_m|)$ with $k$ from a symmetry factor; ours compares two predictions, the line and 0, without a factor |
What we found no counterpart for, in the literature we could reach, is the part that carries the gain in Table 6: the *decision* layer. A bracketing method that recognises the regime in which its model has lost by the similarity of the run to its own past — the recurrence of a dimensionless configuration against the linear prototype — reads the order from the analogous pair itself, distinguishes similarity by scaling from similarity by translation, and leaves the regime by a similarity judgment at a point the model did not choose; and a step whose length is set by a similarity function whose form, measured, is decisive. The methods for multiple roots we found are single-point iterations without a bracket, with the multiplicity assumed, estimated from derivatives, or removed by a transformation ([17, 23]; Roots.jl's `Order1B` and `Order2B` [12]); inside a bracket, [22] needs the multiplicity given and recognises a multiple root only by the number of evaluations spent, against a third of bisection's; Chandrupatla takes the decision to halve by an empirical acceptance band [3]. §7.1 puts this claim against the detectors a reader would try first, and against the transformation with the multiplicity known in advance.
---
## 7. Out-of-sample evidence
Every decision above was taken on the two sets of §4. Pre-registration protects each decision but not the chain: the final configuration — a decision layer of about ten discrete rules, chosen among the many candidates listed in Appendix C — was selected on the same 199 problems. Four further corpora were therefore used, each fixed and committed before any method was run on it, each with its criteria and stop rule written down first.
1.**New corpus 1** — 206 problems: SciPy's `original` set (6) and five generated classes of 40 (seed 20260921): smooth and transcendental simple roots, multiple roots $|x-r|^p$ with $p\sim U(2,20)$, exponential flanks, near-cancelling terms.
2.**Roots.jl / ModAB** — the bracketing benchmark of Roots.jl [12] used by ModAB's authors [10]: 91 problems, 13 of which coincide with APS or Chandrupatla functions and are excluded, leaving 78.
3.**Applied and GSL** — the 10 bracketed problems of the GNU Scientific Library's test suite [11] and 122 equations from physics and engineering on grids fixed beforehand (Kepler, Colebrook–White, Lambert's W, Planck/Wien, van der Waals, internal rate of return, the finite square well).
4.**Corpus 4** — 240 problems in six new families (seed 20260922): random polynomials with a simple root, polynomials with a multiple root, fractional powers $|x-r|^p$ with $p\sim U(0.15,0.95)$, sums of exponentials and sines, compositions $\tanh$, $\arctan$, $\log(1+q^2)$ of a cubic, and functions odd about the root. The code of A′ was committed *before* this corpus was run, and nothing was changed after. The families were chosen by the author, who knew by then where the method is strong: two of the six (multiple roots, fractional powers) are the regime §6 was built for.
A root must carry a sign change to be bracketed, so the multiple roots in these corpora have odd multiplicity (3, 5, 7, 9 in corpus 4) or the form $\operatorname{sign}(x-r)|x-r|^p$ (corpora 1 and 4).
ModAB, the most recent bracketing method we know of, is run from its authors' package (`pymodab` 1.0.9) with the stopping rule and the counting of every other method. The criterion, tolerance, budget and charging are those of §4.
The variant carried to corpora 1–3 was A′ without the tolerance step and the overshoot branch. It transferred to corpus 1 by every pre-registered criterion (per problem 105/34/67 against Chandrupatla, $p=0.005$), did *not* transfer to Roots.jl (38/10/30, $p=0.4$) and lost on the applied corpus; the two readings that closed those gaps were developed on the seen corpora and tested once, on corpus 4.
**Table 8.** A′ against the library rivals on all six corpora (uniform criterion): total evaluations; in brackets the runs that break the $2\times$-bisection bound.
| **corpus 4 (240), the one test** | **177/35/28, $p=10^{-27}$** | **183/23/34** |
Only the last line of corpora is evidence for A′ in the strict sense; the first five were seen while it was developed and are reported as a description. ITP, at its recommended constants and as the per-problem oracle, keeps its own bound of bisection plus $n_0$ everywhere and is behind A′ on every corpus.
**Independence.** A sign test counts problems as independent draws, and not all of them are. In the two canonical sets one function appears with several brackets or parameters, so the independent unit is the function family (15 in APS, 9 in CHA); at that level the counts of Table 5 are not significant, because there are too few families. In corpora 1 and 4 every problem is drawn independently from its family's generator, so the per-problem test is valid — for the population those generators define, which the author chose (above). On corpus 4 A′ is 36% below Chandrupatla and wins 177 problems to 28. Without the two classes aimed at by the last two readings it is 2403 against 3569, per problem 107/31/22 ($p=10^{-14}$); on the simple-root classes alone, 737 against 747 (−1.3%), per problem 43/22/15 ($p=3\cdot10^{-4}$).
**Table 9.** Corpus 4 by class: totals of A′ / Chandrupatla, per problem wins/ties/losses.
| sums of exponentials and sines | 304 | 328 | 23/14/3 |
| $\tanh$, $\arctan$, $\log(1+q^2)$ of a cubic | 503 | 1017 | 25/8/7 |
| odd about the root | 329 | 364 | 33/3/4 |
**What the evidence supports, and what it does not.** Against Brent and TOMS 748 A′ is ahead on every corpus, by 8% to 67% in total, and it stays within its own bound of twice bisection on all 855 problems, a bound that Brent exceeds on 118 and TOMS 748 on 84 — neither promises it. Against ModAB it is ahead on every corpus, in total and problem by problem ($p\le0.0013$) — on corpus 4 by 3656 to 5535 and 183 problems to 34 ($p=7\cdot10^{-26}$), on all 91 problems of ModAB's own benchmark by 1507 to 1588 (56/13/22, $p=1.5\cdot10^{-4}$); ModAB, which does not promise the bound either, exceeds it on 9 problems. Against Chandrupatla it is ahead in total on five corpora and level on the sixth. The lead is large on multiple, fractional and nearly flat roots and small on simple roots. It is behind on `fun9` (65 against 55), the near-cancelling class of corpus 1 (491 against 391) and random polynomials with a simple root in total (433 against 419), and level on the smooth equations of the applied corpus.
### 7.1 Controls: simpler detectors, and the multiplicity known in advance
Two controls ask whether the gain of §6 comes from the recognition by analogy or only from the split (7) with any reasonable detector. Both were pre-registered, with what the paper would say under every outcome, before the run (`sweep_controls.py`).
**Other detectors in A′.** A′ is run with only its detector swapped; the memory, the split, the exit and the five readings of §6.4 are unchanged. (a) *Lagouanelle's estimate* $m_L=1/(1-\theta)$, $\theta=ff''/f'^2$ from the parabola through the last three points ($m_L=\infty$ for $\theta\ge1$), with the same memory — once seen, held until the exit, a hysteresis — entering at $m_L\ge2$ and at $0<m_L\le\tfrac23$ and splitting at $m_L$. (b) *Chandrupatla's acceptance band*, entering when the band rejects the current triple and splitting at the current $m_L$. (c) *No detector*: the regime is entered at every unkept promise.
**Table 10.** A′ against its detector swapped: totals on the two canonical sets, on corpus 4 and over the six corpora of §7 (uniform criterion), and A′'s wins/ties/losses on corpus 4 with the exact sign test. No variant breaks the bound.
| detector | APS | CHA | corpus 4 | six corpora | A′ on corpus 4 | $p$ |
On corpus 4, the one test, A′ beats all three in total and problem by problem, as the pre-registered criterion required. The competent alternative is Lagouanelle's estimate: it is slightly better on the canonical set (2235 against 2259), and on corpus 4 A′'s lead over it lies in the multiple-root and fractional-power families (1163 against 1739, 924 against 1207); it is behind on $\log(1+q^2)$ of a cubic (503 against 477) and level on the rest. The band and the regime without a detector are worse than no regime at all. The decision layer by analogy therefore earns its place against the detectors a reader would try first.
**The multiplicity known in advance.** The second control asks how good the classical transformation is when its multiplicity is given. Library Chandrupatla and Brent are run on $g=\operatorname{sign}(f)\,|f|^{1/m^*}$, with $m^*$ the order read at the known root — the slope of $\ln|f|$ against $\ln|x-r|$ on both sides at $h=10^{-5}$, rounded to 0.01, and 1 where either side shows no zero (a jump). It is not an achievable method but an upper bound. On the multiple and fractional roots, $m^*\ge2$ or $m^*\le\tfrac23$ — the branches of §6.1 where a tangent step loses to a halving:
**Table 11.** Multiple and fractional roots with the multiplicity given: totals, failures charged. A′ wins none of the 148 problems.
| corpus | problems | A′ | Chandrupatla on $g$ | Brent on $g$ | Chandrupatla on $f$ |
|---|---|---|---|---|---|
| Chandrupatla | 20 | 654 | 96 | 66 | 1068 |
| new corpus 1 | 40 | 1229 | 358 | 337 | 1792 |
| corpus 4 | 88 | 2159 | 901 | 844 | 4151 |
With the multiplicity known the transformation is 2.4 to 6.8 times cheaper than A′. A′ is far ahead of Chandrupatla on $f$ itself, which does not know the multiplicity either. On the remaining problems the transformed and untransformed runs differ little and A′ is close to them: ahead on the canonical sets (2259 against 2640, 420 against 437 for Chandrupatla on $g$), behind on corpora 1 and 4 (2061 against 1924, 1497 against 1427). So on multiple roots the contribution of A′ is the estimate of the order without knowing it, and the gap to Table 11 is the price of that estimate — large, and the clearest direction for improving the method.
---
## 8. Conclusion
A root finder can be derived from a similarity function alone, and the derivation shows where its content sits: not in the shape of $S$, whose slope at the origin decides which group of functions comes first, but in the *scale* against which similarity is judged — chosen, the method is linear and brittle; taken from the bracket, it is false position; read off the most recent evaluations, it is Newton's step with Muller's order. The safeguard must project the step rather than replace it; the projection's multiplier is a free constant, declared, and a budget the steps earn removes it at a measured price.
The base rule is level with TOMS 748 and 1.5% behind Chandrupatla's method on the canonical set, and clearly behind Chandrupatla on wide brackets (1505 against 2357); the cause is not the order of the model but the decision to give it up.
That decision is a similarity too, of the run to its own past: a pure power repeats its configuration under zoom, an exponential under translation, a simple root tends to the line, and the analogous pair reads the order that decides between the model and a split. The method built on it, A′, is ahead of Chandrupatla on both canonical sets, and on a corpus fixed before its code was frozen it needs 36% fewer evaluations and wins 177 problems to 28. Its decision layer beats Lagouanelle's estimate, Chandrupatla's band and no detector put in its place; with the multiplicity known in advance the classical transformation is 2.4 to 6.8 times cheaper on multiple roots, which measures what estimating the order costs. The claim the measurements support is a lead over Brent, TOMS 748, ITP and ModAB everywhere, within a bound of twice bisection that Brent, TOMS 748 and ModAB do not promise and exceed, and over Chandrupatla a lead that is large where roots are multiple, fractional or nearly flat and small where they are simple — not superiority by the same margin everywhere. `fun9`, near-cancelling terms and smooth applied equations are where it is behind or level.
### Future work
The first is the price of estimating the order. With the multiplicity given, the transformation of §6.3 is 2.4 to 6.8 times cheaper than A′ on multiple and fractional roots (Table 11): the order read from the analogous pair, or its use in the split, loses most of what the transformation could deliver.
The second is the steep flank. Where the loss remains — `fun9`, the near-cancelling class, 4 − tan x at its pole — the base step approaches along a flank far steeper than its model, and the half contradiction of §6.4 removes only part of the cost. The third is the one geometric choice of §6.2, the Euclidean metric of the $(\xi,\varphi)$ plane: a metric derived from the configuration's own sensitivity would remove it.
---
## Code and data availability
Everything behind this paper is released with it: the reference implementation of the base rule and of A′ (`simroot/pure.py`); `simbracket`, a compiled library of A′ (Cython; wheels for Linux and Windows, Python 3.10 or later) whose results equal the reference bit for bit on all 868 problems of the six corpora; the corpora as code (`corpus_*.py`); every sweep script with its results file; the pre-registrations with the commits of Appendix D; and `verify_article.py`, which recomputes or re-reads every number stated in this paper and is part of the test suite. All numbers come from one Linux machine (§4). Repository: ⟨repository URL⟩.
---
## Appendix A. Where the evaluations go, and where the similarity is read
### A.1 Where the evaluations go
Splitting each run at the geometric mean of the initial width and the attainable tolerance — the log-midpoint of the journey, at which bisection by construction has spent half its evaluations — separates the *approach* from the *endgame*. On Alefeld–Potra–Shi the two-point rule spends 2235 on the approach and 668 on the endgame against Brent's 2175 and 515: its deficit is almost entirely the endgame, i.e. the order, which is what motivated the three-point scale. On Chandrupatla it spends 1068 and 1161 against Brent's 1343 and 1358, winning both phases.
@@ -292,13 +549,11 @@ Two natural explanations of the Chandrupatla advantage were tested and *refuted*
The mechanism is step *quality*. Reconstructing each method's bracket from the signs it observed, the median probe removes 0.500 of the bracket for every method on Alefeld–Potra–Shi, but on Chandrupatla it removes 0.275 for the similarity rule against 0.058 for Brent and 0.068 for TOMS 748. The bracket here is reconstructed from *every* sign the method has observed — the narrowest interval the evaluations prove, not the one the method keeps; Brent and TOMS 748 always probe inside their own bracket, but 69 and 141 of their probes fall outside this narrowest one and remove nothing from it, and those probes are left out of the median. High-order interpolation is a wager that $f$ is locally polynomial. When the wager pays, a high order wins the endgame; when it fails, the probe removes little. The similarity rule makes the weakest available assumption — a single local slope — and degrades gracefully.
---
## 7. Where the similarity is read
### A.2 Where the similarity is read
Everything above reads the similarity in one place: the *length* of the step, $(1-S(z))$ times the distance to the far end. But a bracketing iteration takes several decisions per step, and the similarity was not consulted in any of the others: which end counts as far (decided by distance), which points the local slope is estimated from (decided by recency), and whether the step is worth taking at all (decided after the fact, by the safeguard). Each of those is a place where a similarity can be read, and the placement turns out to matter more than the form of $S$.
Three readings were added to the base rule of §3 (tent similarity, three-point scale), each parameter-free and each measured on the protocol of §4; every number below is re-checked against its own sweep file by `verify_article.py`, and every configuration in Table 5 keeps the $2\lceil\log_2(w_0/\varepsilon_x)\rceil+3$ guarantee (0 violations) and solves all 199 problems.
Three readings were added to the base rule of §3 (tent similarity, three-point scale), each parameter-free and each measured on the protocol of §4; every number below is re-checked against its own sweep file by `verify_article.py`, and every configuration in Table A1 keeps the $2\lceil\log_2(w_0/\varepsilon_x)\rceil+3$ guarantee (0 violations) and solves all 199 problems.
Where a safeguard other than the projection of §3.1 appears, it is one of the grid's. *safe* is the projection preceded by a step-length test — a step not at most half the step taken two evaluations earlier is replaced by a halving — with the projection still carrying the bound. *credit* replaces the scheduled allowance of (3) by one earned step by step: a surplus is banked whenever a step shrinks the bracket by more than half and drawn on when it shrinks it by less, which bounds the run by bisection plus one step. *bracket* and *both* are the replacing safeguards of §3.1, which keep a bound at the price of the order; *step* alone and *none* carry no bound.
@@ -306,7 +561,7 @@ Where a safeguard other than the projection of §3.1 appears, it is one of the g
2.**Which points the model uses.** For a linear $f$ the ratio of positions $\xi=(x-u)/(v-u)$ and the ratio of values $\varphi=(f(x)-f(u))/(f(v)-f(u))$ coincide, so $|\varphi-\xi|$ measures how unlike a linear configuration a triple is. Among the declared candidate triples the one minimising it is *selected* — an argmin over what is already in hand, with no threshold.
3.**Which end is far.** Read by dissimilarity instead of distance: the end whose $|f|$ is larger is the one less similar to the target.
**Table 5.** Reading the similarity in one more place is worth more than changing $S$. Evaluations of $f$; failures charged the full budget.
**Table A1.** Reading the similarity in one more place is worth more than changing $S$. Evaluations of $f$; failures charged the full budget.
| configuration (all with the guarantee, 199/199) | APS-154 | CHA-45 | both |
|---|---|---|---|
@@ -324,21 +579,21 @@ Two results here are worth stating plainly. First, the canonical set improves fr
The readings do not compose. Taken together, (2) and (3) give 2695 and 1928 — worse than either alone on the canonical set, and on the wide-bracket set worse than (3) alone. Two selections that are each an improvement interfere, which is a reason to measure composition rather than assume it.
**Which similarity stands in the step decides whether a reading pays.** The readings above, and the decision *defect* of §7.2, compare quantities directly — an ordinal judgment of which is more similar, in which every decreasing $S$ would decide alike. Their gains nevertheless depend on the $S$ that sets the step (`audit_similarity_places.py`, on the same sweeps): on the canonical set the triple chosen by shape gains 493 evaluations with the tent in the step and loses 250 with $1/\sqrt{1+z^2}$; *defect* gains 283 with the tent and loses 215 with $1-|z|^{1/3}$. The records of this section are the pairing of the tent step with these judgments, not the judgments alone.
**Which similarity stands in the step decides whether a reading pays.** The readings above, and the decision *defect* of §A.4, compare quantities directly — an ordinal judgment of which is more similar, in which every decreasing $S$ would decide alike. Their gains nevertheless depend on the $S$ that sets the step (`audit_similarity_places.py`, on the same sweeps): on the canonical set the triple chosen by shape gains 493 evaluations with the tent in the step and loses 250 with $1/\sqrt{1+z^2}$; *defect* gains 283 with the tent and loses 215 with $1-|z|^{1/3}$. The records of this section are the pairing of the tent step with these judgments, not the judgments alone.
Under the constant-free earned projection the same readings gain little or lose. Reading (1) gives 3316 and 2050, (1)+(2) 3564 and 2074, (1)+(3) 3666 and 2052, all three together 4163 and 2087, against 3377 and 2073 for the constant-free variant itself: the earned budget binds early on the canonical problems, and what the readings buy under the projection is mostly room that the earned budget does not grant.
### 7.1 The similarity function belongs to the place, not to the method
### A.3 The similarity function belongs to the place, not to the method
Everything above varies *where* the similarity is read while keeping one function $S$ for the whole method. That is itself an assumption, and it is measurable. Proposition 1 says that the ratio similarity scaled by the far end is false position; what it does not say is *why* false position stalls. Write $|f|\sim K e^m$ near the current point, $e=|x-r|$. Then the residual ratio against the far end needs no slope at all,
and under the ratio similarity $S=1/(1+|z|)$ the step fraction is $z/(1+z)$, which is exactly inverse linear interpolation. False position therefore reads a dimensionless ratio *at order one* on an approach of order $m$: its three-century-old stall is a mismatch between the similarity function and the place it is read in. The exponent $1/m$ is not a constant — $m=1/(1-ff''/f'^2)$, Lagouanelle's estimate of the multiplicity [13] with the derivatives replaced by divided differences, comes from three evaluations already paid for, as in item 5 of §10, and is 1 whenever the power model does not apply.
and under the ratio similarity $S=1/(1+|z|)$ the step fraction is $z/(1+z)$, which is exactly inverse linear interpolation. False position therefore reads a dimensionless ratio *at order one* on an approach of order $m$: its three-century-old stall is a mismatch between the similarity function and the place it is read in. The exponent $1/m$ is not a constant — $m=1/(1-ff''/f'^2)$, Lagouanelle's estimate of the multiplicity [13] with the derivatives replaced by divided differences, comes from three evaluations already paid for, as in item 5 of Appendix C, and is 1 whenever the power model does not apply.
Measured over the grid, the correction behaves as that reading predicts and not as a universal improvement. Paired inside each (function, safeguard) cell of the far-end reading it is cheaper in 33 of 63 cells — indistinguishable from a coin. But in the one pairing where that reading *is* exact inverse interpolation, the ratio similarity, it is cheaper under all seven safeguards:
**Table 6.** The measured order in the step place, on the far-end reading with the ratio similarity $1/(1+|z|)$. Cheaper in 7 of 7 safeguards; in 33 of 63 cells over all nine functions.
**Table A2.** The measured order in the step place, on the far-end reading with the ratio similarity $1/(1+|z|)$. Cheaper in 7 of 7 safeguards; in 33 of 63 cells over all nine functions.
@@ -352,24 +607,23 @@ Measured over the grid, the correction behaves as that reading predicts and not
Two limits must be stated with it. The estimator is *biased low*: on a pure cube, with no noise at all, it returns 2.830 rather than 3, because a parabola through three points carries only the second difference. The bias is towards $m=1$, that is towards the uncorrected reading, which is the harmless direction, but it is a bias. And the correction does not rescue the far-end reading as a method: the best cell it reaches on the wide-bracket set is 1933, against 1752 for reading (3) above and 1505 for Chandrupatla. In the *slope* reading of §3 the same exponent is harmful, $2634\to2703$, which is the same statement read from the other side — a function that does not belong to a place does not become right by being applied there.
### 7.2 The decision place
### A.4 The decision place
Three places have now been measured: the step length, the far end, and the points the model is built on. The fourth is the *decision* — whether the local model deserves this evaluation or halving is the better use of it. Chandrupatla's method is strong exactly there: it refuses its model on most steps by the acceptance band $1-\sqrt{1-\xi}<\varphi<\sqrt{\xi}$, with $\xi$ and $\varphi$ the ratios of positions and of values defined above. The rule of §3 has no similarity in this place at all: a sign gate and a projection that repairs *after* the step.
Three readings of that place were measured, each on quantities already in hand and each without a constant. Two are ours: refuse when the configuration is less like a linear one than the iterate is off-centre, $|\varphi-\xi|>\min(\xi,1-\xi)$ ("defect"); and refuse when the configuration's similarity to linear falls below the iterate's similarity to the root, $S(|\varphi-\xi|)<S(z)$, with the *same* $S$ in both places ("simpair"). The third is *Chandrupatla's own band*, placed inside our step rule. It is not a proposal; it is an instrument, and it answers whether the remaining gap lives in this place.
In total, no. With Chandrupatla's band in our step, the wide-bracket set costs 2101 under the projection and 1942 under *credit*, against that method's own 1505, while the canonical set rises from 2634 to 5242. The band is not idle — it refuses 63.5% of steps — so this is a decision that was taken and did not pay *in total*. Per family it did pay, and where: it closes almost the whole gap on the flat families and gives back more than that on the families where our step is good. §7.4 takes this apart; the short form is that the decision place does hold most of the gap, but no reading measured so far makes that decision correctly for *our* step.
In total, no. With Chandrupatla's band in our step, the wide-bracket set costs 2101 under the projection and 1942 under *credit*, against that method's own 1505, while the canonical set rises from 2634 to 5242. The band is not idle — it refuses 63.5% of steps — so this is a decision that was taken and did not pay *in total*. Per family it did pay, and where: it closes almost the whole gap on the flat families and gives back more than that on the families where our step is good. §A.6 takes this apart; the short form is that the decision place does hold most of the gap, but no reading measured so far makes that decision correctly for *our* step.
Our own readings do not close it either (best 1941 on the wide-bracket set), but "defect" gives the second-best canonical total: **2351** under the projection of the base rule, next to the 2337 of reading (2) and below every method measured, with the guarantee intact, and between 2343 and 2353 for every multiplier from 1.25 to 4. Under the earned projection it gives 3354 and 2002 (5356 against 5450), the best of the readings of this section without a constant; the flat regime of §8 does better still. It refuses only 6.6% of steps, the fewest of the three. Over both sets it costs 4697 against the 4403 of reading (3), because it gives back on the wide brackets (2346) what it gains on the canonical problems — so it is a second point on the same trade, not a replacement.
Our own readings do not close it either (best 1941 on the wide-bracket set), but "defect" gives the second-best canonical total: **2351** under the projection of the base rule, next to the 2337 of reading (2) and below every method measured, with the guarantee intact, and between 2343 and 2353 for every multiplier from 1.25 to 4. Under the earned projection it gives 3354 and 2002 (5356 against 5450), the best of the readings of this section without a constant; the flat regime of §6 does better still. It refuses only 6.6% of steps, the fewest of the three. Over both sets it costs 4697 against the 4403 of reading (3), because it gives back on the wide brackets (2346) what it gains on the canonical problems — so it is a second point on the same trade, not a replacement.
### 7.3 Why the criterion is a sign change: six problems the published root cannot score
### A.5 Why the criterion is a sign change: six problems the published root cannot score
Chasing the remaining gap to Chandrupatla led to a property of the benchmarks themselves. On the five problems of the wide-bracket family `fun7`, and on the single canonical problem `aps.13.00`, the declared root is 0 while $f$ vanishes *identically* on a neighbourhood of it in floating point: $f(0\pm t)=0$ for every $t$ up to at least $3.2\times10^{-2}$, because the exponential factor underflows — at the returned points $|f(x)|$ is below the smallest normal double. Every method therefore stops after 8 to 28 evaluations at a point where $f(x)=0$ *exactly* — a genuine root — and every method is then scored as having failed, because the harness asks for $|x-\text{root}|\le10^{-6}$ while the returned point sits up to $2.8\times10^{-2}$ away.
The consequence is arithmetic. Each such problem charges the full budget of 200 evaluations to every method, so about 1150 evaluations of every two-set total under the harness — ours and every competitor's — are this artefact, and the "153 of 154" and "40 of 45" that every method reports under the harness are statements about the scoring rather than about the methods. This is why every number in this paper uses the bracketing criterion of §4, a sign change within $10^{-6}$ of the returned point or $f=0$ there: under it Chandrupatla, TOMS 748, ITP, bisection and every configuration of Table 5 solve all 199, Brent 197, Dekker and Illinois 178 and 177.
The consequence is arithmetic. Each such problem charges the full budget of 200 evaluations to every method, so about 1150 evaluations of every two-set total under the harness — ours and every competitor's — are this artefact, and the "153 of 154" and "40 of 45" that every method reports under the harness are statements about the scoring rather than about the methods. This is why every number in this paper uses the bracketing criterion of §4, a sign change within $10^{-6}$ of the returned point or $f=0$ there: under it Chandrupatla, TOMS 748, ITP, bisection and every configuration of Table A1 solve all 199, Brent 197, Dekker and Illinois 178 and 177.
### 7.4 Where the wide-bracket limit actually is
### A.6 Where the wide-bracket limit actually is
The remaining gap looks diffuse — per family +57, +49, +42, +39, +30 and smaller — but part of it has one address. The `fun1` family is the same function with brackets of growing width, and our cost against Chandrupatla's across them is 7/8, 12/12, 19/15, 59/24, 63/44: we tie or win while the bracket is narrow and lose progressively as it widens. The endgame is not the cause; the cost is incurred far from the root: 59 evaluations on a $2\times10^4$-wide bracket is what bisection itself needs ($\lceil\log_2(2\times10^{16})\rceil\approx54$), i.e. there we advance by about one halving per evaluation while Chandrupatla advances by about 2.3.
@@ -381,217 +635,29 @@ What differs on `fun1` is how much each probe removes. Ours shrinks the bracket
That mechanism is right for `fun1` and *wrong as a statement about the whole gap*. Replaying our own trajectories on the 35 problems where we lose, and placing both models on the *same* evaluations — one extra evaluation each, from the same state — splits the 262 evaluations of the gap in two. On `fun1` (58 of them, about a fifth) inverse quadratic interpolation from our own three points removes far more of the bracket than our step did: 10.37 halvings against 0.62 on the $2\times10^4$-wide bracket. There the gap is the order of the model, and the reason is structural: our step $x+(1-S)(x_\mathrm{far}-x)=S\,x+(1-S)\,x_\mathrm{far}$ is a convex combination of two points, while the quadratic interpolant's Lagrange weights at 0 always include exactly one *negative* weight when the triple brackets the root (verified on all 431 non-degenerate states; in the other three the negative weight underflowed to $-0$). A similarity in $[0,1]$ cannot produce a negative weight — Proposition 1 extended from two points to three.
But 172 of the 262 — two thirds — sit on `fun3`–`fun6`, which are pure powers of order 3, 5, 9 and 19, and there *no* interpolating model works: our step removes 0.41–0.75 halvings of the bracket per evaluation, inverse quadratic interpolation from the same points only 0.01–0.17. Chandrupatla does not win those families with his model — his traced run bisects 92, 96, 100 and 100% of his steps. We lose them because our step is slower than bisection there and our guarantee, at most twice bisection, licenses exactly that. His band inside our step confirms it family by family: under the projection it takes `fun4`, `fun5`, `fun6` from 573, 456, 456 to 296, 239, 228 — against his own 297, 233, 233 — and costs +164, +111, +170, +144 on `fun1`, `fun2`, `fun8`, `fun9`, where it refuses steps that were good.
But 172 of the 262 — two thirds — sit on `fun3`–`fun6`, which are pure powers of order 3, 5, 9 and 19, and there *no* interpolating model works: our step removes 0.41–0.75 halvings of the bracket per evaluation, inverse quadratic interpolation from the same points only 0.01–0.17. Chandrupatla's method does not win those families with its model — its traced run bisects 92, 96, 100 and 100% of its steps. We lose them because our step is slower than bisection there and our guarantee, at most twice bisection, licenses exactly that. Its band inside our step confirms it family by family: under the projection it takes `fun4`, `fun5`, `fun6` from 573, 456, 456 to 296, 239, 228 — against its own 297, 233, 233 — and costs +164, +111, +170, +144 on `fun1`, `fun2`, `fun8`, `fun9`, where it refuses steps that were good.
So most of what remains is not the order of the model but the decision *when to give the model up*, and it is open because every detector measured so far confounds "the model is useless here" with something else: the band with "the configuration is not shaped for inverse quadratic interpolation", bracket progress with "the step has not moved the bracket yet" (4258 on the canonical set, §10), and the local order with nothing at all — $m\ge2$ is exactly the condition under which a tangent step loses to a halving, since it lands at $e(1-1/m)$, and it is clean (0.3% of canonical states) but blind: it reports $m\ge2$ on none of the states of families whose order is by construction 3 to 19. For the `fun1` part, three parameter-free ways round the model order have been measured and all three fail in the same way, buying the wide brackets at the price of the canonical set: a fixed fraction of the bracket (the cube-root similarity), a logarithmic change of variable, and the curvature-corrected scale of §10 item 5. The fourth way is to model the curvature directly, which is inverse quadratic interpolation — a different method rather than another reading of this one. Section 8 shows that the decision is reachable without a new model: by a similarity of the run to its own past.
So most of what remains is not the order of the model but the decision *when to give the model up*, and it is open because every detector measured so far confounds "the model is useless here" with something else: the band with "the configuration is not shaped for inverse quadratic interpolation", bracket progress with "the step has not moved the bracket yet" (4258 on the canonical set, Appendix C), and the local order with nothing at all — $m\ge2$ is exactly the condition under which a tangent step loses to a halving, since it lands at $e(1-1/m)$, and it is clean (0.3% of canonical states) but blind: it reports $m\ge2$ on none of the states of families whose order is by construction 3 to 19. For the `fun1` part, three parameter-free ways round the model order have been measured and all three fail in the same way, buying the wide brackets at the price of the canonical set: a fixed fraction of the bracket (the cube-root similarity), a logarithmic change of variable, and the curvature-corrected scale of Appendix C item 5. The fourth way is to model the curvature directly, which is inverse quadratic interpolation — a different method rather than another reading of this one. Section 6 shows that the decision is reachable without a new model: by a similarity of the run to its own past.
On the wide-bracket set our shape reading (2177) is *worse* than bisection (2136); only the dissimilarity reading (1752 under *safe*) is clearly ahead, which is the same statement seen from the other end: where the step cannot march, the reading that helps least is the one that only improves the local model.
Every statement in §§7.3–7.4 is reproduced by `diag_fun7_root.py`, `diag_cha_gap.py`, `diag_who_caps_us.py`, `diag_model_order.py`, `diag_model_order_checks.py`, `diag_flat_detectable.py` and `diag_band_by_family.py`, whose outputs are kept beside them.
## 8. The flat regime, recognised by analogy
§7.4 left one decision open: *when to give the model up*. On the pure powers `fun3`–`fun6` the right move is to halve, Chandrupatla halves on up to all of his steps, and every reading of a single state measured so far confuses "the model is useless here" with something else. This section closes that decision, and the reading that closes it is not a similarity of the current state to a prototype but a similarity of the run to *its own past* — an analogy in Pólya's sense [8], two situations agreeing in more than one relation. Every rule below was pre-registered before its code and its run, with a stop rule; the rejected ones are listed in §10 with their numbers.
### 8.1 The flag: a promise that was not kept
A tangent step on an approach of local order $m$ lands at $e(1-1/m)$ and leaves the residual $r=|f_\text{new}/f_\text{old}|=(1-1/m)^m$ on the same side of the root. It beats a halving exactly when $|1-1/m|<\tfrac12$, that is for $\tfrac23<m<2$. At the upper end $r=(1-\tfrac12)^2=\tfrac14$. So a model step that did not cross the root and left $r\ge\tfrac14$ is a *promise not kept*: the model is in the regime where a halving would have done better. The constant is bisection's $\tfrac12$, carried through the formula. The relation between the order and the contraction, $e(1-1/m)$, is the one behind the classical estimates of multiplicity from successive Newton iterates [14, 15]; here it is not used to estimate $m$ but to set the point at which a model step has lost to a halving.
The flag sees the regime — on the flat families it rises on most model steps — but acted upon step by step it does not pay. Replacing the one flagged step by a halving costs the canonical set 2634 → 2839, because a simple root far from convergence also breaks its promises (the flag rises at least once on 142 of the 154 canonical problems). Every refinement of *when* to halve that reads the moment alone — twice in a row, the promise together with a creep in one direction, a halving that sticks once begun, a judgment of the evaluated point against the line through the bracket — trades one set for the other (§10, items 12–14).
### 8.2 The analogy: similarity of a run to its own past
What distinguishes a pure power from a simple root is not the shape of one configuration — `fun1` on a wide bracket looks as non-linear as $x^9$ — but what happens under zoom. A pure power $f=c(x-r)^m$ is similar to itself: zooming about the root by $L$ scales $x-r$ by $L$ and $f$ by $L^m$, so the *dimensionless configuration* of the last three points,
recurs as the iteration proceeds. At a simple root, zooming in makes $f$ look like its tangent, and the configuration tends to the *linear prototype* $\varphi=\xi$ instead. Two prototypes, then: the line, and the run's own past. The judgment is ordinal — is the current $(\xi,\varphi)$ nearer to some earlier configuration of this run than to the line? — with the plane's Euclidean distance and the perpendicular to the line, the one geometric choice in this section, declared as such.
Nearness of configurations is one relation. An analogy in the full sense needs a second, and the pair supplies it: if the analogous pair shrank $x$ by $L_x$ and $f$ by $L_f$, the power law requires $L_f=L_x^{\,m}$, so the order is *read from the analogy itself*,
$$m=\frac{\ln L_f}{\ln L_x}.\tag{6}$$
Formula (6) is the log–log slope by which any power-law exponent is estimated, and has the shape of the computational order of convergence and its approximation from differences of iterates [18, 19]. What is not classical is *which* two states it compares: not consecutive iterates, but a pair the similarity of their configurations has found. Under an exact zoom a pure power of order $m$ reads exactly $m$; along a real run the analogous pairs are only nearly homothetic and the reading scatters about the order, but the regime asks only whether it reaches 2, the order from which a tangent step loses to a halving (§8.1) — no new constant. On the traces of the base rule, at the moments the flag rises, the three conditions together hold on 3 of the 154 canonical problems, on all 20 flat problems, and on none of `fun2`, `fun8`, `fun9`.
**The analogy belongs to the run, the flag to the moment.** In the cycle the base rule runs on a pure power — undershoot, undershoot, crossing, a nearly empty step — the flag rises in the phases whose configuration does *not* recur (the empty step grows emptier every cycle), while the recurring phases have the flag down. Requiring both in one state delayed the entry on the flat problems to their thirteenth state on average. Once established, the analogy is therefore remembered for the run, and the regime is entered at the next unkept promise (eighth state on average).
**The translation limit.** The exponential is self-similar by *translation*, $e^{x-1}=e^x/e$: along its flank the step does not shrink while $f$ does, so (6) is undefined or negative. That is the limit $m\to\infty$ of (6), steeper than any power, and it is read as $m=\infty$: when an analogous pair shows $L_f<1\le L_x$. A high power far from its root is locally an exponential, $x^m=e^{m\ln x}$, and it was there, not on the flank of `fun9` it was built for, that the limit paid.
**Exit.** Inside the regime the step splits the bracket (§8.3). At every split the evaluated $f$ is judged against two predictions: the line through the bracket ends, $F_A(1-t)+F_Bt$ at the split fraction $t$, and 0. Nearer the line, the regime is left and the remembered analogy is cleared. A sticky halving without this exit cost the canonical set 6707; with it, 2935 — the exit is what makes a false entry cheap.
### 8.3 Where to split: the point from the order
Halving is the answer when nothing is known about the shape; inside the regime the order is known. For $|f|\propto|x-r|^m$ the ends of the bracket lie at distances in the ratio $|F_A|^{1/m}:|F_B|^{1/m}$ from the root, so the regime splits at
which is exact for a pure power and becomes the midpoint as $m\to\infty$. It is false position applied to $\operatorname{sign}(f)|f|^{1/m}$ — the transformation that turns a root of multiplicity $m$ into a simple one, classical since Schröder [16] and Traub [17]; what is new here is only where $m$ comes from. With the multiplicity given in advance the transformation is far cheaper still (§9.1): the contribution is the estimate, and its price is measured there. The order read from a *failed promise*, $(1-1/m)^m=r$, was tried first and gave $m=\infty$ in 163 of 174 entries, because the base step is not a pure tangent; the order read from the *analogy* is the one that works. With it the flat families of the wide-bracket set cost 743–847 evaluations against 1044 for pure bisection and 1068 for Chandrupatla.
### 8.4 Five more readings
**The half the step promises.** $S(z)\ge\tfrac12$ means a step of at most half the way to the far end: the model promises the root in the half of the bracket *near* $x$. The line through the bracket ends puts it nearer the end with the smaller $|f|$. When the far end has the smaller $|f|$ the two disagree about the half, and the evaluation halves. On a steep flank — a slope dominated by $f(81)=1.5\cdot10^{35}$ on `fun9.5` — this is the false promise "the root is here", and removing it alone takes the canonical set from 2634 to 2392. It is a relative of Brent's acceptance condition [2], which admits an interpolant only between the best point and $(3a+b)/4$: both refuse a model that points towards the worse end of the bracket.
**The sign contradiction.** The bracket says which way $f$ must go between its ends; the last two points say which way it goes here. Opposite signs — the local picture contradicts the bracket — and the evaluation halves. It is the cause of the `fun9` well: a near-cancelling end reads the root as close while $|f|$ doubles for seventeen steps.
**The repeated step pattern.** Two successive moves the same way, of similar length, mean the iterate creeps along a geometric series whose remainder is $\text{step}\cdot s/(1-s)$; the step is lengthened by $1/(1-s)$, with $s$ the similarity of successive step lengths, but only when the pattern of successive length ratios repeats with a period read from the data (the lag of highest mean similarity) and that similarity exceeds $\tfrac12$.
**The overshoot branch.** §8.1's inequality has a second branch, $m<\tfrac23$: a root of order below one, where the tangent step overshoots and the iterate oscillates about the root ($\sqrt{|x|}\operatorname{sign}x$: 84 evaluations of the base rule). The flag for it is the unkept promise *with* a sign change, $f_\text{new}/f_\text{old}\le-\tfrac14$, and an analogous pair reading $0<m\le\tfrac23$; the split (7) is again exact for a pure power. The exit judgment of §8.2 separates a flat root from a simple one; at a root of order below one $f$ is far from 0 on both sides and the exit fires falsely, so the memory of this branch is *not* cleared by it; clearing it was measured on the development corpora and was worse (recorded in the pre-registration amendment of this branch).
**Borrowed: the tolerance step.** Once the root is reached from one side the bracket's far end can stay far: on $\sin$ over $[3,4]$ the base rule reaches $|f|\approx10^{-16}$ in 7 evaluations and then halves the remaining $1.3\cdot10^{-7}$ down to the tolerance in 17 more. Dekker [9] and Brent [2] step at least the tolerance; we do the same, $\varepsilon_x/2$ towards the far end, when the model's step is shorter — but only after the model has *kept* its previous promise ($r<\tfrac14$), because at the start of a run a slope dominated by a huge far value promises the root at an end. This element is not ours and is named as such.
### 8.5 The proposed method
The method this paper proposes, **A′**, is the base rule of §3 — tent similarity, three-point scale, projection with the declared multiplier $c=2$ — with the regime of §8.2–8.3 (the analogy remembered, its translation limit, the split (7), the exit) and the five readings of §8.4. Its constants are bisection's $\tfrac12$ and what follows from it: $\tfrac14=(1-\tfrac12)^2$ and the two branches $m\ge2$, $m\le\tfrac23$ of $|1-1/m|>\tfrac12$. Three choices are declared rather than derived: the multiplier $c$ of the projection (§3.1), the Euclidean geometry of the $(\xi,\varphi)$ plane, and the pattern test of the step lengthening (lags from 2 to half the history, threshold $\tfrac12$). The projection carries the guarantee, $2\lceil\log_2(w_0/\varepsilon_x)\rceil+3$ evaluations; it is verified with no violation on every corpus of this paper and under every safeguard of the grid.
**Table 8.** A′ on the two sets of §4 (failures charged 200). A′'s wins/ties/losses against each rival over all problems of the set, exact two-sided sign test.
| | APS-154 | CHA-45 | both | A′ per problem, APS | A′ per problem, CHA |
A′ is 12.9% below Chandrupatla on the canonical set and 28.6% below on the wide-bracket set, and wins more problems than it loses against every rival on both. By function family, the unit in which these problems are independent (§9), it is ahead of Chandrupatla on 8 of the 15 canonical families (6 behind, one level) and on 7 of the 9 wide-bracket families — too few families for either count to be significant ($p=0.79$, $0.18$). The two it is behind on are `fun7`, 69 against 64, and `fun9`, 65 against 55. The earned-budget form keeps its constant-free guarantee of bisection plus one step and pays for it as in §3.1.
**Table 9.** Leave-one-out ablation of A′ on all six corpora of §9 (uniform criterion, a sign change within $10^{-6}$). Δ = total without the component − total with it; positive means the component earns its place.
| component removed | APS | CHA | new-1 | Roots.jl | applied | fourth | sum |
Every component is positive in sum; the regime and the split carry the method. Two are marginal and we say so: the step pattern earns on two corpora of six and loses on four, and the sign contradiction is within noise (+28 over 855 problems). Removing both would change A′'s total by under one per cent (117 of 13032 evaluations); they are kept because the pre-registered rule removes only a component that earns nowhere.
**Table 9a.** The similarity in the step of A′: the same method with each of the nine similarity functions of §5.3 in place of the tent, everything else unchanged. Totals on the two sets of §4 and over the six corpora of §9; the guarantee holds with every function.
| $S$ in the step | APS + CHA | six corpora (855) |
|---|---|---|
| **tent $1-|z|$ (A′)** | **3333** | **13032** |
| $e^{-|z|}$ | 3965 | 14142 |
| $1/(1+|z|)$ | 4424 | 14843 |
| $(1+|z|)^{-2}$ | 7249 | 28500 |
| $1-\sqrt{|z|}$ | 11613 | 45552 |
| $1-|z|^{1/3}$ | 16094 | 63031 |
| $e^{-z^2}$ | 17429 | 67701 |
| $1/(1+z^2)$ | 17452 | 67394 |
| $1/\sqrt{1+z^2}$ | 17487 | 67854 |
The nearest other function costs 19% more on the two sets and 9% more over the six corpora, and the functions that approach 1 quadratically cost five times more; the three functions with a linear approach come first and the three with a quadratic approach last, as the contraction law of §2.2 predicts. What this measures should be said plainly: with the tent the step is the undamped Newton step clipped to the bracket, and the other functions damp it, so the table shows that the undamped step is the better one — expected, and evidence for the choice of $S$ within the rule rather than for the similarity framing as such.
### 8.6 What is known, and what is new
Several mechanisms of A′ coincide with classical ones, and the coincidence is worth stating, because they were not taken from the literature but reached from the principles of the paper — similarity to a prototype and analogy as similarity of relations — and recognised afterwards:
| in A′ | classical counterpart |
|---|---|
| the three-point order estimate of §7.1 | Lagouanelle's multiplicity estimate $m=f'^2/(f'^2-ff'')$ [13] |
| the contraction $e(1-1/m)$ behind the flag of §8.1 | multiplicity estimates from successive Newton iterates [14, 15] |
| the order (6) of an analogous pair | the log–log slope of a power law; the (approximate) computational order of convergence [18, 19] |
| the split (7) | false position on $\operatorname{sign}(f)|f|^{1/m}$ [16, 17] |
| the half the step promises | a relative of Brent's acceptance condition [2] |
| the tolerance step | Dekker [9], Brent [2] — borrowed and named as such |
| the exit judgment of §8.2 (the value at a split against the line) | ModAB's linearity check at the midpoint [10, 20], $|y_m-y_3|<k\,(|y_3|+|y_m|)$ with $k$ from a symmetry factor; ours compares two predictions, the line and 0, without a factor |
What we found no counterpart for, in the literature we could reach, is the part that carries the gain in Table 9: the *decision* layer. A bracketing method that recognises the regime in which its model has lost by the similarity of the run to its own past — the recurrence of a dimensionless configuration against the linear prototype — reads the order from the analogous pair itself, distinguishes similarity by scaling from similarity by translation, and leaves the regime by a similarity judgment at a point the model did not choose; and a step whose length is set by a similarity function whose form, measured, is decisive. The methods for multiple roots we found are single-point iterations without a bracket, with the multiplicity assumed, estimated from derivatives, or removed by a transformation ([17]; Roots.jl's `Order1B` and `Order2B` [12]); Chandrupatla takes the decision to halve by an empirical acceptance band [3]. §9.1 puts this claim against the detectors a reader would try first, and against the transformation with the multiplicity known in advance.
## 9. Out-of-sample evidence
Every decision above was taken on the two sets of §4. Pre-registration protects each decision but not the chain: the final configuration — a decision layer of about ten discrete rules, chosen among the many candidates listed in §10 — was selected on the same 199 problems. Four further corpora were therefore used, each fixed and committed before any method was run on it, each with its criteria and stop rule written down first.
1.**New corpus 1** — 206 problems: SciPy's `original` set (6) and five generated classes of 40 (seed 20260921): smooth simple roots, transcendental simple roots, multiple roots $|x-r|^p$ with $p\sim U(2,20)$, exponential flanks, and near-cancelling terms of the `fun9` kind.
2.**Roots.jl / ModAB** — the published bracketing benchmark of the Julia package Roots.jl [12], taken from the ModAB benchmark of Ganchovski and Traykov [10]: 91 problems, of which 13 coincide with APS or Chandrupatla functions (a seven-point identity test) and are excluded, leaving 78, many of them discontinuous or pathological.
3.**Applied and GSL** — the 10 bracketed problems of the GNU Scientific Library's root-finding test suite [11] and 122 equations from physics and engineering on grids fixed beforehand: Kepler's equation (eccentricity up to 0.999), Colebrook–White, Lambert's W, Planck/Wien, van der Waals for CO₂ (including near-critical), internal rate of return, the finite square well.
4.**Corpus 4** — 240 problems in six new families (seed 20260922): random polynomials with a simple root, polynomials with a multiple root, fractional powers $|x-r|^p$ with $p\sim U(0.15,0.95)$, sums of exponentials and sines, compositions $\tanh$, $\arctan$, $\log(1+q^2)$ of a cubic, and functions odd about the root. The code of A′ was committed *before* this corpus was run, and nothing was changed after. The families were chosen by the author, who knew by then where the method is strong: two of the six (multiple roots, fractional powers) are the regime §8 was built for.
A root must carry a sign change to be bracketed, so the multiple roots in these corpora have odd multiplicity (3, 5, 7, 9 in corpus 4) or the form $\operatorname{sign}(x-r)|x-r|^p$ (corpora 1 and 4).
ModAB, the most recent bracketing method we know of, is run from its authors' package (`pymodab` 1.0.9, a wrapper of their C code) with the stopping rule of every other method — a bracket no wider than $\varepsilon_x$, the relative term at machine precision, no stopping on $|f|$ — and counted by the same wrapper around $f$, whose count agrees with the package's own. Where roots are not known in closed form, and for uniformity on all six corpora, a run is solved when $f$ changes sign within $10^{-6}$ of its answer; the $x$-tolerance, the budget and the charge for a failure are those of §4.
**The history is part of the result.** The variant carried to corpora 1–3 was A′ without the tolerance step and the overshoot branch. It transferred to corpus 1 by every pre-registered criterion (total 10% below Chandrupatla, per problem 105/34/67, $p=0.005$), did *not* transfer to Roots.jl (total 6.5% below, but per problem 38/10/30, $p=0.4$) and lost on the applied corpus (14% above). The losses there were traced to two gaps of the base step — no closing of the bracket once the root is reached, and no recognition of roots of order below one — which became the last two readings of §8.4; they were developed on the seen corpora and tested once, on corpus 4.
**Table 10.** A′ against the library rivals on all six corpora (uniform criterion): total evaluations; in brackets the runs that break the $2\times$-bisection bound.
| **corpus 4 (240), the one test** | **177/35/28, $p=10^{-27}$** | **183/23/34** |
Only the last line of corpora is evidence for A′ in the strict sense; the first five were seen while it was developed and are reported as a description. ITP, at its recommended constants and as the per-problem oracle, keeps its own bound of bisection plus $n_0$ everywhere and is behind A′ on every corpus.
**Independence.** A sign test counts problems as independent draws, and not all of them are. In the two canonical sets one function appears with several brackets or parameters, so the independent unit is the function family (15 in APS, 9 in CHA); at that level the counts of Table 8 are not significant, because there are too few families. In corpora 1 and 4 every problem is drawn independently from its family's generator, so the per-problem test is valid — for the population those generators define, which the author chose (above). On corpus 4 A′ is 36% below Chandrupatla and wins 177 problems to 28. Without the two classes aimed at by the last two readings it is 2403 against 3569, per problem 107/31/22 ($p=10^{-14}$); on the simple-root classes alone, 737 against 747 (−1.3%), per problem 43/22/15 ($p=3\cdot10^{-4}$).
**Table 11.** Corpus 4 by class: totals of A′ / Chandrupatla, per problem wins/ties/losses.
| sums of exponentials and sines | 304 | 328 | 23/14/3 |
| $\tanh$, $\arctan$, $\log(1+q^2)$ of a cubic | 503 | 1017 | 25/8/7 |
| odd about the root | 329 | 364 | 33/3/4 |
**What the evidence supports, and what it does not.** Against Brent and TOMS 748 A′ is ahead on every corpus, by 8% to 67% in total, and it stays within its own bound of twice bisection on all 855 problems, a bound that Brent exceeds on 118 and TOMS 748 on 84 — neither promises it. Against ModAB it is ahead on every corpus, in total and problem by problem ($p\le0.0013$), on the one test of corpus 4 by 3656 to 5535 and 183 problems to 34 ($p=7\cdot10^{-26}$), and on ModAB's own benchmark as well — all 91 problems of it, 1507 against 1588, 56/13/22 ($p=1.5\cdot10^{-4}$); ModAB exceeds that bound, which it does not promise either, on 9 problems. Against Chandrupatla it is ahead in total on five corpora and level on the sixth, and ahead problem by problem on all six, significantly on five. The size of the lead is not uniform: it is large on multiple, fractional and nearly flat roots — the regime §8 recognises — and small on simple roots, where it is a matter of per cent. Where it is behind is named: `fun9` (65 against 55), the near-cancelling class of corpus 1 (491 against 391), random polynomials with a simple root in total (433 against 419, though ahead 20 to 12 in problems), and the smooth equations of the applied corpus, where the two are level.
### 9.1 Controls: simpler detectors, and the multiplicity known in advance
Two controls ask whether the gain of §8 comes from the recognition by analogy or only from the split (7) with any reasonable detector. Both were pre-registered, with what the paper would say under every outcome, before the run (`sweep_controls.py`).
**Other detectors in A′.** A′ is run with only its detector swapped; the memory, the split, the exit and the five readings of §8.4 are unchanged. (a) *Lagouanelle's estimate* $m_L=1/(1-\theta)$, $\theta=ff''/f'^2$ from the parabola through the last three points ($m_L=\infty$ for $\theta\ge1$), with the same memory — once seen, held until the exit, a hysteresis — entering at $m_L\ge2$ and at $0<m_L\le\tfrac23$ and splitting at $m_L$. (b) *Chandrupatla's acceptance band*, entering when the band rejects the current triple and splitting at the current $m_L$. (c) *No detector*: the regime is entered at every unkept promise.
**Table 9b.** A′ against its detector swapped: totals on the two canonical sets, on corpus 4 and over the six corpora of §9 (uniform criterion), and A′'s wins/ties/losses on corpus 4 with the exact sign test. No variant breaks the bound.
| detector | APS | CHA | corpus 4 | six corpora | A′ on corpus 4 | $p$ |
On corpus 4, the one test, A′ beats all three in total and problem by problem, as the pre-registered criterion required. The competent alternative is Lagouanelle's estimate: it is slightly better on the canonical set (2235 against 2259), and on corpus 4 A′'s lead over it lies in the multiple-root and fractional-power families (1163 against 1739, 924 against 1207); it is behind on $\log(1+q^2)$ of a cubic (503 against 477) and level on the rest. The band and the regime without a detector are worse than no regime at all. The decision layer by analogy therefore earns its place against the detectors a reader would try first.
**The multiplicity known in advance.** The second control asks how good the classical transformation is when its multiplicity is given. Library Chandrupatla and Brent are run on $g=\operatorname{sign}(f)\,|f|^{1/m^*}$, with $m^*$ the order read at the known root — the slope of $\ln|f|$ against $\ln|x-r|$ on both sides at $h=10^{-5}$, rounded to 0.01, and 1 where either side shows no zero (a jump). It is not an achievable method but an upper bound. On the multiple and fractional roots, $m^*\ge2$ or $m^*\le\tfrac23$ — the branches of §8.1 where a tangent step loses to a halving:
**Table 9c.** Multiple and fractional roots with the multiplicity given: totals, failures charged. A′ wins none of the 148 problems.
| corpus | problems | A′ | Chandrupatla on $g$ | Brent on $g$ | Chandrupatla on $f$ |
|---|---|---|---|---|---|
| Chandrupatla | 20 | 654 | 96 | 66 | 1068 |
| new corpus 1 | 40 | 1229 | 358 | 337 | 1792 |
| corpus 4 | 88 | 2159 | 901 | 844 | 4151 |
With the multiplicity known the transformation is 2.4 to 6.8 times cheaper than A′. A′ is far ahead of Chandrupatla on $f$ itself, which does not know the multiplicity either. On the remaining problems the transformed and untransformed runs differ little and A′ is close to them: ahead on the canonical sets (2259 against 2640, 420 against 437 for Chandrupatla on $g$), behind on corpora 1 and 4 (2061 against 1924, 1497 against 1427). So on multiple roots the contribution of A′ is the estimate of the order without knowing it, and the gap to Table 9c is the price of that estimate — large, and the clearest direction for improving the method.
Every statement in §§A.5–A.6 is reproduced by `diag_fun7_root.py`, `diag_cha_gap.py`, `diag_who_caps_us.py`, `diag_model_order.py`, `diag_model_order_checks.py`, `diag_flat_detectable.py` and `diag_band_by_family.py`, whose outputs are kept beside them.
---
## 10. Negative results
## Appendix B. Wall-clock time, and why it is not the cost model
Evaluations of $f$ are the cost model because in any real problem $f$ is the expensive part. Wall-clock time measures something narrower — the method's own arithmetic per step — and it is implementation- and language-bound. We report it because a reader will ask, and because what it shows is instructive.
On the 199 problems as they stand, every $f$ being a formula of a few operations, the base rule is the **slowest** of the pure-Python methods compared, at 32.8 ms against 18.4 for Brent, 21.1 for ITP, 14.2 for Ridders and 22.9 for bisection — while using the fewest evaluations of all of them, 4991 against 5932, 5675, 6421 and 9474. Bisection, needing nearly twice the evaluations, finishes in two thirds of our time. (These are the evaluations the timing loop actually spent; for the base rule and bisection, which solve every problem, they equal the totals of §5; Brent here is our pure-Python implementation, which is what is timed.)
That inversion is the point. Making $f$ progressively more expensive by a controlled amount of arithmetic and timing the sweep directly — nothing subtracted, nothing modelled — the base rule overtakes ITP once one call to $f$ costs about 9 µs and Brent at about 20 µs, which on this machine is of the order of a hundred arithmetic operations. Below that the ranking is decided by per-step overhead; above it, by evaluations.
Three caveats, all of them limitations of the measurement rather than of the methods. Timings vary by tens of percent between runs even on an idle machine. An attempt to separate our per-step cost into "algorithm" and "Python call overhead", by timing a flattened rewrite of the same rule, produced a variant that diverged from the original on 42 of the 199 problems and is not quoted. Nothing is subtracted: estimating a per-evaluation overhead by subtracting the measured cost of $f$ returns negative overheads for four of six methods.
---
## Appendix C. Negative results
1.**The prototype-aggregation form of the method loses.** A richer construction — prototypes over *pairs* of evaluated points, softmax weights over the whole memory, similarity on both the value and the abscissa axis — leads on eight hand-picked problems but loses decisively on the real set: 0 wins, 6 ties and 147 losses against Brent. The simplest form of the idea won.
@@ -603,48 +669,68 @@ With the multiplicity known the transformation is 2.4 to 6.8 times cheaper than
5.**The phase detector: derivation sound, estimator not.** Redoing the contraction law without the assumption $|f|\approx|f'(r)|e$ — taking $|f|\approx K e^m$ instead — does produce the march exponent on its own, $p = L/(L-\ln m)$ with $L=\ln(e/D)$, giving $p=1$ at a simple root and dropping to 0.50 and 0.23 as the approach flattens, which is the neighbourhood of the cube root that wins those families. Substituted back it collapses to the multiplier $m$, measurable as $m = 1/(1-\theta)$ with $\theta = f f''/(f')^2$ from three divided differences and no constant — which is Lagouanelle's multiplicity estimate [13]. It improves the eight standard problems (82 against 84) — and loses on the full benchmark, 5336 against 4991. The estimator is the failure: on Alefeld–Potra–Shi, where almost every root is simple and the truth is $m=1$, it reports $m>1$ on 27.9% of steps with excursions to $m=34.8$, against 35.8% on the flat families. 27.9 against 35.8 is no separation. A cruder variant, correcting by the fraction of its own promise the previous step kept, fails everything: the convergence becomes sublinear and the standard problems double to 161.
6.**The harness scores one canonical problem unsolved for every method**, `aps.13.00`, $f(x)=x e^{-1/x^2}$, all of whose derivatives vanish at the root and which underflows to exactly zero throughout $|x|<0.086$. Every method returns a point where $f(x)=0$ exactly — a genuine root — which the harness scores as a failure because it lies more than $10^{-6}$ from the declared root. With the five `fun7` problems of §7.3 it is why this paper scores by a sign change.
6.**The harness scores one canonical problem unsolved for every method**, `aps.13.00`, $f(x)=x e^{-1/x^2}$, all of whose derivatives vanish at the root and which underflows to exactly zero throughout $|x|<0.086$. Every method returns a point where $f(x)=0$ exactly — a genuine root — which the harness scores as a failure because it lies more than $10^{-6}$ from the declared root. With the five `fun7` problems of §A.5 it is why this paper scores by a sign change.
7.**The symmetric ratio similarity degenerates.** For $S(x,y)=\min(|x|,|y|)/\max(|x|,|y|)$ with $y$ the target 0, $S\equiv0$; the only usable reading is $1-|f|/\sigma$, which saturates and stagnates (Table 1, last row).
8.**Placement beats form, and the wrong placement is expensive.** The same "refuse before the step" idea as reading (1) of §7, with refusal decided by *promised bracket progress* instead of by the shape of the model, costs 4258 on the canonical set against 2337 — a difference of 1921 evaluations for one choice of criterion. A secant step routinely collapses the error while barely moving the proven bracket, so bracket progress rejects steps that were excellent; this is the same mechanism that makes the bracket guard of §3.1 cost 7068.
8.**Placement beats form, and the wrong placement is expensive.** The same "refuse before the step" idea as reading (1) of §A.2, with refusal decided by *promised bracket progress* instead of by the shape of the model, costs 4258 on the canonical set against 2337 — a difference of 1921 evaluations for one choice of criterion. A secant step routinely collapses the error while barely moving the proven bracket, so bracket progress rejects steps that were excellent; this is the same mechanism that makes the bracket guard of §3.1 cost 7068.
9.**Making the scale independent of the bracket does not help either.** The natural objection to the derived scale is that it defeats the very principle of a similarity step: with $\sigma=|\hat f'|D$ the normalised residual is $|f|/(|\hat f'|D)$, which on the $2\times10^4$-wide bracket of $x^3-2x-5$ at $x=10^4$ equals 0.167, so $S=0.833$ — "similar" — and the step is small although the root is some five thousand step-lengths away. The similarity is reading what fraction of the *bracket* the Newton step is, not how far the iterate is from the root. Selecting the scale between the two quantities already computed, $\sigma=\min(|\hat f'|D,\max(|f(A)|,|f(B)|))$ — no new constant, a minimum over what is in hand — does give a full-bracket step far away and Newton's step near the root, and on eight hand-picked wide problems it improves 403 evaluations to 390. Over the whole grid it is worse on both sets: its best cell is 3101 on the canonical set against 2539 for the best uncapped cell of the same grid, and 1766 on the wide-bracket set against 1752.
10.**Reading the similarity at the measured order does not cure false position.** Since false position's stall is a similarity read at order one (§7.1), the obvious hope is that reading it at the measured order is the parameter-free version of the classical fix. It is not. Unguarded, the correction takes the far-end reading from 127/154 and 10676 to 129/154 and 9855 — two problems and eight per cent — where Illinois, at the price of one constant (halve the stale ordinate), reaches 153/154 and 7045. A sixth of the gap is not the gap. The correction is real and it is in the right place; it is simply much smaller than a constant that was chosen for exactly this failure. *Our* 10676 and the 10560 of Table 2 are not the same algorithm — our iterate starts at the midpoint of the bracket, plain false position at an end — and the two must not be differenced.
10.**Reading the similarity at the measured order does not cure false position.** Since false position's stall is a similarity read at order one (§A.3), the obvious hope is that reading it at the measured order is the parameter-free version of the classical fix. It is not. Unguarded, the correction takes the far-end reading from 127/154 and 10676 to 129/154 and 9855 — two problems and eight per cent — where Illinois [7], at the price of one constant (halve the stale ordinate), reaches 153/154 and 7045. A sixth of the gap is not the gap. The correction is real and it is in the right place; it is simply much smaller than a constant that was chosen for exactly this failure. *Our* 10676 and the 10560 of Table 2 are not the same algorithm — our iterate starts at the midpoint of the bracket, plain false position at an end — and the two must not be differenced.
11.**A logarithmic change of variable does not buy the wide brackets.** Working in $u=\operatorname{sign}(x-c)\log(1+|x-c|/\varepsilon_x)$ makes the step multiplicative, which looked like the right diagnosis of the flat families — for the same similarity, scale and safeguard ($(1+|z|)^{-2}$, two-point, *safe*) the wide-bracket set improves from 2759 to 2598 — but the canonical set gets worse, 5635 to 6749, against the 2634 of the base rule. A march needs a *ratio* of distances while an identity on narrow brackets needs an *absolute* unit, and no parameter-free unit provides both. §7.4 shows the diagnosis itself was wrong: on the flat families the right move is to halve, not to march.
11.**A logarithmic change of variable does not buy the wide brackets.** Working in $u=\operatorname{sign}(x-c)\log(1+|x-c|/\varepsilon_x)$ makes the step multiplicative, which looked like the right diagnosis of the flat families — for the same similarity, scale and safeguard ($(1+|z|)^{-2}$, two-point, *safe*) the wide-bracket set improves from 2759 to 2598 — but the canonical set gets worse, 5635 to 6749, against the 2634 of the base rule. A march needs a *ratio* of distances while an identity on narrow brackets needs an *absolute* unit, and no parameter-free unit provides both. §A.6 shows the diagnosis itself was wrong: on the flat families the right move is to halve, not to march.
12.**Reading the unkept promise one step at a time does not pay.** The flag of §8.1 sees the flat regime, but a halving in place of each flagged step takes the canonical set from 2634 to 2839 and the wide-bracket set only from 2357 to 2243: a simple root far from convergence breaks its promises too. Requiring the promise to fail twice in a row keeps the canonical set (2613) but fires one step late, before the crossing, and replaces the most useful step of the cycle (wide brackets 2388); requiring a creep in one direction as well gives 2688 and 2399.
12.**Reading the unkept promise one step at a time does not pay.** The flag of §6.1 sees the flat regime, but a halving in place of each flagged step takes the canonical set from 2634 to 2839 and the wide-bracket set only from 2357 to 2243: a simple root far from convergence breaks its promises too. Requiring the promise to fail twice in a row keeps the canonical set (2613) but fires one step late, before the crossing, and replaces the most useful step of the cycle (wide brackets 2388); requiring a creep in one direction as well gives 2688 and 2399.
13.**A halving that sticks once begun wins the flat families and loses everything else.** Without an exit the flat families fall to 1057 — pure bisection's level — and the canonical set rises to 6707, because a false entry now lasts to the end of the run. Splitting at the order read from the failed promise, $(1-1/m)^m=r$, changes nothing: that order is $m=\infty$ in 163 of 174 entries (6591).
14.**The model's own point cannot tell a flat root from a simple one.** Judging the evaluated point against the line through the bracket ends and against 0 — the exit judgment of §8.2 — also at *entry* gives 2820 and 1532 against 2935 and 1546 for the exit alone: the model aims at the root, so $f$ is small there by construction and looks flat at a simple root as readily as at a power. The same judgment works at a split, which the model did not choose.
14.**The model's own point cannot tell a flat root from a simple one.** Judging the evaluated point against the line through the bracket ends and against 0 — the exit judgment of §6.2 — also at *entry* gives 2820 and 1532 against 2935 and 1546 for the exit alone: the model aims at the root, so $f$ is small there by construction and looks flat at a simple root as readily as at a power. The same judgment works at a split, which the model did not choose.
15.**The decision reading "defect" does not transfer.** Combined with the regime it was the best configuration on the two development sets (§7.2's defect reading with the flat families of §8), and it passed every pre-registered criterion there. On new corpus 1 it did not: per problem 95/33/78 against Chandrupatla ($p=0.22$), losing the class of exponential flanks 9 to 26. It is therefore not part of A′.
15.**The decision reading "defect" does not transfer.** Combined with the regime it was the best configuration on the two development sets (§A.4's defect reading with the flat families of §6), and it passed every pre-registered criterion there. On new corpus 1 it did not: per problem 95/33/78 against Chandrupatla ($p=0.22$), losing the class of exponential flanks 9 to 26. It is therefore not part of A′.
16.**A component rejected alone can pay in combination.** Remembering the analogy for the run was rejected by its own pre-registration, under the harness scoring of the time (canonical set 2814 against 2806 without the regime; under the sign-change criterion the two are level, 2633 against 2634), and with the split of §8.3 it is the better form (with the step pattern, wide brackets 1250 against 1388 for the analogy read in one state). Composition was measured, not assumed, throughout — which is also why §7's readings (2) and (3), each an improvement, are not combined.
16.**A component rejected alone can pay in combination.** Remembering the analogy for the run was rejected by its own pre-registration, under the harness scoring of the time (canonical set 2814 against 2806 without the regime; under the sign-change criterion the two are level, 2633 against 2634), and with the split of §6.3 it is the better form (with the step pattern, wide brackets 1250 against 1388 for the analogy read in one state). Composition was measured, not assumed, throughout — which is also why §A.2's readings (2) and (3), each an improvement, are not combined.
---
## 11. Conclusion
## Appendix D. Pre-registrations and the frozen code
A root finder can be derived from a similarity function alone, and the derivation shows where the content of such a method actually sits. It is not in the shape of $S$ — nine shapes were tried and which group of them comes first is dictated by a single number, the slope of $1-S$ at the origin. It is not in making the similarity change faster — that overshoots exactly as readily as flatness stagnates. It is in the *scale* against which similarity is judged: chosen, the method is brittle and merely linear; taken from the bracket, it is false position and three centuries old; read off the data, it is superlinear. And the safeguard that makes such a method safe must project the step, not replace it: replacing costs the order, projecting costs nothing on one benchmark and improves the method on the other. The projection's budget multiplier is a free constant, and we declare it: it does not move the canonical result and it does move the wide-bracket one; projecting onto a budget the steps earn instead removes it, at a measured price.
Every rule of §6 and every test of §7 was written down, with its criteria and stop rule, before its code and its run. Table D1 gives, for each document, the commit that first added it and the commit that first added the results its run wrote, both read from git by `check_prereg_order.py`: 25 of the 30 were committed before their results; four were committed together with them, so git cannot order them; one was decided on its smoke run and has no results file. The code of A′ was committed (`8027d7f`) after corpus 4 was defined (`657df10`) and before its results (`6d2531e`); `simroot/pure.py` did not change in between.
The base rule has one declared numerical constant, converges with Muller's order 1.839 and carries a worst case of twice bisection plus one step. On the canonical Alefeld–Potra–Shi set it is level with TOMS 748 in total and 1.5% behind Chandrupatla's method, while winning more problems than it loses against both; on the wide-bracket set Chandrupatla is genuinely stronger (1505 against 2357). §7.4 locates the cause: not the order of the model but the decision to give it up, which Chandrupatla takes by halving and which no reading of a single state takes correctly for our step.
That decision is a similarity too — of the run to its own past. A pure power repeats its configuration under zoom, an exponential under translation, a simple root tends to the line; the analogous pair reads the local order, the order decides between the model and a halving by bisection's own $\tfrac12$, and it says where to split. The method built on it, A′, is ahead of Chandrupatla on both canonical sets in total and problem by problem, and on a corpus fixed after its code was frozen it needs 36% fewer evaluations and wins 177 problems to 28. Its decision layer beats Lagouanelle's estimate, Chandrupatla's band and no detector put in its place; with the multiplicity known in advance the classical transformation is 2.4 to 6.8 times cheaper on multiple roots, which measures what estimating the order costs. The claim the measurements support is a lead over Brent, TOMS 748, ITP and ModAB everywhere, within a bound of twice bisection that Brent, TOMS 748 and ModAB do not promise and exceed, and over Chandrupatla a lead that is large where roots are multiple, fractional or nearly flat and small where they are simple — not superiority by the same margin everywhere. `fun9`, near-cancelling terms and smooth applied equations are where it is behind or level.
This is the same movement as in parameter-free similarity control [6], where membership functions are eliminated by normalising the input space from the data rather than by choosing widths. Here the object normalised is the value axis, and the price of choosing it by hand is measured rather than asserted.
### Future work
Three questions remain, all narrower than the one this section used to pose.
The first is the price of estimating the order. With the multiplicity given, the transformation of §8.3 is 2.4 to 6.8 times cheaper than A′ on multiple and fractional roots (Table 9c): the order read from the analogous pair, or its use in the split, loses most of what the transformation could deliver.
The second is the steep flank. Where the loss remains — `fun9`, the near-cancelling class, 4 − tan x at its pole — the base step approaches along a flank far steeper than its model, and the half contradiction of §8.4 removes only part of the cost. The third is the one geometric choice of §8.2, the Euclidean metric of the $(\xi,\varphi)$ plane: a metric derived from the configuration's own sensitivity would remove it.
**Table D1.** Pre-registrations, in the order they were written.
@@ -670,3 +756,6 @@ The second is the steep flank. Where the loss remains — `fun9`, the near-cance
18. S. Weerakoon and T. G. I. Fernando, "A variant of Newton's method with accelerated third-order convergence," *Appl. Math. Lett.*, vol. 13, pp. 87–93, 2000.
19. A. Cordero and J. R. Torregrosa, "Variants of Newton's method using fifth-order quadrature formulas," *Appl. Math. Comput.*, vol. 190, pp. 686–698, 2007.
20. N. Ganchovski, O. Smith, C. Rackauckas, L. Tomov and A. Traykov, "Improvements to the Modified Anderson–Björck (modAB) root-finding algorithm," *Algorithms*, vol. 19, 332, 2026; package `pymodab` 1.0.9.
21. J. C. P. Bus and T. J. Dekker, "Two efficient algorithms with guaranteed convergence for finding a zero of a function," *ACM Trans. Math. Softw.*, vol. 1, no. 4, pp. 330–345, 1975.
22. J. M. Fernández-Díaz and C. O. Menéndez-Pérez, "A common framework for modified Regula Falsi methods and new methods of this kind," *Math. Comput. Simul.*, vol. 205, pp. 678–696, 2023.
23. A. Cordero, B. Neta and J. R. Torregrosa, "Memorizing Schröder's method as an efficient strategy for estimating roots of unknown multiplicity," *Mathematics*, vol. 9, 2570, 2021.
# ------------------------------------------------------------------ 2. edits (new numbering)
EDITS=[]
defE(old,new):
EDITS.append((old,new))
# revision history and pronouns (revision: "him" -> the method)
E(" An earlier version gave each competitor the better of the library's implementation and ours, problem by problem. That concession backfired in the competitor's favour: our Chandrupatla stopped on a bracket twice as wide as everybody else's — the factor this protocol corrects for ITP in item 1 and had not corrected there — and the per-problem minimum let the looser run win. With the tolerance corrected our implementation and the library agree on all 199 problems exactly.",
" Our own implementations of Chandrupatla's and Brent's methods agree with the libraries; the tables use the libraries.")
E(" For Brent the library is slightly dearer than our implementation; we use the library anyway, because it is the implementation a reader can check.","")
E("Chandrupatla's own method and Dekker's are among the baselines; an earlier version of these tables was drawn up before either was added.",
"Chandrupatla's own method and Dekker's are among the baselines.")
E("produced a variant that diverged from the original on 42 of the 199 problems and was therefore discarded rather than quoted; the question stays open. And an earlier version of this measurement estimated a per-evaluation overhead by subtracting the measured cost of $f$, which returned *negative* overheads for four of six methods — an impossibility that condemned the estimator, and the reason nothing is subtracted here.",
"produced a variant that diverged from the original on 42 of the 199 problems and is not quoted. Nothing is subtracted: estimating a per-evaluation overhead by subtracting the measured cost of $f$ returns negative overheads for four of six methods.")
E("Chandrupatla does not win those families with his model — his traced run bisects",
"Chandrupatla's method does not win those families with its model — its traced run bisects")
E("against his own 297, 233, 233","against its own 297, 233, 233")
E("Chandrupatla halves on up to all of his steps","Chandrupatla's method halves on up to all of its steps")
# the constant, stated once and honestly
E("which §3.1 declares as the method's one free constant.","which §3.1 declares as the base rule's one numerical constant.")
E("**The multiplier is the method's one free numerical constant.** It is not \"the factor already claimed in the bound\" — that argument is circular, the bound is twice bisection because two was chosen — and measurement settles it (`measure_budget_factor.py`, Linux):",
"**The multiplier is the base rule's one numerical constant**, and it is not neutral (`measure_budget_factor.py`, Linux):")
E("one declared constant and the better canonical result, or no constant, a tighter bound and the better wide-bracket result.",
"one declared numerical constant and the better canonical result, or none, a tighter bound and the better wide-bracket result.")
# citations found missing on revision
E("Each of those answers carries constants: Illinois halves the stale ordinate,",
"Each of those answers carries constants: Illinois [7] halves the stale ordinate,")
E("| 15 | Ridders | no |","| 15 | Ridders [5] | no |")
E("where Illinois, at the price of one constant","where Illinois [7], at the price of one constant")
E("Two remarks apply to both forms.\n",
"Three remarks apply to both forms.\n\n- **Guaranteed bounds of this kind are classical.** Bus and Dekker [21] gave bracketing methods whose "
"worst case is four to five times bisection; the scheduled projection guarantees twice bisection plus one "
"step, the earned one bisection plus one step.")
# the decision layer, counted honestly (revision point 2), and the full listing (point 4)
LISTING=r"""
Countingnumericalconstantsdoesnotmeasurethemethod's freedom, however. Beyond these numbers A′ is a decision layer of about ten discrete rules — the flag, the analogy and its memory, the entry, the exit, the overshoot branch, the split, the step pattern, the half, the contradiction, the tolerance step — chosen on the two development sets among the many candidates of Appendix C. That selection is why the method was tested once on a corpus fixed before its code was frozen, and against simpler rules put in its place (§7). Algorithm 2 is the complete method; `simroot/pure.py` and the compiled `simbracket` implement exactly this and agree bit for bit.
E("it is verified with no violation on every corpus of this paper and under every safeguard of the grid.",
"it is verified with no violation on every corpus of this paper and under every safeguard of the grid."+LISTING)
# related work named on revision, found and read
E("| the split (7) | false position on $\\operatorname{sign}(f)|f|^{1/m}$ [16, 17] |",
"| the split (7) | false position on $\\operatorname{sign}(f)|f|^{1/m}$ [16, 17]; inside a bracket, with the multiplicity given, the SFRFm method of [22] |\n"
"| the dimensionless configuration $(\\xi,\\varphi)$ of §6.2 | the ratios of Chandrupatla's acceptance band [3]; every scaled regula falsi written in two dimensionless parameters [22] |")
E("The methods for multiple roots we found are single-point iterations without a bracket, with the multiplicity assumed, estimated from derivatives, or removed by a transformation ([17];",
"The methods for multiple roots we found are single-point iterations without a bracket, with the multiplicity assumed, estimated from derivatives, or removed by a transformation ([17, 23];")
E("Chandrupatla takes the decision to halve by an empirical acceptance band [3].",
"inside a bracket, [22] needs the multiplicity given and recognises a multiple root only by the number of evaluations spent, against a third of bisection's; Chandrupatla takes the decision to halve by an empirical acceptance band [3].")
# the title
E("# Root Finding by Similarity and Analogy: A Bracketing Method with One Declared Constant",
"# Root Finding by Similarity and Analogy: A Bracketing Method with a Data-Selected Decision Layer")
ABSTRACT="""Bisection places its next iterate at the midpoint because it uses nothing about $f$. We replace that rule by a similarity rule — the step grows with how dissimilar $f(x)$ is to the target 0, measured by a similarity $S$ with $S(0)=1$ — and derive its contraction factor in closed form. The derivation fixes a base rule before any benchmark is consulted: with the tent similarity and a three-point divided-difference scale it is a Newton step of Muller's order 1.839, clipped to the bracket and projected onto a budget of twice bisection whose multiplier is its one numerical constant. On the 154 problems of Alefeld, Potra and Shi it is level with TOMS 748 and 1.5% behind Chandrupatla's method; on wide brackets it is clearly behind. The gap is a decision, when to give the model up, and we take it by analogy: at a flat root the dimensionless configuration of the last three points recurs under zoom, at a simple root it tends to a line, and the analogous pair gives the order that says whether and where to split the bracket. This decision layer, about ten discrete rules selected on the two development sets, gives the method A′. On 240 problems fixed before its code was frozen it needs 3656 evaluations, against 5717 for Chandrupatla, 5535 for ModAB and 4488 for A′ with Lagouanelle's multiplicity estimate in place of the analogy. The lead lies in multiple and fractional roots, which two of that corpus's six families contain, and is small on simple roots. With the multiplicity known in advance, the classical transformation $\\operatorname{sign}(f)|f|^{1/m}$ is 2.4 to 6.8 times cheaper on multiple roots."""
INTRO="""## 1. Introduction
Abracketingrootfindermustdecide,ateverystep,whereinsidethebrackettoevaluatenext.Bisectionanswers"in the middle",whichisthecorrectanswerwhennothingisknown.Everyimprovementsincehasanswered"where a local model of $f$ says the root is":asecantinfalseposition,aninversequadraticinBrent[2],acubicinTOMS748[1],aninterpolate–truncate–projectcycleinITP[4],anAnderson–BjörckstepbehindalinearitycheckinModAB[10,20].Eachofthoseanswerscarriesconstants:Illinois[7]halvesthestaleordinate,Brentacceptsaninterpolationsubjecttothreeempiricalinequalities,ITPisgovernedby$\\kappa_1,\\kappa_2,n_0$.
Thispaperasksadifferentquestion.Supposetheonlythingavailableisa*similarityfunction*—amap$S$fromanoutcometo$[0,1]$,where1means"identical to what was wanted"and0means"not similar at all",inthesenseusedforparameter-freecontrolin[6].Canarootfinderbebuiltoutof$S$alone,andhowdoesitcomparewithmethodsbuiltoutofpolynomialmodelsandtunedconstants?
2.Projectingthestepontothebisectionbudget,insteadofreplacingit,keepstheorderandaworstcaseoftwicebisectionplusonestep;thebudget's multiplier is the base rule'sonenumericalconstant,andaconstant-freevariantcostsameasuredprice(§3.1,§5).
3.Thebaserule's gap on wide brackets is a decision — when to give the model up — and a similarity of the run to its own past takes it (§6). The resulting method, A′, is tested once on a corpus fixed before its code was frozen, against simpler detectors put in its place, and against the classical transformation with the multiplicity known in advance (§7).
4.Everytableusesonecriterion,asignchangewithin$10^{-6}$oftheanswer,becausethebenchmarks' published roots score six problems unsolved for every method (§4, §A.5).
REFS="""21. J. C. P. Bus and T. J. Dekker, "Two efficient algorithms with guaranteed convergence for finding a zero of a function," *ACM Trans. Math. Softw.*, vol. 1, no. 4, pp. 330–345, 1975.
22.J.M.Fernández-DíazandC.O.Menéndez-Pérez,"A common framework for modified Regula Falsi methods and new methods of this kind,"*Math.Comput.Simul.*,vol.205,pp.678–696,2023.
23.A.Cordero,B.NetaandJ.R.Torregrosa,"Memorizing Schröder's method as an efficient strategy for estimating roots of unknown multiplicity,"*Mathematics*,vol.9,2570,2021."""
defsplit_sections(text):
"""[(heading line or '' for the preamble, body)] in document order."""
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Claude Opus 5