baseline: the published sim-numerics artifact as it stands (490 tests, verify_article.py checks all 72 paper numbers against sweep_full.json)
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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Claude Opus 5
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# sim-numerics — similarity as a step rule for root finding
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Artifact for the paper `article/sim_numerics.tex`. Every claim in the paper is
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produced by one of the scripts below; nothing in the text is transcribed by
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hand, and `verify_article.py` checks all 72 stated numbers against the run
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data as part of the test suite.
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## Reproducing
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```
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python -m pytest tests/ -q # 490 tests
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python sweep_full.py # the full grid, writes sweep_full.json
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python verify_article.py # every paper number vs the data
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```
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Heavy runs go to a Linux machine (Python 3.12, run detached with a log,
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`setsid nohup ... > sweep.log 2>&1`). **Use one platform for all numbers**: the
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test functions call `libm`, whose last digit differs between Windows and Linux,
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and the same SciPy version scores 2819 for TOMS 748 on Windows against 2820 on
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Linux for the same set. The paper's numbers are the Linux ones.
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## Where each claim comes from
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| Paper | Claim | Script |
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|---|---|---|
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| Prop. 1 | the two-point similarity step **is** regula falsi | `tests/test_simroot.py::test_the_pure_two_point_similarity_step_is_regula_falsi_exactly` |
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| Prop. 2, Table 1 | contraction law, predicted 0.1716 vs measured 0.1716 | `stage0c_why_pure_fails.py` |
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| Cor. 1 | a chosen similarity fails on 5–8 of 8 problems | `stage0b_pure_similarity.py` |
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| Cor. 2 | faster similarity is not better (`sqrt`, `cbrt`) | `stage0d_derived_similarity.py` |
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| Cor. 3 | derived scale ⇒ the step is \|f\|/\|f'\| ⇒ superlinear | `stage0d_derived_similarity.py`, `stage0e_guard_cost.py` |
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| §3 safeguards | bracket vs step vs both, and what each costs | `stage2_guard_modes.py`, `stage3_bound_check.py` |
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| §3 order | 1.584 (2-point) vs 1.944 (3-point) | `stage5_three_point.py` |
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| §3.1 | project vs replace; the guarantee costs nothing | `stage6_projection.py` |
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| §3.1 | the projection invariant, instrumented per step | `stage6b_invariant.py` |
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| §4 fairness | ITP is not handicapped; κ₁ and n₀ sweeps | `stage2b_itp_fairness.py` |
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| Tables 2, 3 | the 154-configuration ranking on both sets | `sweep_full.py` → `report_sweep.py` |
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| Table 4 | the law across the whole grid | `sweep_full.py` → `report_sweep.py` |
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| §6 | approach vs endgame, order, boldness, step quality | `stage4_why.py` |
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| §7.1 | the prototype-aggregation form loses 0/6/147 | `bench_full.py` (`SIM.pair2d.*`) |
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| §7.2 | the 2× bound and high order are incompatible | `stage5_three_point.py`, `tests/test_three_point.py` |
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| §7.4 | the symmetric ratio similarity degenerates | `simroot/pure.py` (`PURE_SIMS["ratio"]` docstring), `stage0d` |
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## Layout
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```
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simroot/pure.py the method: one similarity, one step rule, three guards
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simroot/baselines.py bisection, 4 false-position variants, Ridders, Brent,
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TOMS 748 (SciPy), ITP (own, validated against its theorem)
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simroot/sim.py the prototype-aggregation form — a NEGATIVE result, kept
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because the paper reports it
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stage0*..stage5* the derivation, in the order it was actually established
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bench_full.py 17 methods on both sets
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sweep_full.py 9 similarities x 4 scales x 4 guards + 10 baselines
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report_sweep.py reads sweep_full.json, produces the paper's tables
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verify_article.py asserts the paper against the data (72 checks)
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tests/ 490 tests; every bug found in audit is pinned here
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```
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## Negative results, so they are not reopened
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1. Similarity normalised by the two bracket ends **is** regula falsi, identically.
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2. A faster-changing similarity is not better; what matters is the slope of
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`1 - S` at zero matching the problem, and only the data supplies it.
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3. Prototype-pair aggregation with softmax over the whole memory loses to the
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simplest form: 0 wins / 6 ties / 147 losses against Brent on APS.
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4. "Our safeguard fires less often" — refuted: Brent discards 53% of its
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interpolations on the set it wins, 34% on the set it loses.
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5. "Our steps are bolder" — refuted: on Chandrupatla Brent is bolder (56.3%
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against 41.8%) and still loses. The mechanism is step *quality*: median
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bracket removed 0.275 against 0.058.
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6. Every safeguard that REPLACES the computed step with a midpoint destroys the
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order: with the three-point scale, order falls from 1.94 to 1.00 and the APS
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total from 2806 to 7260. **Resolved** by projecting the step onto the
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admissible interval instead (`guard="project"`), which costs nothing on APS
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(2806 either way) and gains on Chandrupatla (40/45 at 3220 against 35/45 at
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4104 unguarded).
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7. Zero slack in the projection is degenerate: a worst case of exactly bisection
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demands a halving at every step, leaving the projection no freedom.
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8. Writing the projection as a radius around the midpoint instead of a clamp
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loses the invariant to cancellation — a relative 8.9e-5 overshoot on eleven
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Chandrupatla problems.
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```
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