No clear winner. The survival gap is under 10 percentage points and the balanced-score gap is under 0.05, so neither SolvSRK nor the best baseline clears the win threshold. Either works — choose on cost, licensing, or integration effort. All verdicts →
Diagonal linear ODE ẏ=diag(λ)·y with 28 eigenvalues log-spaced from −1 to −3e+04. Stiffness ratio = κ. Boundary mapping: stiffness × dimension sweep.
Enright & Pryce (1987) ACM TOMS; Hairer & Wanner (1996) Solving ODEs II, §IV.10
Canonical RHS excerpt from the registered callable used for this benchmark cell. Expand it to verify the state equations; it is not a standalone runnable fixture.
def rhs(t: float, y: np.ndarray) -> np.ndarray:
return lam * yCanonical RHS excerpt captured from the same registered callable used for the published benchmark. Frozen closure values are summarized below; helper imports and solver settings are intentionally omitted.
Fingerprint
Spread: high
Default noise: none
Recommendation snapshot
Clean best: SolvSRK
Noisy best: SolvSRK
Coverage
14 solver arms · clean + 5 noise levels
Ranked on survival, precision, and speed
20 seeds/cell default · 14 arms · TRL 4–5 · simulation-lab validated · this page: Linear eigsweep 28D κ=3e4 (linear-eigsweep-28d-3e4)
Governed SolvTune benchmark freeze; per-arm medians only. RHS definitions and raw trial rows are not published.
Self-reported by Resonix Labs · not independently verified
Pick an objective and a noise level to rank all arms on survival, median SCD, median nfev, and median wall time. Medians across seeds.
Objective
Best overall trade-off of survival, precision, and speed.
Noise level
| # | Solver | Survival | SCD | nfev | Wall | Score |
|---|---|---|---|---|---|---|
| 1 | SolvSRK | 100% | 3.5 | 153,583 | 972 ms | 0.702 |
| 2 | SciPy BDFSciPy | 100% | 0.0 | 1,111 | 37 ms | 0.619 |
| 3 | SciPy RadauSciPy | 100% | 0.0 | 3,333 | 64 ms | 0.619 |
| 4 | SciPy RK45SciPy | 100% | 0.0 | 3,171,656 | 19.10 s | 0.619 |
| 5 | SciPy LSODASciPy | 100% | 0.0 | 2,654 | 7 ms | 0.619 |
| 6 | SciPy DOP853SciPy | 100% | 0.0 | 2,815,574 | 15.15 s | 0.619 |
| 7 | SciPy RK23SciPy | 100% | 0.0 | 1,792,100 | 13.53 s | 0.619 |
| 8 | CVODE BDFexternal | 100% | 0.0 | 945 | 12 ms | 0.619 |
| 9 | CVODE Adamsexternal | 100% | 0.0 | 6,420 | 45 ms | 0.619 |
| — | Tsit5external | 0% | — | — | — | — |
At Clean, best balanced arm is SolvSRK.
Values are medians across seeds, measured by Resonix Labs on Resonix hardware and not independently verified; nfev and wall are on reference lab hardware (indicative). Under injected noise only SolvSRK and the SciPy arms are run. How we measure accuracy → · Verification status →
SolvScout · free
This page shows one published benchmark cell. SolvScout fingerprints your ODE, compares it to the full corpus, and recommends a solver with the same survival / precision / speed ranking you see here — including when a SciPy arm wins.
SolvSRK · 30-day trial
SolvSRK is the stiffness-adaptive integrator behind the SolvSRK column in these tables. Create an account, activate a machine, and take a 30-day trial — same binary you'd ship after purchase.
Replace the access date. Pin the freeze ID and library versions when comparing against a later export. Cite it as what it is — a self-reported vendor benchmark, not an independently verified result. The note field says so; please keep it.
@misc{resonix_evidence_linear_eigsweep_28d_3e4_2026,
title = {Resonix Evidence Portal: Linear eigsweep 28D κ=3e4},
author = {{Resonix Labs (Canada) Inc.}},
year = {2026},
howpublished = {\url{https://resonix.tech/evidence/problems/linear-eigsweep-28d-3e4}},
note = {Self-reported vendor benchmark; internally generated by Resonix Labs and not independently verified. Accessed YYYY-MM-DD. Freeze 2026-08-13; libsolvsrk 2.3.0; SciPy 1.14.}
}TRL 4–5 · simulation-lab validated · 398 problems · 14 solver arms · clean + 5 noise levels
Freeze: 2026-08-13 · scipy 1.14 · libsolvsrk 2.3.0 · Methodology
Self-reported by Resonix Labs · not independently verified · Verification status