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Linear eigsweep 10D κ=1e2

ADVANTAGES1 · dim 10

SolvSRK wins. At the comparison noise level, SolvSRK beats the best baseline by at least 10 percentage points of survival, or by at least 0.05 balanced score when survival is tied. Use SolvSRK for this class of problem. All verdicts →

Diagonal linear ODE ẏ=diag(λ)·y with 10 eigenvalues log-spaced from −1 to −1e+02. Stiffness ratio = κ. Boundary mapping: stiffness × dimension sweep.

Synthetic & linear-stiff benchmarks

Problem definition

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.

Show canonical RHS excerpt
def rhs(t: float, y: np.ndarray) -> np.ndarray:
    return lam * y
Parameters
  • lam = [-1, -1.6681005372, -2.78255940221, -4.64158883361, -7.74263682681, -12.9154966501, -21.5443469003, -35.938136638, -59.9484250319, -100]
Initial condition
y(0) = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
Horizon
t ∈ [0, 50]

Canonical 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: low

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

Versions & freeze

Methodology →
Freeze
2026-08-13
libsolvsrk
2.3.0
SciPy
1.14
SUNDIALS
CVODE (bundled backend)

20 seeds/cell default · 14 arms · TRL 4–5 · simulation-lab validated · this page: Linear eigsweep 10D κ=1e2 (linear-eigsweep-10d-1e2)

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

Results matrix

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

#SolverSurvivalSCDnfevWallScore
1SolvSRK
100%
3.547,089145 ms0.703
2SciPy BDFSciPy
100%
0.068524 ms0.619
3SciPy RadauSciPy
100%
0.02,03539 ms0.619
4SciPy RK45SciPy
100%
0.010,76661 ms0.619
5SciPy LSODASciPy
100%
0.01,3894 ms0.619
6SciPy DOP853SciPy
100%
0.09,60249 ms0.619
7SciPy RK23SciPy
100%
0.07,28953 ms0.619
8CVODE BDFexternal
100%
0.04699 ms0.619
9CVODE Adamsexternal
100%
0.068810 ms0.619
10Tsit5external
100%
0.011,1061.71 s0.619
11Vern7external
100%
0.011,0724.05 s0.619
12Vern9external
100%
0.018,1304.16 s0.619
13TRBDF2external
100%
0.01,1054.96 s0.619
14FBDFexternal
100%
0.05674.87 s0.619

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 →

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SolvSRK · 30-day trial

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Cite this page

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_10d_1e2_2026,
  title        = {Resonix Evidence Portal: Linear eigsweep 10D κ=1e2},
  author       = {{Resonix Labs (Canada) Inc.}},
  year         = {2026},
  howpublished = {\url{https://resonix.tech/evidence/problems/linear-eigsweep-10d-1e2}},
  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.}
}

Related

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