A baseline wins. At the comparison noise level, the best baseline beats SolvSRK by at least 10 percentage points of survival, or by at least 0.05 balanced score when survival is tied. Use the winning baseline named on the problem page — not SolvSRK. All verdicts →
The classic two-species ecological oscillator with periodic orbits. A second disadvantage case: SciPy is both more precise on clean runs and more robust under noise.
Lotka (1925); Volterra (1926)
Math used in the published benchmark cell — so you can confirm the name matches the ODE you expect. Not a runnable fixture.
ẋ = a x − b x z ż = d x z − c z
Conservative predator–prey form used in the freeze (Hamiltonian structure).
Fingerprint
Spread: low
Default noise: clean
Recommendation snapshot
Clean best: SciPy Radau (precision)
Noisy best: SciPy
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: Lotka–Volterra predator–prey (lotka_volterra)
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 | SciPy RadauSciPy | 100% | 8.6 | 11,548 | 215 ms | 0.823 |
| 2 | SolvSRK | 100% | 8.3 | 6,803 | 25 ms | 0.817 |
| 3 | Vern7external | 100% | 8.2 | 2,142 | 3.82 s | 0.815 |
| 4 | Vern9external | 100% | 7.7 | 1,906 | 3.84 s | 0.802 |
| 5 | Tsit5external | 100% | 6.9 | 2,682 | 921 ms | 0.784 |
| 6 | SciPy RK45SciPy | 100% | 6.8 | 2,510 | 16 ms | 0.781 |
| 7 | SciPy DOP853SciPy | 100% | 6.7 | 2,006 | 12 ms | 0.777 |
| 8 | SciPy LSODASciPy | 100% | 6.2 | 1,377 | 5 ms | 0.767 |
| 9 | FBDFexternal | 100% | 6.1 | 2,251 | 4.86 s | 0.764 |
| 10 | SciPy RK23SciPy | 100% | 6.1 | 16,823 | 136 ms | 0.763 |
| 11 | SciPy BDFSciPy | 100% | 5.4 | 3,641 | 110 ms | 0.748 |
| 12 | CVODE Adamsexternal | 100% | 5.2 | 1,071 | 14 ms | 0.744 |
| 13 | CVODE BDFexternal | 100% | 5.1 | 1,465 | 17 ms | 0.739 |
| 14 | TRBDF2external | 100% | 3.2 | 13,371 | 5.15 s | 0.696 |
At Clean, best balanced arm is SciPy Radau · SolvSRK survival 100%, SCD 8.3.
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_lotka_volterra_2026,
title = {Resonix Evidence Portal: Lotka–Volterra predator–prey},
author = {{Resonix Labs (Canada) Inc.}},
year = {2026},
howpublished = {\url{https://resonix.tech/evidence/problems/lotka_volterra}},
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