Overview Evidence tour Methodology Dead zones Problems

Lotka–Volterra predator–prey

DISADVANTAGES0 · dim 2

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.

Oscillators & nonlinear dynamics

Problem definition

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

Parameters: a = 1.5
b = 1
c = 3
d = 1
Initial condition: (x, z)(0) = (1, 1)
Horizon: t ∈ [0, 20]

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

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: 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

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

#	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 →

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_lotka_volterra_2026,
  title        = {Resonix Evidence Portal: Lotka–Volterra predator–prey},
  author       = {{Resonix Labs (Canada) Inc.}},
  year         = {2026},
  howpublished = {\url{https://resonixusa.com/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