Overview Evidence tour Methodology Dead zones Problems

Relay feedback system

PARITYS0 · dim 2

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 →

Piecewise-smooth switching system; limit cycle from relay nonlinearity

Oscillators & nonlinear dynamics

Problem definition

Canonical benchmark implementation

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 _relay_feedback_rhs(t, y):
    x1, x2 = y
    u = 1.0 if x1 < 0 else -1.0
    return np.array([x2, -x2 + u])

Parameters: No captured parameters; constants are explicit in the RHS excerpt.
Initial condition: y(0) = [0.5, 0]
Horizon: t ∈ [0, 20]

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: SciPy Radau

Noisy best: SciPy RK23

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: Relay feedback system (relay-feedback-system)

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%	—	518,183	7.20 s	0.809
2	SciPy RK45SciPy	100%	—	209,222	765 ms	0.809
3	SciPy DOP853SciPy	100%	—	422,414	1.44 s	0.809
4	SciPy RK23SciPy	100%	—	111,728	519 ms	0.809
5	CVODE BDFexternal	100%	—	78,907	306 ms	0.809
6	CVODE Adamsexternal	100%	—	64,718	245 ms	0.809
7	Tsit5external	100%	—	317,220	18.24 s	0.809
8	Vern7external	100%	—	616,712	6.73 s	0.809
9	Vern9external	100%	—	1,099,618	10.13 s	0.809
10	TRBDF2external	100%	—	33,670	3.93 s	0.809
11	FBDFexternal	100%	—	114,025	4.52 s	0.809
12	SolvSRK	100%	—	181,144	376 ms	0.809
—	SciPy BDFSciPy	0%	—	—	—	—
—	SciPy LSODASciPy	0%	—	—	—	—

At Clean, best balanced arm is SciPy Radau · SolvSRK survival 100%.

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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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_relay_feedback_system_2026,
  title        = {Resonix Evidence Portal: Relay feedback system},
  author       = {{Resonix Labs (Canada) Inc.}},
  year         = {2026},
  howpublished = {\url{https://resonix.tech/evidence/problems/relay-feedback-system}},
  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