Moog ladder filter

DISADVANTAGES1 · dim 4

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 →

4-stage tanh transistor ladder filter; resonance near self-oscillation at k≈4

Audio & music electronics

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 rhs(t: float, y: np.ndarray) -> np.ndarray:
    V = y.copy()
    # Input: 440 Hz tone
    u = 0.5 * np.sin(2.0 * np.pi * 440.0 * t)
    stages = np.tanh(V)
    dV = np.empty(4)
    dV[0] = omega_c * (np.tanh(u - k * V[3]) - stages[0])
    dV[1] = omega_c * (stages[0] - stages[1])
    dV[2] = omega_c * (stages[1] - stages[2])
    dV[3] = omega_c * (stages[2] - stages[3])
    return dV
Parameters
  • k = 3.8
  • omega_c = 6283.18530718
Initial condition
y(0) = [0, 0, 0, 0]
Horizon
t ∈ [0, 0.1]

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: Vern9

Noisy best: SciPy BDF

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: Moog ladder filter (moog-ladder-filter)

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
1Vern9external
100%
9.924,2744.18 s0.855
2Vern7external
100%
9.120,3724.14 s0.836
3SciPy DOP853SciPy
100%
9.016,178113 ms0.834
4SciPy RadauSciPy
100%
9.036,982729 ms0.833
5SolvSRK
100%
8.924,631158 ms0.832
6Tsit5external
100%
7.619,5662.34 s0.801
7SciPy RK45SciPy
100%
7.523,000177 ms0.797
8CVODE Adamsexternal
100%
6.93,26233 ms0.784
9SciPy BDFSciPy
100%
6.210,438372 ms0.766
10SciPy LSODASciPy
100%
6.17,75138 ms0.765
11SciPy RK23SciPy
100%
6.157,362544 ms0.764
12FBDFexternal
100%
6.015,2434.65 s0.762
13CVODE BDFexternal
100%
6.05,59251 ms0.761
14TRBDF2external
100%
2.310,0225.05 s0.673

At Clean, best balanced arm is Vern9 · SolvSRK survival 100%, SCD 8.9.

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_moog_ladder_filter_2026,
  title        = {Resonix Evidence Portal: Moog ladder filter},
  author       = {{Resonix Labs (Canada) Inc.}},
  year         = {2026},
  howpublished = {\url{https://resonix.tech/evidence/problems/moog-ladder-filter}},
  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