10-Target Full Swarm Boundary Probe

ADVANTAGES2 · dim 70

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 →

10 targets: full swarm composition with maneuvering (omega 0.1-0.5 rad/s), cruise (omega 0.01-0.05 rad/s), and hovering (omega~0). Gated on MTTA-6T survival >= 90%. Highest-dim tracking problem in the catalog.

Target tracking

Problem definition

Bar-Shalom et al. (2001) Ch. 11; Blackman & Popoli (1999) Ch. 7-8

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, y):
    d = np.empty(dim)
    for i in range(n_targets):
        base = 7 * i
        x   = y[base]
        yy  = y[base + 1]
        z   = y[base + 2]
        vx  = y[base + 3]
        vy  = y[base + 4]
        vz  = y[base + 5]
        a_m = y[base + 6]

        v_mag = np.sqrt(vx * vx + vy * vy + vz * vz)
        v_safe = max(v_mag, _VEL_FLOOR)
        omega = omegas_arr[i]

        d[base]     = vx
        d[base + 1] = vy
        d[base + 2] = vz
        d[base + 3] = -omega * vy + a_m * vx / v_safe
        d[base + 4] =  omega * vx + a_m * vy / v_safe
        d[base + 5] =               a_m * vz / v_safe
        d[base + 6] = -a_m * inv_tau_a

    return d
Parameters
  • _VEL_FLOOR = 1e-06
  • dim = 70
  • inv_tau_a = 0.2
  • n_targets = 10
  • omegas_arr = [0.1, 0.3, 0.5, 0.25, 0.02, 0.01, 0.05, 0, 0, 0]
Initial condition
y(0) = [1000, 0, 500, 50, 100, 0, …] [shape=(70,), min=-2000, max=4000]
Horizon
t ∈ [0, 60]

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: extreme

Default noise: low

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: 10-Target Full Swarm Boundary Probe (10-target-full-swarm-boundary-probe)

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%
10.46,100265 ms0.867
2SciPy RadauSciPy
100%
10.24,830113 ms0.862
3SciPy LSODASciPy
100%
8.484711 ms0.820
4Tsit5external
100%
8.41,650623 ms0.819
5SciPy DOP853SciPy
100%
8.25307 ms0.814
6SciPy RK45SciPy
100%
7.91,75424 ms0.808
7CVODE Adamsexternal
100%
7.954212 ms0.806
8SciPy RK23SciPy
100%
7.310,646157 ms0.792
9CVODE BDFexternal
100%
7.11,40226 ms0.788
10SciPy BDFSciPy
100%
6.51,16937 ms0.774

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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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

Run the winner on your machine

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.

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_10_target_full_swarm_boundary_probe_2026,
  title        = {Resonix Evidence Portal: 10-Target Full Swarm Boundary Probe},
  author       = {{Resonix Labs (Canada) Inc.}},
  year         = {2026},
  howpublished = {\url{https://resonix.tech/evidence/problems/10-target-full-swarm-boundary-probe}},
  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