Local Level Lab

Airborne measurements. Open methods.

Bounded-method software screen

This compares two actual optimization strategies on 60 small software starts: TRF (the completed trial's method) and dogbox (a rectangular trust-region method). It reads no archival observations and performs no native integrations. Both use the same original parameter box, Jacobian scaling, 600-evaluation ceiling, fresh gradient check and unchanged cost/residual stability checks.

Known-answer control TRF Dogbox
interior 3/3 3/3
boundary 3/3 3/3
redundant 3/3 3/3
scaled 3/3 3/3
nonlinear 3/3 3/3
gain_offset 3/3 3/3
mixed_interior 3/3 3/3
mixed_boundary 3/3 3/3
coupled_interior 3/3 3/3
coupled_boundary 2/3 3/3
Total 29/30 30/30

Each control has three predetermined starts. The last two controls have 27 coupled parameters, a 100-million-to-one singular-value ratio, and a near-boundary start. Known prediction and objective recovery are checked separately from numerical termination. Unique parameters are checked only where the equations identify them. Both strategies call the real SciPy optimizer; temporary dispatch leaves the completed trial's source unchanged.

Failed software checks

What this establishes

This is a screen, not qualification for the airborne archive. The controls demonstrate behavior on specified equations, not the cause of a particular saved flight's stall. Complete observed Jacobians and rejected-step traces were not saved in the recovery trial. A new observed diagnostic needs a separate source freeze and finite allowance. Neither a successful software test nor an unused completed allowance permits replaying the 41 unfinished stretches.

Run screen_methods.py --render-only to regenerate this page from saved results. Running it without that flag performs this small software screen again; it never starts archival fits. Input/source/environment hashes are in the screen receipt.