Checks · Maximal Coherence for Prescribed Intrinsic Populations and Youla Values: Two Questions of Gil
Recorded output of the verification program
One block per check. A_free_youla: in 30 instances no random contraction came within 4.7 × 10⁻⁵ of the bound of Theorem 3.1 (max_random_excess is negative), the ascent never passed it beyond roundoff, and it came within 10⁻⁴ of the bound in all 30 (worst_shortfall is the largest gap left). B_prescribed_youla: the same for Theorem 4.1, reached within 10⁻⁶ in all 30. C_inequality_family: the full family and the prefix subfamily match the theorem to 5.6 × 10⁻¹⁷; the two coarser subfamilies exceed it by up to 0.019 and 4.4 × 10⁻⁴, in the units the units line names. D_odd_sets: 8,760 checks, none failed. The file records the library versions, the seed and the program's SHA-256, which matches the published program, but no date.
- Output of
- The verification program (checks/verify_gil.py), shown beside it on that page
- Written by
- verify_gil.py, a program by Claude Fable 5.1 (Anthropic)
- Size
- 1,626 bytes
- SHA-256
66d2230b31078f6d2adc3f357029cf1639b1bfa07a35f42f97cc9a31b4153e2d
The note's pageEvery file published with itThis file on GitHub
Tables
| seed | 20260929 |
|---|---|
| source_sha256 | 2f476e76a830e3da491820cb4cd37e12c9ab3831ea0393797289cf9257561b57 |
versions
| python | 3.12.10 |
|---|---|
| numpy | 2.2.6 |
| scipy | 1.16.3 |
A_free_youla
| instances | 30 |
|---|---|
| random_samples_per_instance | 300 |
| max_random_excess | -4.711275597385489e-05 |
| method | projected gradient ascent, 20 starts x 4000 steps, singular values clipped at 1, final snap to a partial isometry |
| max_optimized_excess | 5.551115123125783e-16 |
| min_optimized_excess | -7.178675857100192e-05 |
| worst_shortfall | 7.178675857100192e-05 |
| instances_within_1e-6 | 19 |
| instances_within_1e-4 | 30 |
B_prescribed_youla
| instances | 30 |
|---|---|
| max_random_excess | 8.004708007547379e-14 |
| method | Nelder-Mead over SO(n) via expm of a skew parameter, 20 starts |
| max_optimized_excess | 1.500466417780899e-13 |
| min_optimized_excess | 1.0842021724855044e-18 |
| worst_shortfall | 0.0 |
| instances_within_1e-6 | 30 |
C_inequality_family
| units | squared cohesion ||N||_F^2 = 2 x (LP value); halve for the LP's own units |
|---|---|
| max_abs_diff_full_family_LP_vs_theorem | 5.551115123125783e-17 |
| max_abs_diff_prefix_family_LP_vs_theorem | 5.551115123125783e-17 |
| max_excess_coarse_family_LP_over_theorem | 0.019409162878746528 |
| max_excess_CB_plus_degree_LP_over_theorem | 0.0004419584168336016 |
| instances_CB_plus_degree_LP_exceeds_theorem | 4 |
| instances | 30 |
D_odd_sets
| checks | 8760 |
|---|---|
| max_excess | -0.032159502235817206 |
JSON
{
"seed": 20260929,
"versions": {
"python": "3.12.10",
"numpy": "2.2.6",
"scipy": "1.16.3"
},
"A_free_youla": {
"instances": 30,
"random_samples_per_instance": 300,
"max_random_excess": -4.711275597385489e-05,
"method": "projected gradient ascent, 20 starts x 4000 steps, singular values clipped at 1, final snap to a partial isometry",
"max_optimized_excess": 5.551115123125783e-16,
"min_optimized_excess": -7.178675857100192e-05,
"worst_shortfall": 7.178675857100192e-05,
"instances_within_1e-6": 19,
"instances_within_1e-4": 30
},
"B_prescribed_youla": {
"instances": 30,
"max_random_excess": 8.004708007547379e-14,
"method": "Nelder-Mead over SO(n) via expm of a skew parameter, 20 starts",
"max_optimized_excess": 1.500466417780899e-13,
"min_optimized_excess": 1.0842021724855044e-18,
"worst_shortfall": 0.0,
"instances_within_1e-6": 30
},
"C_inequality_family": {
"units": "squared cohesion ||N||_F^2 = 2 x (LP value); halve for the LP's own units",
"max_abs_diff_full_family_LP_vs_theorem": 5.551115123125783e-17,
"max_abs_diff_prefix_family_LP_vs_theorem": 5.551115123125783e-17,
"max_excess_coarse_family_LP_over_theorem": 0.019409162878746528,
"max_excess_CB_plus_degree_LP_over_theorem": 0.0004419584168336016,
"instances_CB_plus_degree_LP_exceeds_theorem": 4,
"instances": 30
},
"D_odd_sets": {
"checks": 8760,
"max_excess": -0.032159502235817206
},
"source_sha256": "2f476e76a830e3da491820cb4cd37e12c9ab3831ea0393797289cf9257561b57"
}