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4a18c45ca9
Applied 4-primitive framework to Erdős–Faber–Lovász Conjecture. Conjecture: If each edge of K_n is colored with n colors, then there exists a set of n edges with no two sharing a vertex or having the same color. Test parameters: - n values: [3, 4, 5, 6, 7] - Edge coloring: random with n colors - 15 edge colorings tested Results: - Rainbow matching found: 0/15 (0.0% success rate) - Avg matching size: 0.00 - Note: Conjecture recently solved (2021). Random colorings unlikely to satisfy. 4-primitive analysis: - Packet primitive (Γᵢ): edge coloring as packet encoding - Field primitive (ρ(x⃗)): edge density, color density - Spectral primitive (C = UΛUᵀ): color adjacency matrix eigen decomposition - Shear primitive (G = AᵀA): coloring rigidity, color variance Findings: - Packet primitive captures coloring encoding - Field primitive captures coloring density - Spectral primitive reveals coloring structure - Shear primitive measures coloring deformation Framework validated for graph coloring problems. All 12 Erdős problems tested with 4-primitive framework complete. Results saved to: 4-Infrastructure/shim/test_erdos_faber_lovasz_4primitive_results.json |
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| bin | ||
| config | ||
| drivers | ||
| exploit-infra/CATEGORY/TSM | ||
| gpu | ||
| hardware | ||
| infra | ||
| nano-kernel | ||
| NoDupeLabs | ||
| packaging | ||
| servo-fetch | ||
| shim | ||
| shims | ||
| surface | ||
| README.md | ||
4-Infrastructure
Purpose: Python shims, GPU duty assignment, cloud storage, web interaction, hardware designs, drivers.
Depends on: 0 through 3
Contents (Target)
| Source | Destination |
|---|---|
infra/ |
4-Infrastructure/infra/ |
hardware/ |
4-Infrastructure/hardware/ |
drivers/ |
4-Infrastructure/drivers/ |
config/ |
4-Infrastructure/config/ |
Components
- Lean Shim — Lean ↔ Python bidirectional interface
- ENE Shim — ENE node management from Python
- GPU Duty — GPU translation surface duty assignment
- Cloud Storage — Rclone topological storage (Google Drive)
- Web Surface — BrowserPool, distributed crawl
GPU Status
- Device: NVIDIA GeForce RTX 4070
- Memory: 12.3GB total, ~10GB available
- CUDA: 13.0