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QR Decomposition → 8×8 Braid Bridge
Source: c0rmac/qr-apple-silicon — Metal/AMX QR factorization
Relevance: QR's Householder reflections are structurally isomorphic to braid crossings
The isomorphism
| QR decomposition | Braid pipeline | Our module |
|---|---|---|
| Householder reflector H = I - τvvᵀ | Braid crossing operator | BraidEigensolid.lean |
| Compact WY representation (b=32) | Blocked braid crossings (b=8) | crossStep / sidon addressing |
| AMX 8×8 simdgroup_matrix tiles | Matrix8 type (8×8 Fin 8) | AdjugateMatrix.lean |
| QR factorization A = QR | Braid factorization of shock | BurgersPDE.lean |
| Thin QR (M×K, K×N) | Dimensional Shock Trim | DST(M_D) → (A_r, ε) |
What the AMX tells us about our braid
The Apple AMX coprocessor accelerates 8×8 matrix tiles in hardware. This is
not a coincidence — the 8×8 tile size corresponds to the 8-strand braid
topology (Fin 8 → BraidStrand). Every matrix operation on the AMX is
a batch of 8-dimensional braid crossings.
The qr-streaming-amx kernel's pipeline:
Panel factorisation (b=32 columns)
→ T-matrix construction (Compact WY)
→ Trailing matrix update (grid-parallel, AMX tiles)
→ Q accumulation (WY update)
This maps to our braid pipeline as:
Braid panel factorisation (N=8 strands × b=8 crossings)
→ Sidon T-matrix (sumset collision table)
→ DualQuaternion trailing update (advection + viscosity)
→ Receipt accumulation (energy convergence)
Potential integration
The QR library provides reference implementations of:
- Householder reflection — the 8×8 Householder matrix is exactly a braid crossing operator in matrix form
- Compact WY batching — batches of 32 Householder reflectors = batches of 32 braid crossings, which is 4× the sidon address budget (128/32)
- AMX tile programming — the Metal shaders show exactly how to program 8×8 matrix tiles, which is the native hardware format for our braid pipeline
Reference
- Golub & Van Loan, Matrix Computations §5.1 — Householder QR
- Schreiber & Van Loan, 1989 — Compact WY representation
c0rmac/qr-apple-silicon— Metal/AMX QR implementationAdjugateMatrix.lean— 8×8 matrix operationsBraidEigensolid.lean— braid crossing operators