wifi-densepose/docs/adr/coherence-engine/ADR-CE-002-incremental-computation.md

ADR-CE-002: Incremental Coherence Computation

Status: Accepted Date: 2026-01-22 Parent: ADR-014 Coherence Engine Architecture

Context

Recomputing global coherence energy for every update is O(|E|) where |E| is edge count. For large graphs with frequent updates, this is prohibitive.

Decision

Incremental computation with stored residuals, subgraph summaries, and global fingerprints.

Components:

1. Stored residuals: Cache per-edge residuals, update only affected edges
2. Subgraph summaries: Pre-aggregate energy by scope/namespace
3. Global fingerprints: Hash-based staleness detection

When node v changes:

1. Find edges incident to v: O(degree(v))
2. Recompute only those residuals: O(degree(v) × d)
3. Update affected subgraph summaries: O(log n)

Consequences

Benefits

• Single node update: O(degree × d) instead of O(|E| × d)
• Fingerprints enable efficient cache invalidation
• Subgraph summaries support scoped queries

Risks

• Memory overhead for cached residuals
• Consistency between cache and graph requires careful management

References

• ADR-014: Coherence Engine Architecture, Section 2