d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
39 lines
1.1 KiB
Markdown
39 lines
1.1 KiB
Markdown
# ADR-CE-002: Incremental Coherence Computation
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**Status**: Accepted
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**Date**: 2026-01-22
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**Parent**: ADR-014 Coherence Engine Architecture
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## Context
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Recomputing global coherence energy for every update is O(|E|) where |E| is edge count. For large graphs with frequent updates, this is prohibitive.
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## Decision
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**Incremental computation with stored residuals, subgraph summaries, and global fingerprints.**
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Components:
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1. **Stored residuals**: Cache per-edge residuals, update only affected edges
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2. **Subgraph summaries**: Pre-aggregate energy by scope/namespace
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3. **Global fingerprints**: Hash-based staleness detection
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When node v changes:
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1. Find edges incident to v: O(degree(v))
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2. Recompute only those residuals: O(degree(v) × d)
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3. Update affected subgraph summaries: O(log n)
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## Consequences
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### Benefits
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- Single node update: O(degree × d) instead of O(|E| × d)
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- Fingerprints enable efficient cache invalidation
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- Subgraph summaries support scoped queries
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### Risks
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- Memory overhead for cached residuals
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- Consistency between cache and graph requires careful management
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## References
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- ADR-014: Coherence Engine Architecture, Section 2
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