wenmin-wu/ds-skills/squeeze-compact-local-search
Claude Code and agent skill by wenmin-wu/ds-skills
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Three-stage packing refinement — uniform squeeze toward centroid, greedy compaction per object, then multi-directional local search — to tighten solutions after metaheuristic optimization
Install
npx vskill@latest install wenmin-wu/ds-skills/squeeze-compact-local-searchSKILL.md
Squeeze-Compact-Local Search Pipeline
Overview
After simulated annealing or another metaheuristic finds a feasible packing, three refinement stages can squeeze out additional density: (1) Squeeze — uniformly scale all objects toward the centroid until overlap occurs, (2) Compact — greedily move each object toward the center, accepting if no collision, (3) Local search — try small translations and rotations in 8 directions at decreasing step sizes. Run in sequence; each stage feeds its output to the next.
Quick Start
Workflow
- Squeeze: scale all objects toward centroid with decreasing factor until first overlap
- Compact: for each object, greedily step toward center if no collision
- Local search: 8-directional moves + rotation at decreasing step sizes
- Accept moves only if feasible and score improves (or stays same)
- Chain:
squeeze → compact → local_search, optionally repeat
Key Decisions
- Squeeze step: 0.0005 per step is conservative; larger steps risk missing the tight boundary
- Compact rounds: 50 is usually enough for convergence
- Local search granularity: start coarse (0.1), end fine (0.005) — progressive refinement
- Corner-first: prioritize objects touching the bounding box boundary for bigger gains
References
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Source
- SKILL.md in the repositoryCheckingskills/tabular/squeeze-compact-local-search/SKILL.md
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