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Challenge solved#1

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vbtcl merged 14 commits into
partcleda:mainfrom
PrithviSeran:challenge-solved
Sep 30, 2025
Merged

Challenge solved#1
vbtcl merged 14 commits into
partcleda:mainfrom
PrithviSeran:challenge-solved

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@PrithviSeran
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@vbtcl
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vbtcl commented Sep 30, 2025

Can you submit a PR with just the update to the README with your addition to the leaderboard. And sort by overlap, then, wirelength, then runtime,
update instructions to the readme if you'd like if that wasn't clear.

@PrithviSeran
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Can you submit a PR with just the update to the README with your addition to the leaderboard. And sort by overlap, then, wirelength, then runtime, update instructions to the readme if you'd like if that wasn't clear.

Yup, got it.

@vbtcl vbtcl merged commit ff7a358 into partcleda:main Sep 30, 2025
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OmMistry25 added a commit to OmMistry25/intern_challenge that referenced this pull request Sep 30, 2025
…eda#1)

- Implemented multi-scale polynomial penalty in overlap_repulsion_loss()
- Optimized hyperparameters: lambda_overlap=5000-10000, adaptive epochs
- Achieved 0.0177 average overlap (beats previous partcleda#1 by 64.5%)
- Wirelength: 0.4902 (comparable to leader)
- Runtime: 407.78s

Key innovations:
- Quartic + cubic + quadratic + linear penalty terms for robust gradients
- Adaptive epochs (3500-5000) based on design size
- Early stopping when overlap-free for 50 epochs
- Higher learning rate (0.05-0.1) for faster convergence

Test Results: 5/10 perfect (0.0 overlap), average 0.0177 across all tests
ashvin-verma added a commit to ashvin-verma/intern_challenge that referenced this pull request Mar 22, 2026
Pipeline: annealed softplus overlap loss → density penalty → deterministic
legalization → greedy repair. Scalable spatial hash handles 100K+ cells.

Key components in ashvin/:
- solver.py: single-stage annealed solver (softplus beta 0.1→6.0, lambda ramp)
- overlap.py: two-tier spatial hash (macros exhaustive, std cells binned)
- density.py: bilinear density penalty
- legalize.py: row-based packing with macro repair
- repair.py: greedy nudge with brute-force fallback
- config.py: preset configs for optuna tuning
- run_tests.py: instrumented runner with CSV output
- view.py: versioned placement visualizations

Results: 0.0000 overlap on tests 1-10 (verified on alternate seeds).
WL ~0.51 (leaderboard partcleda#1 is 0.13 — Phase 2 target).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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3 participants