Challenge solved#1
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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, |
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Yup, got it. |
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OmMistry25
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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
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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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