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base repository: partcleda/intern_challenge
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compare: solution/placement-optimization
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  • 3 commits
  • 2 files changed
  • 2 contributors

Commits on Jan 27, 2026

  1. Implement overlap_repulsion_loss with advanced optimization 

    Implementation details:
- Vectorized overlap detection using ReLU-based pairwise calculation
- log(1+x^2) penalty function for numerical stability and strong gradients
- Adaptive lambda scaling (1x to 5x over training) to escape local minima
- Cosine annealing learning rate scheduler
- Early stopping when all overlaps eliminated
- Increased to 5000 epochs with lr=0.1 for better convergence

Results on tests 1-9:
- Average Overlap: 0.0135 (1.35%)
- Average Wirelength: 0.5424
- 5/9 tests passed (0.0000 overlap)
- Runtime: 520s (~9 minutes)

Note: Tests 10-12 (2k-100k cells) require spatial data structures
for O(N^2) to scale. Current pairwise approach is computationally
infeasible for designs >1000 cells.

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
    @claude
    willpartcl and claude committed Jan 27, 2026
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  2. Add auto-scaling epochs based on problem size 

    - Small designs (<50 cells): 5000 epochs
- Medium designs (50-150 cells): 3000 epochs
- Large designs (150-500 cells): 2000 epochs
- Very large (500-2000 cells): 1000 epochs
- Huge (>2000 cells): 500 epochs

This makes runtime more reasonable for large designs, though
O(N²) pairwise approach still struggles with 1000+ cells.

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
    @claude
    willpartcl and claude committed Jan 27, 2026
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  3. Update leaderboard with results 

    Tests 1-9 results:
- Average Overlap: 0.0135 (1.35%)
- Average Wirelength: 0.5424
- Runtime: 520s
- 5/9 tests with 0.0000 overlap

Tests 10-12 (2k-100k cells) not included due to O(N²)
computational complexity. Requires spatial data structures
for production-scale optimization.

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
    @claude
    willpartcl and claude committed Jan 27, 2026
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