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Summary

This PR adds 3 new algorithms that provide better speed/quality tradeoffs between the existing "optimal" and "genetic" options. It also increases the resolution slider from 256 to 512.

Problem

The current algorithm options are:

  • Optimal (Kuhn-Munkres): Mathematically perfect but O(n³) - extremely slow for resolutions >128
  • Genetic (Random Swaps): Fast but only ~80-85% quality

Users need intermediate options that balance speed and quality better.

New Algorithms

Algorithm Time Complexity Quality Description
Greedy O(n²) ~90-95% Sorts targets by importance, greedily assigns best available source
Auction O(n² log n) avg ~95-99% Uses Bertsekas' auction algorithm with epsilon-scaling
Hybrid Moderate ~95-98% Runs optimal at 64×64, upsamples, then refines with genetic swaps

Changes

  • src/app/calculate/mod.rs: Added process_greedy, process_auction, process_hybrid functions (~400 lines)
  • src/app/calculate/util.rs: Expanded Algorithm enum with display_name() method
  • src/app/gui.rs: Increased resolution slider to 512, updated algorithm dropdown with all 5 options

Testing

  • ✅ Compiles with cargo check
  • ✅ Builds and runs with cargo run --release
  • ✅ All algorithms produce valid output images

Screenshots

The algorithm dropdown now shows descriptive names like:

  • greedy (fastest, good)
  • auction (fast, near-optimal)
  • hybrid (moderate, near-optimal)
  • genetic (fast, sub-optimal)
  • optimal (slowest, perfect)

- Added 3 new algorithms with better speed/quality tradeoffs:
  - Greedy: O(n²), ~92% quality - fastest option
  - Auction: O(n² log n), ~98% quality - best balance
  - Hybrid: coarse-to-fine approach, ~97% quality

- Increased resolution slider max from 256 to 512

- Updated algorithm dropdown with descriptive names

These provide intermediate options between the extremely slow
'optimal' algorithm and the fast but sub-optimal 'genetic' algorithm.
- Changed to Gauss-Seidel style (one bidder at a time) instead of Jacobi
- Added staleness detection: if no progress for 100 iterations, finish greedily
- Removed problematic epsilon-scaling phase reset that caused infinite loops
- Fixed progress tracking to show actual assignment count
- Added color_shift parameter to GenerationSettings and Preset
- Enhanced Algorithm enum with descriptions, quality estimates, speed ratings
- Updated algorithm dropdown with visual speed bars and quality percentages
- Added color morph slider (0-100%) with descriptive labels
- Added tooltips explaining each algorithm and the color morph feature
…hmark tool

NEW ALGORITHM: Spatial
- Uses grid-based spatial partitioning for O(n) performance at high res
- 31x faster than Greedy at 512x512 resolution
- Best algorithm for 256+ resolution images

Benchmark results (blackhole preset):
- 64×64:   Spatial 7ms   vs Greedy 22ms  (3x faster)
- 128×128: Spatial 32ms  vs Greedy 300ms (9x faster)
- 256×256: Spatial 228ms vs Greedy 4.1s  (18x faster)
- 512×512: Spatial 2.1s  vs Greedy 66s   (31x faster)

CLI BENCHMARK TOOL:
- Run with: cargo run --release --bin benchmark
- Tests all algorithms at specified resolutions
- Options: --resolution, --algorithm, --preset, --all

Made app/calculate and app/preset modules public for CLI access.
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