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cloud.google.com·7h ago
AI Research
arXiv CS.AI·25d ago·5 min read
One Color Preprocessing Improves DSATUR
TL;DR
SSLD (Semidefinite Spectral Learning with DSATUR) enhances the DSATUR graph coloring heuristic by preprocessing a color class using Semidefinite Programming, achieving better results in over 1600 benchmark instances. While SSLD is significantly slower than DSATUR, it demonstrates the potential of SDP-guided preprocessing for future improvements.
✦ Why It Matters
Consider implementing SSLD for graph coloring tasks to potentially improve your results significantly.
Key Takeaways
- Explore SSLD for better graph coloring outcomes.
- Utilize Semidefinite Programming for initial color class selection.
- Evaluate performance across various graph types.
- Be aware of the increased runtime when using SSLD.
How It Works
SSLD improves the DSATUR algorithm by first identifying a promising color class through Semidefinite Programming. This preprocessing step allows for a more efficient coloring of the remaining graph, leveraging the properties of the chosen color class to reduce the overall number of colors used.