Summary of Disco: Efficient Diffusion Solver For Large-scale Combinatorial Optimization Problems, by Kexiong Yu et al.
DISCO: Efficient Diffusion Solver for Large-Scale Combinatorial Optimization Problems
by Kexiong Yu, Hang Zhao, Yuhang Huang, Renjiao Yi, Kai Xu, Chenyang Zhu
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an efficient solver for large-scale combinatorial optimization (CO) problems, called DISCO. The authors highlight the limitations of current neural solvers in capturing the multi-modal nature of CO landscapes and the time-consuming denoising processes involved. They claim that DISCO’s approach enhances solution quality by constraining the sampling space to a more meaningful domain guided by solution residues, while preserving the multi-modal properties of output distributions. Additionally, it accelerates the denoising process through an analytically solvable approach, reducing inference time. The authors test DISCO on Traveling Salesman Problems and Maximal Independent Set benchmarks, demonstrating strong performance and inference speed up to 5.28 times faster than other diffusion alternatives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to solve big problems that are hard to find the best answer for. Right now, computers struggle to find good solutions because they look at too many possibilities. The authors make a machine learning model called DISCO that can find better answers and do it faster. It does this by focusing on a smaller area where the good answers are likely to be. This makes it more efficient and accurate than other models. They tested DISCO on two types of problems and found that it worked well, solving them up to 5 times faster than others. |
Keywords
» Artificial intelligence » Diffusion » Inference » Machine learning » Multi modal » Optimization
