Summary of Cycleformer : Tsp Solver Based on Language Modeling, by Jieun Yook et al.
CycleFormer : TSP Solver Based on Language Modeling
by Jieun Yook, Junpyo Seo, Joon Huh, Han Joon Byun, Byung-ro Moon
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The CycleFormer is a novel transformer model specifically designed for the Traveling Salesman Problem (TSP). It addresses the limitations of conventional transformer models when applied to TSP by incorporating unique characteristics of the problem. The model’s architecture is tailored to handle the dynamic and unlimited token set in TSP, unlike traditional language models. CycleFormer achieves state-of-the-art performance on TSP-50 to TSP-500 benchmarks, with a notable reduction of approximately 2.8 times in the optimality gap for TSP-500. The paper’s contributions include a new positional encoding for encoder tokens and circular positional encoding for decoder tokens that consider the cyclic properties of a tour. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The CycleFormer is a new computer model designed to solve a problem called Traveling Salesman Problem (TSP). This problem is about finding the shortest possible route that visits certain cities and returns to the starting point. The traditional way to solve this problem doesn’t work well with a special kind of computer model called transformer models. So, researchers created a new transformer model specifically designed for TSP. This model performs better than existing models in solving this problem. |
Keywords
» Artificial intelligence » Decoder » Encoder » Positional encoding » Token » Transformer
