Summary of Deep Symbolic Optimization For Combinatorial Optimization: Accelerating Node Selection by Discovering Potential Heuristics, By Hongyu Liu et al.
Deep Symbolic Optimization for Combinatorial Optimization: Accelerating Node Selection by Discovering Potential Heuristics
by Hongyu Liu, Haoyang Liu, Yufei Kuang, Jie Wang, Bin Li
First submitted to arxiv on: 14 Jun 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 A novel deep symbolic optimization learning framework is proposed to improve combinatorial optimization solvers. The framework, called Dso4NS, combines the strengths of traditional Branch-and-Bound (B&B) solvers and deep learning (DL) models. Dso4NS guides the search for mathematical expressions within a high-dimensional discrete symbolic space, incorporating the highest-performing expressions into a solver. This approach leverages data-driven methods to capture rich feature information in input data and generates symbolic expressions that enable fast inference with high interpretability. The learned CPU-based policies outperform existing approaches on a CPU machine, achieving performance comparable to state-of-the-art GPU-based methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of solving math problems is being developed. It combines two old ideas to make something better. This approach helps computers find the best solution by searching through many possible answers and choosing the best one. The new method is called Dso4NS, which stands for “deep symbolic optimization for node selection.” It uses special types of computer programs called neural networks to help solve math problems more efficiently. |
Keywords
* Artificial intelligence * Deep learning * Inference * Optimization
