Summary of Milp-studio: Milp Instance Generation Via Block Structure Decomposition, by Haoyang Liu et al.
MILP-StuDio: MILP Instance Generation via Block Structure Decomposition
by Haoyang Liu, Jie Wang, Wanbo Zhang, Zijie Geng, Yufei Kuang, Xijun Li, Bin Li, Yongdong Zhang, Feng Wu
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Discrete Mathematics (cs.DM)
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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 a novel framework, Block Structure Decomposition (MILP-StuDio), to generate high-quality Mixed-integer Linear Programming (MILP) instances that preserve specific block structures in constraint coefficient matrices (CCMs). The framework decomposes instances into block units and uses three operators to construct new instances by removing, substituting, or appending these units. This approach aims to address the limitation of existing generation techniques, which can generate computationally trivial or infeasible instances due to disruptions of block structures. MILP-StuDio demonstrates strong ability to preserve feasibility and computational hardness of generated instances. Experimental results on benchmarks show that using instances generated by MILP-StuDio reduces solving time for learning-based solvers by over 10%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at solving complex math problems called Mixed-integer Linear Programming (MILP) problems. Right now, it’s hard to get enough good data to help the computer solve these problems faster. So, researchers came up with a way to create more MILP problems that are similar to real ones. They want to make sure these new problems are not too easy or impossible for the computer to solve. They developed a special method called Block Structure Decomposition (MILP-StuDio) to create these new problems. This approach helps preserve the original problem’s difficulty and feasibility, making it more efficient for computers to solve. |
