On Representing Linear Programs by Graph Neural Networks
Authors: Ziang Chen, Jialin Liu, Xinshang Wang, Wotao Yin
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate our results, we train a simple GNN and present its accuracy in mapping LPs to their feasibilities and solutions. |
| Researcher Affiliation | Collaboration | Ziang Chen Department of Mathematics, Duke University Durham, NC 27708 ziang@math.duke.edu Jialin Liu Damo Academy, Alibaba US Bellevue, WA 98004 jialin.liu@alibaba-inc.com |
| Pseudocode | Yes | Algorithm 1 The WL test for LP-Graphs2(denoted by WLLP) |
| Open Source Code | Yes | The codes are modified from Gasse et al. (2019) and can be found in https://github.com/liujl11git/GNN-LP.git. |
| Open Datasets | No | We generate each LP with the following way. We set m = 10 and n = 50. Each matrix A is sparse with 100 nonzero elements whose positions are sampled uniformly and values are sampled normally. |
| Dataset Splits | No | The paper mentions 'training set' and 'testing set' but does not specify a 'validation set' or 'validation split' for the experiments. |
| Hardware Specification | Yes | All the experiments are conducted on a Linux server with an Intel Xeon Platinum 8163 GPU and eight NVIDIA Tesla V100 GPUs. |
| Software Dependencies | No | The paper mentions software like 'Tensor Flow', 'scipy.optimize.linprog', and 'Adam', but it does not provide specific version numbers for these or other key software components used in the experiments. |
| Experiment Setup | Yes | We use Adam (Kingma & Ba, 2014) as our training optimizer with learning rate of 0.0003. ... We set L = 2 for all GNNs and those learnable functions f V in, f W in , fout, f W out, {f V l , f W l , g V l , g W l }L l=0 are all parameterized with MLPs. |