GMS

Introduction

[paper]

Title: Can Graph Neural Networks Learn to Solve MaxSAT Problem?

Authors: Minghao Liu and Fuqi Jia and Pei Huang and Fan Zhang and Yuchen Sun and Shaowei Cai and Feifei Ma and Jian Zhang

Abstract: With the rapid development of deep learning techniques, various recent work has tried to apply graph neural networks (GNNs) to solve NP-hard problems such as Boolean Satisfiability (SAT), which shows the potential in bridging the gap between machine learning and symbolic reasoning. However, the quality of solutions predicted by GNNs has not been well investigated in the literature. In this paper, we study the capability of GNNs in learning to solve Maximum Satisfiability (MaxSAT) problem, both from theoretical and practical perspectives. We build two kinds of GNN models to learn the solution of MaxSAT instances from benchmarks, and show that GNNs have attractive potential to solve MaxSAT problem through experimental evaluation. We also present a theoretical explanation of the effect that GNNs can learn to solve MaxSAT problem to some extent for the first time, based on the algorithmic alignment theory.

Config

load_split_dataset: True
feature_type: all_one
task: satisfiability
task_type: lcg
task_level: graph
load_field: ["label:float"]
dataset_path: ./dataset/my_3_sat_1000

model_settings:
  model: gms
  input_size: 1
  hidden_size: 128
  output_size: 1
  dropout_ratio: 0
  loss: binary_cross_entropy
  num_fc: 3
  num_round: 32
  dropout_ratio: 0
  sigmoid: True
  pooling: mean

scheduler_settings:
  scheduler: ReduceLROnPlateau
  patience: 10
  factor: 0.5
  mode: min

# train settings
valid_metric: acc
epochs: 100
lr: 1e-4
weight_decay: 1e-10
device: cuda:7
batch_size: 32

#log settings
log_file: ./log/old_gms.log