One Model, Any CSP: Graph Neural Networks as Fast Global Search Heuristics for Constraint Satisfaction

• URL: http://arxiv.org/abs/2208.10227v1
• Date: Mon, 22 Aug 2022 12:09:19 GMT
• Title: One Model, Any CSP: Graph Neural Networks as Fast Global Search Heuristics for Constraint Satisfaction
• Authors: Jan T\"onshoff, Berke Kisin, Jakob Lindner, Martin Grohe
• Abstract summary: We propose a universal Graph Neural Network architecture which can be trained as an end-2-end search for any Constraint Satisfaction Problem (CSP) Our architecture can be trained unsupervised with policy gradient descent to generate problem specifics for any CSP in a purely data driven manner.
• Score: 1.9813182042770607
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a universal Graph Neural Network architecture which can be trained as an end-2-end search heuristic for any Constraint Satisfaction Problem (CSP). Our architecture can be trained unsupervised with policy gradient descent to generate problem specific heuristics for any CSP in a purely data driven manner. The approach is based on a novel graph representation for CSPs that is both generic and compact and enables us to process every possible CSP instance with one GNN, regardless of constraint arity, relations or domain size. Unlike previous RL-based methods, we operate on a global search action space and allow our GNN to modify any number of variables in every step of the stochastic search. This enables our method to properly leverage the inherent parallelism of GNNs. We perform a thorough empirical evaluation where we learn heuristics for well known and important CSPs from random data, including graph coloring, MaxCut, 3-SAT and MAX-k-SAT. Our approach outperforms prior approaches for neural combinatorial optimization by a substantial margin. It can compete with, and even improve upon, conventional search heuristics on test instances that are several orders of magnitude larger and structurally more complex than those seen during training.

Related papers

• Assessing and Enhancing Graph Neural Networks for Combinatorial Optimization: Novel Approaches and Application in Maximum Independent Set Problems [0.0]
  Graph Neural Networks (GNNs) show promise for researchers in solving Combinatorial optimization (CO) problems. This study investigates the effectiveness of GNNs in solving the maximum independent set (MIS) problem.
  arXiv  Detail & Related papers  (2024-11-06T09:12:31Z)
• Scalable Graph Compressed Convolutions [68.85227170390864]
  We propose a differentiable method that applies permutations to calibrate input graphs for Euclidean convolution. Based on the graph calibration, we propose the Compressed Convolution Network (CoCN) for hierarchical graph representation learning.
  arXiv  Detail & Related papers  (2024-07-26T03:14:13Z)
• A Benchmark for Maximum Cut: Towards Standardization of the Evaluation of Learned Heuristics for Combinatorial Optimization [12.016449555335976]
  We propose an open-source benchmark suite MaxCut-Bench dedicated to the NP-hard Maximum Cut problem in both its weighted and unweighted variants. We use the benchmark in an attempt to systematically corroborate or reproduce the results of several, popular learning-based approaches. Our results show that several of the learneds fail to outperform a naive greedy algorithm, and that only one of them consistently outperforms Tabu Search.
  arXiv  Detail & Related papers  (2024-06-14T19:44:23Z)
• Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNs [13.871690454501389]
  We introduce GCON, a novel GNN architecture that leverages a complex filter bank and localized attention mechanisms to solve CO problems on graphs. GCON is competitive across all tasks and consistently outperforms other specialized GNN-based approaches.
  arXiv  Detail & Related papers  (2024-05-31T00:02:07Z)
• MARLIN: Soft Actor-Critic based Reinforcement Learning for Congestion Control in Real Networks [63.24965775030673]
  We propose a novel Reinforcement Learning (RL) approach to design generic Congestion Control (CC) algorithms. Our solution, MARLIN, uses the Soft Actor-Critic algorithm to maximize both entropy and return. We trained MARLIN on a real network with varying background traffic patterns to overcome the sim-to-real mismatch.
  arXiv  Detail & Related papers  (2023-02-02T18:27:20Z)
• A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking [124.21408098724551]
  Large-scale graph training is a notoriously challenging problem for graph neural networks (GNNs) We present a new ensembling training manner, named EnGCN, to address the existing issues. Our proposed method has achieved new state-of-the-art (SOTA) performance on large-scale datasets.
  arXiv  Detail & Related papers  (2022-10-14T03:43:05Z)
• Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis [94.64007376939735]
  We theoretically characterize the impact of connectivity patterns on the convergence of deep neural networks (DNNs) under gradient descent training. We show that by a simple filtration on "unpromising" connectivity patterns, we can trim down the number of models to evaluate.
  arXiv  Detail & Related papers  (2022-05-11T17:43:54Z)
• Learning to Detect Critical Nodes in Sparse Graphs via Feature Importance Awareness [53.351863569314794]
  The critical node problem (CNP) aims to find a set of critical nodes from a network whose deletion maximally degrades the pairwise connectivity of the residual network. This work proposes a feature importance-aware graph attention network for node representation. It combines it with dueling double deep Q-network to create an end-to-end algorithm to solve CNP for the first time.
  arXiv  Detail & Related papers  (2021-12-03T14:23:05Z)
• IQNAS: Interpretable Integer Quadratic Programming Neural Architecture Search [40.77061519007659]
  A popular approach to find fitting networks is through constrained Neural Architecture Search (NAS) Previous methods use complicated predictors for the accuracy of the network. We introduce Interpretable Quadratic programming Neural Architecture Search (IQNAS)
  arXiv  Detail & Related papers  (2021-10-24T09:45:00Z)
• Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks [100.14670789581811]
  We train a graph convolutional network to fit the performance of sampled sub-networks. With this strategy, we achieve a higher rank correlation coefficient in the selected set of candidates.
  arXiv  Detail & Related papers  (2020-04-17T19:12:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.