Combinatorial optimization and reasoning with graph neural networks

• URL: http://arxiv.org/abs/2102.09544v1
• Date: Thu, 18 Feb 2021 18:47:20 GMT
• Title: Combinatorial optimization and reasoning with graph neural networks
• Authors: Quentin Cappart, Didier Ch\'etelat, Elias Khalil, Andrea Lodi, Christopher Morris, Petar Veli\v{c}kovi\'c
• Abstract summary: Combinatorial optimization is a well-established area in operations research and computer science. Recent years have seen a surge of interest in using machine learning, especially graph neural networks (GNNs) as a key building block for tasks.
• Score: 7.8107109904672365
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused on solving problem instances in isolation, ignoring the fact that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning, especially graph neural networks (GNNs), as a key building block for combinatorial tasks, either as solvers or as helper functions. GNNs are an inductive bias that effectively encodes combinatorial and relational input due to their permutation-invariance and sparsity awareness. This paper presents a conceptual review of recent key advancements in this emerging field, aiming at both the optimization and machine learning researcher.

Related papers

• Exact Combinatorial Optimization with Temporo-Attentional Graph Neural Networks [17.128882942475]
  We investigate two essential aspects of machine learning algorithms for optimization: temporal characteristics and attention. We argue that for the task of variable selection in the branch-and-bound (B&B) algorithm, incorporating the temporal information as well as the bipartite graph attention improves the solver's performance.
  arXiv  Detail & Related papers  (2023-11-23T08:07:15Z)
• Neural Algorithmic Reasoning for Combinatorial Optimisation [20.36694807847833]
  We propose leveraging recent advancements in neural reasoning to improve the learning of CO problems. We suggest pre-training our neural model on relevant algorithms before training it on CO instances. Our results demonstrate that by using this learning setup, we achieve superior performance compared to non-algorithmically informed deep learning models.
  arXiv  Detail & Related papers  (2023-05-18T13:59:02Z)
• Learning Branching Heuristics from Graph Neural Networks [1.4660170768702356]
  We first propose a new graph neural network (GNN) model designed using a probabilistic method. Our approach introduces a new way of applying GNNs towards enhancing the classical backtracking algorithm used in AI.
  arXiv  Detail & Related papers  (2022-11-26T00:01:01Z)
• Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks [51.8723187709964]
  We study the OOD generalization of neural algorithmic reasoning tasks. The goal is to learn an algorithm from input-output pairs using deep neural networks.
  arXiv  Detail & Related papers  (2022-11-01T18:33:20Z)
• Neural Combinatorial Optimization: a New Player in the Field [69.23334811890919]
  This paper presents a critical analysis on the incorporation of algorithms based on neural networks into the classical optimization framework. A comprehensive study is carried out to analyse the fundamental aspects of such algorithms, including performance, transferability, computational cost and to larger-sized instances.
  arXiv  Detail & Related papers  (2022-05-03T07:54:56Z)
• Inducing Gaussian Process Networks [80.40892394020797]
  We propose inducing Gaussian process networks (IGN), a simple framework for simultaneously learning the feature space as well as the inducing points. The inducing points, in particular, are learned directly in the feature space, enabling a seamless representation of complex structured domains. We report on experimental results for real-world data sets showing that IGNs provide significant advances over state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-04-21T05:27:09Z)
• A Differentiable Approach to Combinatorial Optimization using Dataless Neural Networks [20.170140039052455]
  We propose a radically different approach in that no data is required for training the neural networks that produce the solution. In particular, we reduce the optimization problem to a neural network and employ a dataless training scheme to refine the parameters of the network such that those parameters yield the structure of interest.
  arXiv  Detail & Related papers  (2022-03-15T19:21:31Z)
• The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights [59.93939636422896]
  The ML4CO aims at improving state-of-the-art optimization solvers by replacing key components. The competition featured three challenging tasks: finding the best feasible solution, producing the tightest optimality certificate, and giving an appropriate routing configuration.
  arXiv  Detail & Related papers  (2022-03-04T17:06:00Z)
• GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks [68.61934077627085]
  We introduce GNNRank, a modeling framework compatible with any GNN capable of learning digraph embeddings. We show that our methods attain competitive and often superior performance compared with existing approaches.
  arXiv  Detail & Related papers  (2022-02-01T04:19:50Z)
• Deep Learning Chromatic and Clique Numbers of Graphs [0.0]
  We develop deep learning models to predict the chromatic number and maximum clique size of graphs. In particular, we show that deep neural networks, and in particular convolutional neural networks, obtain strong performance on this problem.
  arXiv  Detail & Related papers  (2021-08-04T02:02:53Z)
• Evaluating Logical Generalization in Graph Neural Networks [59.70452462833374]
  We study the task of logical generalization using graph neural networks (GNNs) Our benchmark suite, GraphLog, requires that learning algorithms perform rule induction in different synthetic logics. We find that the ability for models to generalize and adapt is strongly determined by the diversity of the logical rules they encounter during training.
  arXiv  Detail & Related papers  (2020-03-14T05:45:55Z)

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.