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Learning from obstructions: An effective deep learning approach for minimum vertex cover

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Abstract

Computational intractability has for decades motivated the development of a plethora of methodologies that mainly aim at a quality-time trade-off. The use of Machine Learning has finally emerged as one of the possible tools to obtain approximate solutions to \(\mathcal {N}\mathcal {P}\)-hard optimization problems. Recently, Dai et al. introduced a method for computing such approximate solutions for instances of the Vertex Cover problem. In this paper we consider the effectiveness of selecting a proper training strategy by considering special problem instances called obstructions that we believe carry some intrinsic properties of the problem. Capitalizing on the recent work of Dai et al. on Vertex Cover, and using the same case study as well as 19 other problem instances, we show the utility of using obstructions for training neural networks. Experiments show that training with obstructions results in a surprisingly huge reduction in number of iterations needed for convergence, thus gaining a substantial reduction in the time needed for training the model.

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Data availability

The data sets used in our experiments are publicly available. The graphs used in [11] can be obtained from http://www.memetracker.org/ and the authors’ public github site (https://github.com/Hanjun-Dai/graphnn). The other graphs are obtained from the Stanford Large Network Dataset Collection [25] and the Network Repository [29].

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No funding was received for conducting this study.

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Authors and Affiliations

  1. Lebanese American University, Chouran, Beirut, 1102 2801, Lebanon

    Faisal N. Abu-Khzam & Moussa Haidous

  2. School of Engineering, Information Technology & Environment, Charles Darwin University, Darwin, Northern Territory, Australia

    Mohamed M. Abd El-Wahab

  3. College of Computing and Information Technology, Arab Academy of Science and Technology, Alexandria, Egypt

    Noureldin Yosri

Authors
  1. Faisal N. Abu-Khzam
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  2. Mohamed M. Abd El-Wahab
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  3. Moussa Haidous
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  4. Noureldin Yosri
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Correspondence to Faisal N. Abu-Khzam.

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Abu-Khzam, F.N., Abd El-Wahab, M.M., Haidous, M. et al. Learning from obstructions: An effective deep learning approach for minimum vertex cover. Ann Math Artif Intell (2022). https://doi.org/10.1007/s10472-022-09813-2

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