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There have been recent efforts for incorporating Graph Neural Network models for learning full-stack solvers for constraint satisfaction problems (CSP) and particularly Boolean satisfiability (SAT).
Algorithms for constraint-satisfaction problems: A survey
Kumar, V · 1992
Earlier work this paper cites.
Search in a small world
Walsh, T. et al · 1999
Earlier work this paper cites.
Understanding random sat: Beyond the clauses-to-variables ratio
Nudelman, E., Leyton-Brown, K., Hoos, H. H., Devkar, A., and Shoham, Y · 2004
Earlier work this paper cites.
Comparing beliefs, surveys, and random walks
Aurell, E., Gordon, U., and Kirkpatrick, S · 2005
Earlier work this paper cites.
Survey propagation: An algorithm for satisfiability
Braunstein, A., Mézard, M., and Zecchina, R · 2005
Earlier work this paper cites.
Survey-propagation decimation through distributed local computations
Chavas, J., Furtlehner, C., Mézard, M., and Zecchina, R · 2005
Earlier work this paper cites.
Modularity and community structure in networks
Newman, M. E · 2006
Earlier work this paper cites.
Gibbs states and the set of solutions of random constraint satisfaction problems
Krzakała, F., Montanari, A., Ricci-Tersenghi, F., Semerjian, G., and Zdeborová, L · 2007
Earlier work this paper cites.
Solving constraint satisfaction problems through belief propagation-guided decimation
Montanari, A., Ricci-Tersenghi, F., and Semerjian, G · 2007
Earlier work this paper cites.
Measuring the hardness of sat instances
Ansótegui, C., Bonet, M. L., Levy, J., and Manya, F · 2008
Earlier work this paper cites.
Satzilla: portfolio-based algorithm selection for sat
Xu, L., Hutter, F., Hoos, H. H., and Leyton-Brown, K · 2008
Earlier work this paper cites.
On the structure of industrial sat instances
Ansótegui, C., Bonet, M. L., and Levy, J · 2009
Earlier work this paper cites.
Restart strategy selection using machine learning techniques
Haim, S. and Walsh, T · 2009
Earlier work this paper cites.
Probabilistic graphical models: principles and techniques
Koller, D., Friedman, N., and Bach, F · 2009
Earlier work this paper cites.
Information, physics, and computation
Mezard, M. and Montanari, A · 2009
Earlier work this paper cites.
Avatarsat: An auto-tuning boolean sat solver
Singh, R., Near, J. P., Ganesh, V., and Rinard, M · 2009
Earlier work this paper cites.
Sequential model-based optimization for general algorithm configuration
Hutter, F., Hoos, H. H., and Leyton-Brown, K · 2011
Earlier work this paper cites.
Learning message-passing inference machines for structured prediction
Ross, S., Munoz, D., Hebert, M., and Bagnell, J. A · 2011
Cited alongside, same era.
The community structure of sat formulas
Ansótegui, C., Giráldez-Cru, J., and Levy, J · 2012
Cited alongside, same era.
Perceptron learning of sat
Flint, A. and Blaschko, M · 2012
Cited alongside, same era.
Predicting satisfiability at the phase transition
Xu, L., Hoos, H. H., and Leyton-Brown, K · 2012
Cited alongside, same era.
On the performance of cdcl-based message passing inspired decimation using ρ \rho σ \sigma pmpi
Gableske, O., Müelich, S., and Diepold, D · 2013
Cited alongside, same era.
Learning to pass expectation propagation messages
Heess, N., Tarlow, D., and Winn, J · 2013
Cited alongside, same era.
Generating sat instances with community structure
Giráldez-Cru, J. and Levy, J · 2016
Later among the works it cites.
Composing graphical models with neural networks for structured representations and fast inference
Johnson, M., Duvenaud, D. K., Wiltschko, A., Adams, R. P., and Datta, S. R · 2016
Later among the works it cites.
Learning rate based branching heuristic for sat solvers
Liang, J. H., Ganesh, V., Poupart, P., and Czarnecki, K · 2016
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Geometric deep learning: going beyond euclidean data
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., and Vandergheynst, P · 2017
Later among the works it cites.
Learning combinatorial optimization algorithms over graphs
Khalil, E., Dai, H., Zhang, Y., Dilkina, B., and Song, L · 2017
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Deep sets
Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R. R., and Smola, A. J · 2017
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Chung, J., Gulcehre, C., Cho, K., and Bengio, Y · 2014
Cited alongside, same era.
Can machine learning learn a decision oracle for np problems? a test on sat
Grozea, C. and Popescu, M · 2014
Cited alongside, same era.
Learning to search in branch and bound algorithms
He, H., Daume III, H., and Eisner, J. M · 2014
Cited alongside, same era.
Impact of community structure on sat solver performance
Newsham, Z., Ganesh, V., Fischmeister, S., Audemard, G., and Simon, L · 2014
Cited alongside, same era.
On the classification of industrial sat families
Jordi, L · 2015
Cited alongside, same era.
The art of computer programming, Volume 4, Fascicle 6: Satisfiability , volume 4
Knuth, D. E · 2015
Cited alongside, same era.
Later among the works it cites.
On the glucose sat solver
Audemard, G. and Simon, L · 2018
Later among the works it cites.
Machine learning for combinatorial optimization: a methodological tour d’horizon
Bengio, Y., Lodi, A., and Prouvost, A · 2018
Later among the works it cites.
Streamlining variational inference for constraint satisfaction problems
Grover, A., Achim, T., and Ermon, S · 2018
Later among the works it cites.
Combinatorial optimization with graph convolutional networks and guided tree search
Li, Z., Chen, Q., and Koltun, V · 2018
Later among the works it cites.
Recurrent relational networks
Palm, R., Paquet, U., and Winther, O · 2018
Later among the works it cites.
Learning to solve np-complete problems-a graph neural network for the decision tsp
Prates, M. O., Avelar, P. H., Lemos, H., Lamb, L., and Vardi, M · 2018
Later among the works it cites.
Inference in probabilistic graphical models by graph neural networks
Yoon, K., Liao, R., Xiong, Y., Zhang, L., Fetaya, E., Urtasun, R., Zemel, R., and Pitkow, X · 2018
Later among the works it cites.
Learning to solve circuit-SAT: An unsupervised differentiable approach
Amizadeh, S., Matusevych, S., and Weimer, M · 2019
Closest in time.
Learning a sat solver from single-bit supervision
Selsam, D., Lamm, M., Bunz, B., Liang, P., de Moura, L., and Dill, D. L · 2019
Closest in time.
A comprehensive survey on graph neural networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Yu, P. S · 2019
Closest in time.