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Combinatorial optimization (CO) is naturally discrete, making machine learning based on differentiable optimization inapplicable.
Concerning nonnegative matrices and doubly stochastic matrices
Sinkhorn, R. and Knopp, P · 1967
Earlier work this paper cites.
Probabilistic methods in combinatorics
Erdős, P. and Spencer, J · 1974
Earlier work this paper cites.
On the history of the minimum spanning tree problem
Graham, R. L. and Hell, P · 1985
Earlier work this paper cites.
Expressing combinatorial optimization problems by linear programs
Yannakakis, M · 1988
Earlier work this paper cites.
On the number of successes in independent trials
Wang, Y. H · 1993
Earlier work this paper cites.
A lagrangian-based heuristic for large-scale set covering problems
Ceria, S., Nobili, P., and Sassano, A · 1998
Earlier work this paper cites.
Approximation algorithms for connected dominating sets
Guha, S. and Khuller, S · 1998
Earlier work this paper cites.
The maximum clique problem
Bomze, I. M., Budinich, M., Pardalos, P. M., and Pelillo, M · 1999
Earlier work this paper cites.
The budgeted maximum coverage problem
Khuller, S., Moss, A., and Naor, J. S · 1999
Earlier work this paper cites.
Algorithms for the set covering problem
Caprara, A., Toth, P., and Fischetti, M · 2000
Earlier work this paper cites.
An evolutionary algorithm for large scale set covering problems with application to airline crew scheduling
Marchiori, E. and Steenbeek, A · 2000
Earlier work this paper cites.
The dense k-subgraph problem
Feige, U., Peleg, D., and Kortsarz, G · 2001
Earlier work this paper cites.
An optimal minimum spanning tree algorithm
Pettie, S. and Ramachandran, V · 2002
Earlier work this paper cites.
The robust coloring problem
Yanez, J. and Ramirez, J · 2003
Earlier work this paper cites.
Facility location: applications and theory
Drezner, Z. and Hamacher, H. W · 2004
Earlier work this paper cites.
Classical coloring of graphs
Kosowski, A. and Manuszewski, K · 2004
Earlier work this paper cites.
Facility location and covering problems
Mihelic, J. and Robic, B · 2004
Earlier work this paper cites.
Different formulations for solving the heaviest k-subgraph problem
Billionnet, A · 2005
Earlier work this paper cites.
Robust graph coloring for uncertain supply chain management
Lim, A. and Wang, F · 2005
Earlier work this paper cites.
The traveling salesman problem: a linear programming formulation
Diaby, M · 2006
Earlier work this paper cites.
Random constraint satisfaction: Easy generation of hard (satisfiable) instances
Xu, K., Boussemart, F., Hemery, F., and Lecoutre, C · 2007
Earlier work this paper cites.
Data reduction and exact algorithms for clique cover
Gramm, J., Guo, J., Hüffner, F., and Niedermeier, R · 2009
Earlier work this paper cites.
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Saha, B. and Getoor, L · 2009
Earlier work this paper cites.
Graph coloring problems
Jensen, T. R. and Toft, B · 2011
Earlier work this paper cites.
Learning with submodular functions: A convex optimization perspective
Bach, F. et al · 2013
Earlier work this paper cites.
On computing the distribution function for the poisson binomial distribution
Hong, Y · 2013
Earlier work this paper cites.
Submodular maximization with cardinality constraints
Buchbinder, N., Feldman, M., Naor, J., and Schwartz, R · 2014
Earlier work this paper cites.
A fast minimum spanning tree algorithm based on k-means
Zhong, C., Malinen, M., Miao, D., and Fränti, P · 2015
Earlier work this paper cites.
The probabilistic method
Alon, N. and Spencer, J. H · 2016
Earlier work this paper cites.
Neural combinatorial optimization with reinforcement learning
Bello, I., Pham, H., Le, Q. V., Norouzi, M., and Bengio, S · 2016
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Coverage and mobile sensor placement for vehicles on predetermined routes: A greedy heuristic approach
Ali, J. and Dyo, V · 2017
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Clustering uncertain graphs
Ceccarello, M., Fantozzi, C., Pietracaprina, A., Pucci, G., and Vandin, F · 2017
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On embedding uncertain graphs
Hu, J., Cheng, R., Huang, Z., Fang, Y., and Luo, S · 2017
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Learning combinatorial optimization algorithms over graphs
Khalil, E., Dai, H., Zhang, Y., Dilkina, B., and Song, L · 2017
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Poisson binomial distribution for python (github repository)
Straka, M · 2017
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DIMES: A differentiable meta solver for combinatorial optimization problems
Qiu, R., Sun, Z., and Yang, Y · 2022
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Understanding dropout for graph neural networks
Shu, J., Xi, B., Li, Y., Wu, F., Kamhoua, C., and Ma, J · 2022
Later among the works it cites.
Unsupervised learning for combinatorial optimization with principled objective relaxation
Wang, H. P., Wu, N., Yang, H., Hao, C., and Li, P · 2022
Later among the works it cites.
RL4CO: an extensive reinforcement learning for combinatorial optimization benchmark
Berto, F., Hua, C., Park, J., Kim, M., Kim, H., Son, J., Kim, H., Kim, J., and Park, J · 2023
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Combinatorial optimization and reasoning with graph neural networks
Cappart, Q., Chételat, D., Khalil, E. B., Lodi, A., Morris, C., and Velickovic, P · 2023
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Reinforcement learning for solving the vehicle routing problem
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Physics-inspired optimization for quadratic unconstrained problems using a digital annealer
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Chen, X., Chen, M., Shi, W., Sun, Y., and Zaniolo, C · 2019
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Attention, learn to solve routing problems!
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Differentiation of blackbox combinatorial solvers
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Learning what to defer for maximum independent sets
Ahn, S., Seo, Y., and Shin, J · 2020
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Combinatorial optimization with policy adaptation using latent space search
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Bq-nco: Bisimulation quotienting for generalizable neural combinatorial optimization
Drakulic, D., Michel, S., Mai, F., Sors, A., and Andreoli, J.-M · 2023
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Surco: Learning linear surrogates for combinatorial nonlinear optimization problems
Ferber, A. M., Huang, T., Zha, D., Schubert, M., Steiner, B., Dilkina, B., and Tian, Y · 2023
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Winner takes it all: Training performant RL populations for combinatorial optimization
Grinsztajn, N., Daniel, F.-B., Shikha, S., Bonnet, C., and Barrett, T · 2023
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Gurobi Optimizer Reference Manual, 2023
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Empowering graph representation learning with test-time graph transformation
Jin, W., Zhao, T., Ding, J., Liu, Y., Tang, J., and Shah, N · 2023
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Robust graph clustering via meta weighting for noisy graphs
Jo, H., Bu, F., and Shin, K · 2023
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Neural combinatorial optimization with heavy decoder: Toward large scale generalization
Luo, F., Lin, X., Liu, F., Zhang, Q., and Wang, Z · 2023
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Unsupervised learning for solving the travelling salesman problem
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Challenges and opportunities in deep reinforcement learning with graph neural networks: A comprehensive review of algorithms and applications
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Or-tools v9.7, 2023
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Variational annealing on graphs for combinatorial optimization
Sanokowski, S., Berghammer, W., Hochreiter, S., and Lehner, S. L · 2023
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Meta-sage: Scale meta-learning scheduled adaptation with guided exploration for mitigating scale shift on combinatorial optimization
Son, J., Kim, M., Kim, H., and Park, J · 2023
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Revisiting sampling for combinatorial optimization
Sun, H., Goshvadi, K., Nova, A., Schuurmans, D., and Dai, H · 2023
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DIFUSCO: Graph-based diffusion solvers for combinatorial optimization
Sun, Z. and Yiming, Y · 2023
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Policy-based optimization: Single-step policy gradient method seen as an evolution strategy
Viquerat, J., Duvigneau, R., Meliga, P., Kuhnle, A., and Hachem, E · 2023
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Unsupervised learning for combinatorial optimization needs meta-learning
Wang, H. and Li, P · 2023
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Towards one-shot neural combinatorial solvers: Theoretical and empirical notes on the cardinality-constrained case
Wang, R., Shen, L., Chen, Y., Yang, X., Tao, D., and Yan, J · 2023
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Let the flows tell: Solving graph combinatorial optimization problems with gflownets
Zhang, D., Dai, H., Malkin, N., Courville, A., Bengio, Y., and Pan, L · 2023
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Tackling prevalent conditions in unsupervised combinatorial optimization: Code and datasets
Bu, F., Jo, H., Lee, S. Y., Ahn, S., and Shin, K · 2024
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Expected probabilistic hierarchies, 2024
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Distilling autoregressive models to obtain high-performance non-autoregressive solvers for vehicle routing problems with faster inference speed
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Glop: Learning global partition and local construction for solving large-scale routing problems in real-time
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