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Combinatorial optimization is a well-established area in operations research and computer science.
Fundamentals of a method for evaluating rail net capacities
T. E. Harris and F. S. Ross · 1955
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Maximal flow through a network
L. R. Ford and D. R. Fulkerson · 1956
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Shortest connection networks and some generalizations
R. C. Prim · 1957
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On a routing problem
R. Bellman · 1958
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A note on two problems in connexion with graphs
E. W. Dijkstra et al · 1959
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An automatic method of solving discrete programming problems
A.H. Land and A.G. Doig · 1960
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An improved equivalence algorithm
A. Galler, B and M. J. Fisher · 1964
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Mathematical theory of connecting networks and telephone traffic
V. E. Beneš et al · 1965
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Dynamic programming
R. Bellman · 1966
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The reduction of a graph to canonical form and the algebra which appears therein
B. Weisfeiler and A. Leman · 1968
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Algorithm for solution of a problem of maximum flow in networks with power estimation
E. A. Dinic · 1970
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The complexity of theorem-proving procedures
Stephen A Cook · 1971
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Theoretical improvements in algorithmic efficiency for network flow problems
K. Edmonds and R. M. Karp · 1972
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Reducibility among combinatorial problems
R. M. Karp · 1972
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Efficiency of a good but not linear set union algorithm
R. E. Tarjan · 1975
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A data structure for dynamic trees
D. D. Sleator and R. E. Tarjan · 1983
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“Neural” computation of decisions in optimization problems
J. J. Hopfield and D. W. Tank · 1985
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Local search in routing problems with time windows
M. W. P. Savelsbergh · 1985
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An analogue approach to the travelling salesman problem using an elastic net method
R. Durbin and D. Willshaw · 1987
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Simulated annealing
P. J. M. Van Laarhoven and E. H. L. Aarts · 1987
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A new approach to the maximum-flow problem
A. V. Goldberg and R. E. Tarjan · 1988
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Making data structures persistent
J. R. Driscoll, N. Sarnak, D. D. Sleator, and R. E. Tarjan · 1989
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A learning algorithm for continually running fully recurrent neural networks
R. J. Williams and D. Zipser · 1989
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Vehicle routing with split deliveries
M. Dror, G. Laporte, and P. Trudeau · 1994
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A filtering algorithm for constraints of difference in CSPs
J.-C. Régin · 1994
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Chemnet: A novel neural network based method for graph/property mapping
D. B. Kireev · 1995
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Mapping combinatorial optimization problems onto neural networks
J. Ramanujam and P. Sadayappan · 1995
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A reinforcement learning approach to job-shop scheduling
W. Zhang and T. G. Dietterich · 1995
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Polynomial time approximation schemes for Euclidean TSP and other geometric problems
Sanjeev Arora · 1996
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Softmax to softassign: Neural network algorithms for combinatorial optimization
S. Gold, A. Rangarajan, et al · 1996
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Introduction to linear optimization
D. Bertsimas and J.N. Tsitsiklis · 1997
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Variable neighborhood search
N. Mladenović and P. Hansen · 1997
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Supervised neural networks for the classification of structures
A. Sperduti and A. Starita · 1997
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Tabu search
F. Glover and M. Laguna · 1998
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Neural networks for combinatorial optimization: a review of more than a decade of research
K. A. Smith · 1999
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Policy gradient methods for reinforcement learning with function approximation
R. S. Sutton, D. McAllester, S. Singh, and Y. Mansour · 1999
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Sat based predicate abstraction for hardware verification
E. Clarke, M. Talupur, H. Veith, and D. Wang · 2003
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Local branching
M. Fischetti and A. Lodi · 2003
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Global constraints and filtering algorithms
J.-C. Régin · 2004
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Global constraint catalog
N. Beldiceanu, M. Carlsson, and J.-X. Rampon · 2005
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A better approximation ratio for the vertex cover problem
G. Karakostas · 2005
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Automatic generation of complementary descriptors with molecular graph networks
C. Merkwirth and T. Lengauer · 2005
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A survey of recent advances in sat-based formal verification
M. R. Prasad, A. Biere, and A. Gupta · 2005
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Handbook of metaheuristics , volume 57
F. W. Glover and G. A. Kochenberger · 2006
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SAT-based verification methods and applications in hardware verification
A. Gupta, M. K. Ganai, and C. Wang · 2006
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Managing the complexity of large free and open source package-based software distributions
F. Mancinelli, J. Boender, R. Di Cosmo, J. Vouillon, B. Durak, X. Leroy, and R. Treinen · 2006
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Handbook of constraint programming
F. Rossi, P. Van Beek, and T. Walsh · 2006
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Opium: Optimal package install/uninstall manager
C. Tucker, D. Shuffelton, R. Jhala, and S. Lerner · 2007
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Z3: An efficient smt solver
Leonardo de Moura and Nikolaj Bjørner · 2008
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Curriculum learning
Y. Bengio, J. Louradour, R. Collobert, and J. Weston · 2009
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Introduction to Algorithms
T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein · 2009
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The graph neural network model
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini · 2009
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Mixed integer programming computation
Andrea Lodi · 2010
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Approximation Algorithms
V. V. Vazirani · 2010
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Complexity and approximation: Combinatorial optimization problems and their approximability properties
Giorgio Ausiello, Pierluigi Crescenzi, Giorgio Gambosi, Viggo Kann, Alberto Marchetti-Spaccamela, and Marco Protasi · 2012
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A survey of Monte Carlo tree search methods
C. B. Browne, E. Powley, D. Whitehouse, S. M. Lucas, P. I. Cowling, P. Rohlfshagen, S. Tavener, D. Perez, S. Samothrakis, and S. Colton · 2012
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Multiple choice learning: Learning to produce multiple structured outputs
A. Guzman-Rivera, D. Batra, and P. Kohli · 2012
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Combinatorial Optimization: Theory and Algorithms
B. Korte and J. Vygen · 2012
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Foundations of Machine Learning
M. Mohri, A. Rostamizadeh, and A. Talwalkar · 2012
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A survey on optimization metaheuristics
I. Boussaïd, J. Lepagnot, and P. Siarry · 2013
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The heuristic (dark) side of MIP solvers
A. Lodi · 2013
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Interactive Learning for Sequential Decisions and Predictions
S. Ross · 2013
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Survey of local algorithms
J. Suomela · 2013
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Spectral networks and deep locally connected networks on graphs
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun · 2014
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A brief introduction to exact, approximation, and heuristic algorithms for solving hard combinatorial optimization problems
P. Festa · 2014
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A. Graves, G. Wayne, and I. Danihelka · 2014
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Understanding Machine Learning: From Theory to Algorithms
S. Shalev-Shwartz and S. Ben-David · 2014
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Vehicle routing: problems, methods, and applications
P. Toth and S. Vigo · 2014
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W. Zaremba and I. Sutskever · 2014
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On the power of color refinement
V. Arvind, J. Köbler, G. Rattan, and O. Verbitsky · 2015
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Convolutional networks on graphs for learning molecular fingerprints
D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams · 2015
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Flows in networks
L. R. Ford and D. R. Fulkerson · 2015
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L. Kaiser and I. Sutskever · 2015
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K. Kurach, M. Andrychowicz, and I. Sutskever · 2015
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Neural programmer-interpreters
S. Reed and N. De Freitas · 2015
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Pointer networks
O. Vinyals, M. Fortunato, and N. Jaitly · 2015
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Decision diagrams for optimization , volume 1
D. Bergman, A. A. Cire, W.-J. Van Hoeve, and J. Hooker · 2016
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G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba · 2016
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Discriminative embeddings of latent variable models for structured data
H. Dai, B. Dai, and L. Song · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
M. Defferrard, X. Bresson, and P. Vandergheynst · 2016
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Hybrid computing using a neural network with dynamic external memory
A. Graves, G. Wayne, M. Reynolds, T. Harley, I. Danihelka, A. Grabska-Barwińska, S. Gómez Colmenarejo, E. Grefenstette, T. Ramalho, J. Agapiou, A. Badia Puigdomènech, K. M. Hermann, Y. Zwols, G. Ostrovski, A. Cain, H. King, C. Summerfield, P. Blunsom, K. Kavukcuoglu, and D. Hassabis · 2016
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Value iteration networks
A. Tamar, Y. Wu, G. Thomas, S. Levine, and P. Abbeel · 2016
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Neural combinatorial optimization with reinforcement learning
I. Bello, H. Pham, Q. V. Le, M. Norouzi, and S. Bengio · 2017
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The SCIP Optimization Suite 7.0
G. Gamrath, D. Anderson, K. Bestuzheva, W.-K. Chen, L. Eifler, M. Gasse, P. Gemander, A. Gleixner, L. Gottwald, K. Halbig, G. Hendel, C. Hojny, T. Koch, P. Le Bodic, S. J. Maher, F. Matter, M. Miltenberger, E. Mühmer, B. Müller, M. E. Pfetsch, F. Schlösser, F. Serrano, Y. Shinano, C. Tawfik, S. Vigerske, F. Wegscheider, D. Weninger, and J. Witzig · 2020
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On the unreasonable effectiveness of sat solvers., 2020
V. Ganesh and M. Y. Vardi · 2020
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Generalization and representational limits of graph neural networks
V. Garg, S. Jegelka, and T. Jaakkola · 2020
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D. Georgiev and P. Lió · 2020
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MIPLIB 2017: Data-Driven Compilation of the 6th Mixed-Integer Programming Library
A. Gleixner, G. Hendel, G. Gamrath, T. Achterberg, M. Bastubbe, T. Berthold, P. M. Christophel, K. Jarck, T. Koch, J. Linderoth, M. Lübbecke, H. D. Mittelmann, D. Ozyurt, T. K. Ralphs, D. Salvagnin, and Y. Shinano · 2020
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X. Bresson and T. Laurent · 2017
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Learning combinatorial optimization algorithms over graphs
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Neural message passing for quantum chemistry
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl · 2017
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Inductive representation learning on large graphs
W. L. Hamilton, R. Ying, and J. Leskovec · 2017
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Imitation learning: A survey of learning methods
A. Hussein, M. M. Gaber, E. Elyan, and C. Jayne · 2017
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In-datacenter performance analysis of a tensor processing unit
N. P. Jouppi, C. Young, N. Patil, D. Patterson, G. Agrawal, R. Bajwa, S. Bates, S. Bhatia, N. Boden, A. Borchers, et al · 2017
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
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Erdos goes neural: an unsupervised learning framework for combinatorial optimization on graphs
N. Karalias and A. Loukas · 2020
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Can Q-learning with graph networks learn a generalizable branching heuristic for a SAT solver?
V. Kurin, S. Godil, S. Whiteson, and B. Catanzaro · 2020
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Graph neural networks meet neural-symbolic computing: A survey and perspective
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