Improving Variable Orderings of Approximate Decision Diagrams Using Reinforcement Learning
Published Online:4 May 2022https://doi.org/10.1287/ijoc.2022.1194
References
- (2002) Statistical mechanics of complex networks. Rev. Modern Phys. 74(1):47.Crossref, Google Scholar
- (2007) A constraint store based on multivalued decision diagrams. Internat. Conf. Principles Practice Constraint Programming (Springer), 118–132.Google Scholar
- (2016) Neural combinatorial optimization with reinforcement learning. Preprint, submitted November 29, https://arxiv.org/abs/1611.09940.Google Scholar
- (2021) Machine learning for combinatorial optimization: A methodological tour d’horizon. Eur. J. Oper. Res. 290(2):405–421.Crossref, Google Scholar
- (2018) Discrete nonlinear optimization by state-space decompositions. Management Sci. 64(10):4700–4720.Link, Google Scholar
- (2012) Variable ordering for the application of BDDs to the maximum independent set problem. Internat. Conf. Integration Artificial Intelligence (AI) Oper. Res. (OR) Techniques Constraint Programming (Springer), 34–49.Google Scholar
- (2013) Optimization bounds from binary decision diagrams. INFORMS J. Comput. 26(2):253–268.Link, Google Scholar
- (2016a) Decision Diagrams for Optimization (Springer).Crossref, Google Scholar
- (2016b) Discrete optimization with decision diagrams. INFORMS J. Comput. 28(1):47–66.Link, Google Scholar
- (2011) Manipulating MDD relaxations for combinatorial optimization. Achterberg T, Beck JC, eds. Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems 20–35.Crossref, Google Scholar
- (2006) Primal heuristics for mixed integer programs. Doctoral dissertation, Zuse Institute Berlin (ZIB).Google Scholar
- (1986) Graph-based algorithms for Boolean function manipulation. IEEE Transactions Comput. 100(8):677–691.Crossref, Google Scholar
- (2021a) Combinatorial optimization and reasoning with graph neural networks. Zhou ZH, ed. Proc. Thirtieth Internat. Joint Conf. Artificial Intelligence, IJCAI-21 (International Joint Conferences on Artificial Intelligence Organization), 4348–4355.Google Scholar
- (2019) Improving optimization bounds using machine learning: Decision diagrams meet deep reinforcement learning. Proc. Conf. AAAI Artificial Intelligence 33(01):1443–1451.Crossref, Google Scholar
- (2021b) Combining reinforcement learning and constraint programming for combinatorial optimization. Proc. Conf. AAAI Artificial Intelligence 35:3677–3687.Crossref, Google Scholar
- (1981) State-space relaxation procedures for the computation of bounds to routing problems. Networks 11(2):145–164.Crossref, Google Scholar
- (2016) Discriminative embeddings of latent variable models for structured data. Internat. Conf. Machine Learning, 2702–2711.Google Scholar
- (2018) Learning heuristics for the TSP by policy gradient. Internat. Conf. Integration Constraint Programming, Artificial Intelligence Oper. Res. (Springer), 170–181.Google Scholar
- (2002) Benchmarking optimization software with performance profiles. Math. Programming 91(2):201–213.Crossref, Google Scholar
- (2018) Improving the linear programming technique in the search for lower bounds in secret sharing. Annual Internat. Conf. Theory Appl. Cryptographic Techniques (Springer), 597–621.Google Scholar
- (2015) Progress in presolving for mixed integer programming. Math. Programming Comput. 7(4):367–398.Crossref, Google Scholar
- (2019) Exact combinatorial optimization with graph convolutional neural networks. Adv. Neural Inform. Process. Systems 32.Google Scholar
- (1993) Geometric Algorithms and Combinatorial Optimization (Springer).Crossref, Google Scholar
- (2006) Postoptimality analysis for integer programming using binary decision diagrams. GICOLAG Workshop.Google Scholar
- (2008) Exploring Network Structure, Dynamics, and Function Using NetworkX. Technical report, Los Alamos National Laboratory, Los Alamos, NM.Google Scholar
- (2014) Learning to search in branch and bound algorithms. Adv. Neural Inform. Process. Systems 27:3293–3301.Google Scholar
- (2018) Deep reinforcement learning that matters. Proc. AAAI Conf. Artificial Intelligence, volume 32.Google Scholar
- (2011) Sequential model-based optimization for general algorithm configuration. Internat. Conf. Learning Intelligent Optimization (Springer), 507–523.Google Scholar
- (2017) Learning combinatorial optimization algorithms over graphs. Adv. Neural Inform. Process. Systems 30:6351–6361.Google Scholar
- (2014) Adam: A method for stochastic optimization. Preprint, submitted December 22, https://arxiv.org/abs/1412.6980.Google Scholar
- (2019) Attention, learn to solve routing problems! Internat. Conf. Learning Representations.Google Scholar
- (2015) Deep learning. Nature 521(7553):436.Crossref, Google Scholar
- (1959) Representation of switching circuits by binary-decision programs. Bell Syst. Tech. J. 38(4):985–999.Crossref, Google Scholar
- (1992) Self-improving reactive agents based on reinforcement learning, planning and teaching. Machine Learning 8(3–4):293–321.Crossref, Google Scholar
- (2017) On learning and branching: a survey. TOP. 25(2):207–236.Crossref, Google Scholar
- (2018) Revisiting small batch training for deep neural networks. Preprint, submitted April 20, https://arxiv.org/abs/1804.07612.Google Scholar
- (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529.Crossref, Google Scholar
- (2021) Improving branch-and-bound using decision diagrams and reinforcement learning. Internat. Conf. Integration Constraint Programming Artificial Intelligence Oper. Res. (Springer), 446–455.Google Scholar
- (2005) Neural fitted Q iteration–first experiences with a data efficient neural reinforcement learning method. Eur. Conf. Machine Learning (Springer), 317–328.Google Scholar
- (1986) Learning representations by back-propagating errors. Nature 323(6088):533.Crossref, Google Scholar
- (2019) Primal heuristics for branch and price: The assets of diving methods. INFORMS J. Comput. 31(2):251–267.Link, Google Scholar
- (2008) The graph neural network model. IEEE Trans. Neural Network 20(1):61–80.Crossref, Google Scholar
- (2015) Prioritized experience replay. Preprint, submitted November 18, https://arxiv.org/abs/1511.05952.Google Scholar
- (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529(7587):484–489.Crossref, Google Scholar
- (2017) Mastering chess and shogi by self-play with a general reinforcement learning algorithm. Preprint, submitted December 5, https://arxiv.org/abs/1712.01815.Google Scholar
- (2018) Reinforcement Learning: An Introduction (MIT Press).Google Scholar
- (2020) Reinforcement learning for integer programming: Learning to cut. Internat. Conf. Machine Learning (PMLR), 9367–9376.Google Scholar
- (2016) Deep reinforcement learning with double q-learning. Proc. AAAI Conf. Artificial Intelligence, vol. 30.Google Scholar
- (2018) Compact-mdd: efficiently filtering (s) mdd constraints with reversible sparse bit-sets. Proc. 27th Internat. Joint Conf. Artificial Intelligence (AAAI Press), 1383–1389.Google Scholar
- (1992) Q-learning. Machine Learning 8(3–4):279–292.Crossref, Google Scholar

