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Combinatorial optimization (CO) is fundamental to several real-world applications, from logistics and scheduling to hardware design and resource allocation.
Sequencing and Scheduling: An Introduction to the Mathematics of the Job-Shop
Graham K. Rand. 1982 · 1982
Earlier work this paper cites.
Routing and scheduling of vehicles and crews
Lawrence Bodin. 1983 · 1983
Earlier work this paper cites.
An algorithm for the traveling salesman problem with pickup and delivery customers
Bahman Kalantari, Arthur V Hill, and Sant R Arora. 1985 · 1985
Earlier work this paper cites.
The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization
EL Lawler, JK Lenstra, AHG Rinnooy Kan, and DB Shmoys. 1986 · 1986
Earlier work this paper cites.
The prize collecting traveling salesman problem
Egon Balas. 1989 · 1989
Earlier work this paper cites.
The selective travelling salesman problem
Gilbert Laporte and Silvano Martello. 1990 · 1990
Earlier work this paper cites.
Adaptive mixtures of local experts
Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton. 1991 · 1991
Earlier work this paper cites.
TSPLIB—A traveling salesman problem library
Gerhard Reinelt. 1991 · 1991
Earlier work this paper cites.
Routing and scheduling in a flexible job shop by tabu search
Paolo Brandimarte. 1993 · 1993
Earlier work this paper cites.
Benchmarks for basic scheduling problems
Eric Taillard. 1993 · 1993
Earlier work this paper cites.
Hierarchical mixtures of experts and the EM algorithm
Michael I Jordan and Robert A Jacobs. 1994 · 1994
Earlier work this paper cites.
The general pickup and delivery problem
Martin WP Savelsbergh and Marc Sol. 1995 · 1995
Earlier work this paper cites.
A fast and effective heuristic for the orienteering problem
I-Ming Chao, Bruce L Golden, and Edward A Wasil. 1996 · 1996
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Solving the orienteering problem through branch-and-cut
Matteo Fischetti, Juan Jose Salazar Gonzalez, and Paolo Toth. 1998 · 1998
Earlier work this paper cites.
The budgeted maximum coverage problem
Samir Khuller, Anna Moss, and Joseph Seffi Naor. 1999 · 1999
Earlier work this paper cites.
Actor-critic algorithms
Vijay Konda and John Tsitsiklis. 1999 · 1999
Earlier work this paper cites.
Policy gradient methods for reinforcement learning with function approximation
Richard S Sutton, David McAllester, Satinder Singh, and Yishay Mansour. 1999 · 1999
Earlier work this paper cites.
Location problems in the public sector
Vladimir Marianov, Daniel Serra, et al · 2002
Earlier work this paper cites.
Facility location: applications and theory
Zvi Drezner and Horst W Hamacher. 2004 · 2004
Earlier work this paper cites.
Perspectives on free and open source software
Joseph Feller. 2005 · 2005
Earlier work this paper cites.
The open vehicle routing problem: Algorithms, large-scale test problems, and computational results
Feiyue Li, Bruce Golden, and Edward Wasil. 2007 · 2005
Earlier work this paper cites.
Coverage optimization to support security monitoring
Alan T Murray, Kamyoung Kim, James W Davis, Raghu Machiraju, and Richard Parent. 2007 · 2007
Earlier work this paper cites.
Ant colony optimization and its application to the vehicle routing problem with pickups and deliveries
Bülent Çatay. 2009 · 2009
Earlier work this paper cites.
MILP software
Jeffrey T Linderoth, Andrea Lodi, et al · 2010
Earlier work this paper cites.
Set-Cover approximation algorithms for load-aware readers placement in RFID networks. In 2011 IEEE international conference on communications (ICC) . IEEE, 1–6
Kashif Ali, Waleed Alsalih, and Hossam Hassanein. 2011 · 2011
Earlier work this paper cites.
Measuring the impact of primal heuristics
Timo Berthold. 2013 · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning . pmlr, 448–456
Sergey Ioffe and Christian Szegedy. 2015 · 2015
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Earlier work this paper cites.
Pointer Networks. In Advances in Neural Information Processing Systems , Vol. 28. 2692–2700
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. 2015 · 2015
Earlier work this paper cites.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. 2016 · 2016
Earlier work this paper cites.
Instance normalization: The missing ingredient for fast stylization
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. 2016 · 2016
Earlier work this paper cites.
Neural Combinatorial Optimization with Reinforcement Learning
Irwan Bello, Hieu Pham, Quoc V. Le, Mohammad Norouzi, and Samy Bengio. 2017 · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Earlier work this paper cites.
An Extension of the Lin-Kernighan-Helsgaun TSP Solver for Constrained Traveling Salesman and Vehicle Routing Problems
Keld Helsgaun. 2017 · 2017
Earlier work this paper cites.
Learning combinatorial optimization algorithms over graphs
Elias Khalil, Hanjun Dai, Yuyu Zhang, Bistra Dilkina, and Le Song. 2017 · 2017
Earlier work this paper cites.
Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations
Thomas N Kipf and Max Welling. 2017 · 2017
Earlier work this paper cites.
Ray rllib: A composable and scalable reinforcement learning library
Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Joseph Gonzalez, Ken Goldberg, and Ion Stoica. 2017 · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017
Earlier work this paper cites.
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. In International Conference on Learning Representations
Noam Shazeer, *Azalia Mirhoseini, *Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. 2017 · 2017
Earlier work this paper cites.
Mastering the game of Go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio, et al · 2017
Earlier work this paper cites.
JAX: composable transformations of Python+NumPy programs
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang. 2018 · 2018
Earlier work this paper cites.
Investigation of wind farm location planning by considering budget constraints
Reza Lotfi, Ali Mostafaeipour, Nooshin Mardani, and Shadi Mardani. 2018 · 2018
Earlier work this paper cites.
Handbook of heuristics
Rafael Mart, Panos M Pardalos, and Mauricio GC Resende. 2018 · 2018
Earlier work this paper cites.
Reinforcement learning for solving the vehicle routing problem
Mohammadreza Nazari, Afshin Oroojlooy, Lawrence Snyder, and Martin Takác. 2018 · 2018
Earlier work this paper cites.
Multi-round influence maximization. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining . 2249–2258
Lichao Sun, Weiran Huang, Philip S Yu, and Wei Chen. 2018 · 2018
Earlier work this paper cites.
Orl: Reinforcement learning benchmarks for online stochastic optimization problems
Bharathan Balaji, Jordan Bell-Masterson, Enes Bilgin, Andreas Damianou, Pablo Moreno Garcia, Arpit Jain, Runfei Luo, Alvaro Maggiar, Balakrishnan Narayanaswamy, and Chun Ye. 2019 · 2019
Earlier work this paper cites.
Google colaboratory
Ekaba Bisong and Ekaba Bisong. 2019 · 2019
Earlier work this paper cites.
How attentive are graph attention networks?. In International Conference on Learning Representations
Shaked Brody, Uri Alon, and Eran Yahav. 2019 · 2019
Earlier work this paper cites.
Learning to Perform Local Rewriting for Combinatorial Optimization. In Advances in Neural Information Processing Systems
Xinyun Chen and Yuandong Tian. 2019 · 2019
Cited alongside, same era.
Ant colony optimization: overview and recent advances
Marco Dorigo and Thomas Stützle. 2019 · 2019
Cited alongside, same era.
PyTorch Lightning
William Falcon and The PyTorch Lightning team. 2019 · 2019
Cited alongside, same era.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019 · 2019
Cited alongside, same era.
An efficient graph convolutional network technique for the travelling salesman problem
Chaitanya K Joshi, Thomas Laurent, and Xavier Bresson. 2019 · 2019
Cited alongside, same era.
BQ-NCO: Bisimulation Quotienting for Generalizable Neural Combinatorial Optimization
Darko Drakulic, Sofia Michel, Florian Mai, Arnaud Sors, and Jean-Marc Andreoli. 2023 · 2023
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Winner takes it all: Training performant rl populations for combinatorial optimization
Nathan Grinsztajn, Daniel Furelos-Blanco, Shikha Surana, Clément Bonnet, and Tom Barrett. 2023 · 2023
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DevFormer: A Symmetric Transformer for Context-Aware Device Placement
Haeyeon Kim, Minsu Kim, Federico Berto, Joungho Kim, and Jinkyoo Park. 2023 · 2023
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Learning Feature Embedding Refiner for Solving Vehicle Routing Problems
Jingwen Li, Yining Ma, Zhiguang Cao, Yaoxin Wu, Wen Song, Jie Zhang, and Yeow Meng Chee. 2023 · 2023
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Large language models as evolutionary optimizers
Shengcai Liu, Caishun Chen, Xinghua Qu, Ke Tang, and Yew-Soon Ong. 2023 · 2023
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Attention, learn to solve routing problems!
Wouter Kool, Herke Van Hoof, and Max Welling. 2019a · 2019
Cited alongside, same era.
PyTorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
Optimization of competitive facility location for chain stores
Wenxuan Shan, Qianqian Yan, Chao Chen, Mengjie Zhang, Baozhen Yao, and Xuemei Fu. 2019 · 2019
Cited alongside, same era.
Hydra - A framework for elegantly configuring complex applications
Omry Yadan. 2019 · 2019
Cited alongside, same era.
Learning to solve vehicle routing problems with time windows through joint attention
Jonas K Falkner and Lars Schmidt-Thieme. 2020 · 2020
Cited alongside, same era.
Learning a latent search space for routing problems using variational autoencoders. In International Conference on Learning Representations
André Hottung, Bhanu Bhandari, and Kevin Tierney. 2020 · 2020
Cited alongside, same era.
OR-Gym: A reinforcement learning library for operations research problems
Christian D Hubbs, Hector D Perez, Owais Sarwar, Nikolaos V Sahinidis, Ignacio E Grossmann, and John M Wassick. 2020 · 2020
Cited alongside, same era.
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Unsupervised Learning for Solving the Travelling Salesman Problem. In Neural Information Processing Systems
Yimeng Min, Yiwei Bai, and Carla P Gomes. 2023 · 2023
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TensorDict: your PyTorch universal data carrier
Vincent Moens. 2023 · 2023
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Faster Causal Attention Over Large Sequences Through Sparse Flash Attention
Matteo Pagliardini, Daniele Paliotta, Martin Jaggi, and François Fleuret. 2023 · 2023
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Versatile Genetic Algorithm-Bayesian Optimization (GA-BO) Bi-Level Optimization for Decoupling Capacitor Placement. In 2023 IEEE 32nd Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS) . IEEE, 1–3
Hyunah Park, Haeyeon Kim, Hyunwoo Kim, Joonsang Park, Seonguk Choi, Jihun Kim, Keeyoung Son, Haeseok Suh, Taesoo Kim, Jungmin Ahn, et al · 2023
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Learn to Solve the Min-max Multiple Traveling Salesmen Problem with Reinforcement Learning. In Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems . 878–886
Junyoung Park, Changhyun Kwon, and Jinkyoo Park. 2023b · 2023
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OR-Tools
Laurent Perron and Vincent Furnon. 2023 · 2023
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Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization. In Proceedings of the 40th International Conference on Machine Learning , Vol. 202. PMLR, 32194–32210
Jiwoo Son, Minsu Kim, Hyeonah Kim, and Jinkyoo Park. 2023 · 2023
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DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimization. In Advances in Neural Information Processing Systems , Vol. 36. 3706–3731
Zhiqing Sun and Yiming Yang. 2023 · 2023
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Routing Arena: A Benchmark Suite for Neural Routing Solvers
Daniela Thyssens, Tim Dernedde, Jonas K Falkner, and Lars Schmidt-Thieme. 2023 · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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RLOR: A Flexible Framework of Deep Reinforcement Learning for Operation Research
Ching Pui Wan, Tung Li, and Jason Min Wang. 2023 · 2023
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DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization
Haoran Ye, Jiarui Wang, Zhiguang Cao, Helan Liang, and Yong Li. 2023 · 2023
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Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets. In Advances in Neural Information Processing Systems , A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine (Eds.), Vol. 36. Curran Associates, Inc., 11952–11969
Dinghuai Zhang, Hanjun Dai, Nikolay Malkin, Aaron C Courville, Yoshua Bengio, and Ling Pan. 2023 · 2023
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Towards Omni-generalizable Neural Methods for Vehicle Routing Problems. In International Conference on Machine Learning
Jianan Zhou, Yaoxin Wu, Wen Song, Zhiguang Cao, and Jie Zhang. 2023 · 2023
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OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Models. In International Conference on Machine Learning
Ali AhmadiTeshnizi, Wenzhi Gao, and Madeleine Udell. 2024 · 2024
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RouteFinder: Towards Foundation Models for Vehicle Routing Problems
Federico Berto, Chuanbo Hua, Nayeli Gast Zepeda, André Hottung, Niels Wouda, Leon Lan, Kevin Tierney, and Jinkyoo Park. 2024 · 2024
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Learning to handle complex constraints for vehicle routing problems
Jieyi Bi, Yining Ma, Jianan Zhou, Wen Song, Zhiguang Cao, Yaoxin Wu, and Jie Zhang. 2024 · 2024
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Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX. In International Conference on Learning Representations
Clément Bonnet, Daniel Luo, Donal Byrne, Shikha Surana, Sasha Abramowitz, Paul Duckworth, Vincent Coyette, Laurence I. Midgley, Elshadai Tegegn, Tristan Kalloniatis, Omayma Mahjoub, Matthew Macfarlane, Andries P. Smit, Nathan Grinsztajn, Raphael Boige, Cemlyn N. Waters, Mohamed A. Mimouni, Ulrich A. Mbou Sob, Ruan de Kock, Siddarth Singh, Daniel Furelos-Blanco, Victor Le, Arnu Pretorius, and Alexandre Laterre. 2024 · 2024
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Albert Bou, Matteo Bettini, Sebastian Dittert, Vikash Kumar, Shagun Sodhani, Xiaomeng Yang, Gianni De Fabritiis, and Vincent Moens. 2024 · 2024
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Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization: Cardinality, Minimum, Covering, and More. In International Conference on Machine Learning
Fanchen Bu, Hyeonsoo Jo, Soo Yong Lee, Sungsoo Ahn, and Kijung Shin. 2024 · 2024
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Combinatorial optimization with policy adaptation using latent space search
Felix Chalumeau, Shikha Surana, Clément Bonnet, Nathan Grinsztajn, Arnu Pretorius, Alexandre Laterre, and Tom Barrett. 2024 · 2024
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Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement
Jinbiao Chen, Zizhen Zhang, Zhiguang Cao, Yaoxin Wu, Yining Ma, Te Ye, and Jiahai Wang. 2024 · 2024
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RoutePlacer: An End-to-End Routability-Aware Placer with Graph Neural Network. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Yunbo Hou, Haoran Ye, Yingxue Zhang, Siyuan Xu, and Guojie Song. 2024 · 2024
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Self-Guiding Exploration for Combinatorial Problems
Zangir Iklassov, Yali Du, Farkhad Akimov, and Martin Takac. 2024 · 2024
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RouteExplainer: An Explanation Framework for Vehicle Routing Problem. In Pacific-Asia Conference on Knowledge Discovery and Data Mining . Springer, 30–42
Daisuke Kikuta, Hiroki Ikeuchi, Kengo Tajiri, and Yuusuke Nakano. 2024 · 2024
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From distribution learning in training to gradient search in testing for combinatorial optimization
Yang Li, Jinpei Guo, Runzhong Wang, and Junchi Yan. 2024 · 2024
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CVRPLIB: Capacitated Vehicle Routing Problem Library
Ivan Lima, Eduardo Uchoa, Diego Pecin, Artur Pessoa, Marcus Poggi, Thibaut Vidal, Anand Subramanian, Richard W, Daniel Oliveira, and Eduardo Queiroga. 2014 · 2024
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Neural combinatorial optimization with heavy decoder: Toward large scale generalization
Fu Luo, Xi Lin, Fei Liu, Qingfu Zhang, and Zhenkun Wang. 2024a · 2024
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Self-Improved Learning for Scalable Neural Combinatorial Optimization
Fu Luo, Xi Lin, Zhenkun Wang, Tong Xialiang, Mingxuan Yuan, and Qingfu Zhang. 2024b · 2024
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Neural Combinatorial Optimization on Heterogeneous Graphs: An Application to the Picker Routing Problem in Mixed-Shelves Warehouses. In Proceedings of the International Conference on Automated Planning and Scheduling , Vol. 34. 351–359
Laurin Luttmann and Lin Xie. 2024 · 2024
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Learning to search feasible and infeasible regions of routing problems with flexible neural k-opt
Yining Ma, Zhiguang Cao, and Yeow Meng Chee. 2024 · 2024
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Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but Improvement
Jonathan Pirnay and Dominik G Grimm. 2024 · 2024
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Mathematical discoveries from program search with large language models
Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Matej Balog, M Pawan Kumar, Emilien Dupont, Francisco JR Ruiz, Jordan S Ellenberg, Pengming Wang, Omar Fawzi, et al · 2024
Closest in time.
Equity-Transformer: Solving NP-Hard Min-Max Routing Problems as Sequential Generation with Equity Context. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 38. 20265–20273
Jiwoo Son, Minsu Kim, Sanghyeok Choi, Hyeonah Kim, and Jinkyoo Park. 2024 · 2024
Closest in time.
HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding
Huijie Tang, Federico Berto, Zihan Ma, Chuanbo Hua, Kyuree Ahn, and Jinkyoo Park. 2024b · 2024
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Ensembling Prioritized Hybrid Policies for Multi-agent Pathfinding
Huijie Tang, Federico Berto, and Jinkyoo Park. 2024a · 2024
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From Large Language Models and Optimization to Decision Optimization CoPilot: A Research Manifesto
Segev Wasserkrug, Leonard Boussioux, Dick den Hertog, Farzaneh Mirzazadeh, Ilker Birbil, Jannis Kurtz, and Donato Maragno. 2024 · 2024
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PyVRP: A high-performance VRP solver package
Niels A Wouda, Leon Lan, and Wouter Kool. 2024 · 2024
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Chain-of-Experts: When LLMs Meet Complex Operations Research Problems. In International Conference on Learning Representations
Ziyang Xiao, Dongxiang Zhang, Yangjun Wu, Lilin Xu, Yuan Jessica Wang, Xiongwei Han, Xiaojin Fu, Tao Zhong, Jia Zeng, Mingli Song, and Gang Chen. 2024 · 2024
Closest in time.
Large Language Models as Optimizers. In International Conference on Learning Representations
Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V Le, Denny Zhou, and Xinyun Chen. 2024 · 2024
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DPN: Decoupling Partition and Navigation for Neural Solvers of Min-max Vehicle Routing Problems
Zhi Zheng, Shunyu Yao, Zhenkun Wang, Xialiang Tong, Mingxuan Yuan, and Ke Tang. 2024 · 2024
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MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts. In International Conference on Machine Learning
Jianan Zhou, Zhiguang Cao, Yaoxin Wu, Wen Song, Yining Ma, Jie Zhang, and Chi Xu. 2024 · 2024
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DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
DeepSeek-AI and DeepSeek-R1 Team. 2025 · 2025
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PolyNet: Learning Diverse Solution Strategies for Neural Combinatorial Optimization. In International Conference on Learning Representations
André Hottung, Mridul Mahajan, and Kevin Tierney. 2025 · 2025
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Ant Colony Sampling with GFlowNets for Combinatorial Optimization. In Proceedings of The 28th International Conference on Artificial Intelligence and Statistics , Vol. 258. 469–477
Minsu Kim, Sanghyeok Choi, Jiwoo Son, Hyeonah Kim, Jinkyoo Park, and Yoshua Bengio. 2025 · 2025
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Streamlining the Design Space of ML4TSP Suggests Principles for Learning and Search. In International Conference on Learning Representations
Yang Li, Jiale Ma, Wenzheng Pan, Runzhong Wang, Haoyu Geng, Nianzu Yang, and Junchi Yan. 2025 · 2025
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Awesome Machine Learning for Combinatorial Optimization
Chang Liu, Runzhong Wang, Jiayi Zhang, Zelin Zhao, Haoyu Geng, Tianzhe Wang, Wenxuan Guo, Wenjie Wu, Nianzu Yang, Ziao Guo, Yang Li, Hao Xiong, and Junchi Yan. 2025 · 2025
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