Results 11 to 20 of about 8,140,957 (328)
Multimode extensions of Combinatorial Optimization problems [PDF]
Electronic Notes in Discrete Mathematics, 2016 We review some complexity results and present a viable heuristic approach based on the Variable Neighborhood Search (VNS) framework for multimode extension of combinatorial optimization problems, such as the the Set Covering Problem (SCP) and the Covering Location Problem (CLP).
Cordone, R., Lulli, G.
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New techniques for cost sharing in combinatorial optimization games [PDF]
, 2010 Combinatorial optimization games form an important subclass of cooperative games. In recent years, increased attention has been given to the issue of finding good cost shares for such games.
Caprara, A +3 more
core +5 more sources
DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization [PDF]
Neural Information Processing Systems, 2023 Ant Colony Optimization (ACO) is a meta-heuristic algorithm that has been successfully applied to various Combinatorial Optimization Problems (COPs). Traditionally, customizing ACO for a specific problem requires the expert design of knowledge-driven ...
Haoran Ye +4 more
semanticscholar +1 more source
RL4CO: An Extensive Reinforcement Learning for Combinatorial Optimization Benchmark [PDF]
Knowledge Discovery and Data Mining, 2023 Combinatorial optimization (CO) is fundamental to several real-world applications, from logistics and scheduling to hardware design and resource allocation.
Federico Berto +9 more
semanticscholar +1 more source
Combinatorial optimization and reasoning with graph neural networks [PDF]
International Joint Conference on Artificial Intelligence, 2021 Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have mostly focused on solving problem instances in isolation, ignoring the fact that they often stem from related data ...
Quentin Cappart +5 more
semanticscholar +1 more source
Combinatorial optimization with physics-inspired graph neural networks [PDF]
Nature Machine Intelligence, 2021 Combinatorial optimization problems are pervasive across science and industry. Modern deep learning tools are poised to solve these problems at unprecedented scales, but a unifying framework that incorporates insights from statistical physics is still ...
M. Schuetz +2 more
semanticscholar +1 more source
Hysteresis in Combinatorial Optimization Problems
The International FLAIRS Conference Proceedings, 2021 Hysteresis is a physical phenomenon reflected in macroscopic observables of materials that are subjected to external perturbations. For example, magnetic hysteresis is observed in ferromagnetic metals such as iron, nickel and cobalt in the presence of a changing external magnetic field.
Yuling Guan +4 more
openaire +3 more sources
Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization [PDF]
Neural Information Processing Systems, 2022 Deep reinforcement learning (DRL)-based combinatorial optimization (CO) methods (i.e., DRL-NCO) have shown significant merit over the conventional CO solvers as DRL-NCO is capable of learning CO solvers less relying on problem-specific expert domain ...
Minsu Kim, Junyoung Park, Jinkyoo Park
semanticscholar +1 more source
Local and global lifted cover inequalities for the 0-1 multidimensional knapsack problem [PDF]
, 2007 The 0-1 Multidimensional Knapsack Problem (0-1 MKP) is a well- known (and strongly N P -hard) combinatorial optimization problem with many applications.
Kaparis, Konstantinos +2 more
core +5 more sources
DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems [PDF]
Neural Information Processing Systems, 2022 Recently, deep reinforcement learning (DRL) models have shown promising results in solving NP-hard Combinatorial Optimization (CO) problems. However, most DRL solvers can only scale to a few hundreds of nodes for combinatorial optimization problems on ...
Ruizhong Qiu, Zhiqing Sun, Yiming Yang
semanticscholar +1 more source