A deep instance generative framework for milp solvers under limited data availability
In the past few years, there has been an explosive surge in the use of machine learning (ML)
techniques to address combinatorial optimization (CO) problems, especially mixed-integer …
techniques to address combinatorial optimization (CO) problems, especially mixed-integer …
Learning Backdoors for Mixed Integer Programs with Contrastive Learning
Many real-world problems can be efficiently modeled as Mixed Integer Programs (MIPs) and
solved with the Branch-and-Bound method. Prior work has shown the existence of MIP …
solved with the Branch-and-Bound method. Prior work has shown the existence of MIP …
Solving combinatorial optimization problems with deep neural network: A survey
F Wang, Q He, S Li - Tsinghua Science and Technology, 2024 - ieeexplore.ieee.org
Combinatorial Optimization Problems (COPs) are a class of optimization problems that are
commonly encountered in industrial production and everyday life. Over the last few decades …
commonly encountered in industrial production and everyday life. Over the last few decades …
SymILO: A symmetry-aware learning framework for integer linear optimization
Integer linear programs (ILPs) are commonly employed to model diverse practical problems
such as scheduling and planning. Recently, machine learning techniques have been …
such as scheduling and planning. Recently, machine learning techniques have been …
RL-MILP Solver: A reinforcement learning approach for solving mixed-integer linear programs with graph neural networks
TH Lee, MS Kim - arxiv preprint arxiv:2411.19517, 2024 - arxiv.org
Mixed-Integer Linear Programming (MILP) is an optimization technique widely used in
various fields. Primal heuristics, which reduce the search space of MILP, have enabled …
various fields. Primal heuristics, which reduce the search space of MILP, have enabled …
MGMatch: Fast Matchmaking with Nonlinear Objective and Constraints via Multimodal Deep Graph Learning
As a core problem of online games, matchmaking is to assign players into multiple teams to
maximize their gaming experience. With the rapid development of game industry, it is …
maximize their gaming experience. With the rapid development of game industry, it is …
Scalable Primal Heuristics Using Graph Neural Networks for Combinatorial Optimization
By examining the patterns of solutions obtained for various instances, one can gain insights
into the structure and behavior of combinatorial optimization (CO) problems and develop …
into the structure and behavior of combinatorial optimization (CO) problems and develop …
GDPlan: Generative Network Planning via Graph Diffusion Model
Network planning is crucial to facilitate network service under limited network operation
costs. However, adapting the network topology (ie, connections and capacities for physical …
costs. However, adapting the network topology (ie, connections and capacities for physical …
Multi-task Representation Learning for Mixed Integer Linear Programming
Mixed Integer Linear Programs (MILPs) are highly flexible and powerful tools for modeling
and solving complex real-world combinatorial optimization problems. Recently, machine …
and solving complex real-world combinatorial optimization problems. Recently, machine …
Efficient Rectification of Neuro-Symbolic Reasoning Inconsistencies by Abductive Reflection
Neuro-Symbolic (NeSy) AI could be regarded as an analogy to human dual-process
cognition, modeling the intuitive System 1 with neural networks and the algorithmic System 2 …
cognition, modeling the intuitive System 1 with neural networks and the algorithmic System 2 …