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Combinatorial Optimization

Combinatorial Optimization is a category of problems which requires optimizing a function over a combination of discrete objects and the solutions are constrained. Examples include finding shortest paths in a graph, maximizing value in the Knapsack problem and finding boolean settings that satisfy a set of constraints. Many of these problems are NP-Hard, which means that no polynomial time solution can be developed for them. Instead, we can only produce approximations in polynomial time that are guaranteed to be some factor worse than the true optimal solution.

Source: Recent Advances in Neural Program Synthesis

Papers

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Showing 1–50 of 1277 papers

TitleStatusHype
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization BenchmarkCode4
Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language ModelCode3
ReEvo: Large Language Models as Hyper-Heuristics with Reflective EvolutionCode3
MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-ExpertsCode3
Diffusion models as plug-and-play priorsCode2
Domain-Independent Dynamic ProgrammingCode2
DevFormer: A Symmetric Transformer for Context-Aware Device PlacementCode2
UDC: A Unified Neural Divide-and-Conquer Framework for Large-Scale Combinatorial Optimization ProblemsCode2
Ant Colony Sampling with GFlowNets for Combinatorial OptimizationCode2
A Diffusion Model Framework for Unsupervised Neural Combinatorial OptimizationCode2
HeuriGym: An Agentic Benchmark for LLM-Crafted Heuristics in Combinatorial OptimizationCode2
HeurAgenix: Leveraging LLMs for Solving Complex Combinatorial Optimization ChallengesCode2
Monte Carlo Tree Search for Comprehensive Exploration in LLM-Based Automatic Heuristic DesignCode2
Efficient Parallel Genetic Algorithm for Perturbed Substructure Optimization in Complex NetworkCode2
Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph MatchingCode2
DIFUSCO: Graph-based Diffusion Solvers for Combinatorial OptimizationCode2
Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement LearningCode2
LLMOPT: Learning to Define and Solve General Optimization Problems from ScratchCode2
Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey with PerspectivesCode2
Solving the Job Shop Scheduling Problem with Graph Neural Networks: A Customizable Reinforcement Learning EnvironmentCode2
FloorSet -- a VLSI Floorplanning Dataset with Design Constraints of Real-World SoCsCode2
Combinatorial Optimization with Automated Graph Neural NetworksCode2
Learning to Solve Job Shop Scheduling under UncertaintyCode2
DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective PartitioningCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionsCode1
DAG Matters! GFlowNets Enhanced Explainer For Graph Neural NetworksCode1
DeepACO: Neural-enhanced Ant Systems for Combinatorial OptimizationCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionCode1
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on GraphsCode1
A Comprehensive Evaluation of Contemporary ML-Based Solvers for Combinatorial OptimizationCode1
Contingency-Aware Influence Maximization: A Reinforcement Learning ApproachCode1
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent setCode1
A Two-stage Reinforcement Learning-based Approach for Multi-entity Task AllocationCode1
Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming ProblemCode1
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
D2Match: Leveraging Deep Learning and Degeneracy for Subgraph MatchingCode1
Deep Boltzmann Machines in Estimation of Distribution Algorithms for Combinatorial OptimizationCode1
A Reinforcement Learning Approach to the Orienteering Problem with Time WindowsCode1
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing ProblemsCode1
A Reinforcement Learning Environment For Job-Shop SchedulingCode1
Combinatorial Optimization with Graph Convolutional Networks and Guided Tree SearchCode1
A Deep Instance Generative Framework for MILP Solvers Under Limited Data AvailabilityCode1
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with DroneCode1
Are Graph Neural Networks Optimal Approximation Algorithms?Code1
Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable ApproachCode1
Attention, Learn to Solve Routing Problems!Code1
Automatic Truss Design with Reinforcement LearningCode1
Adversarial Immunization for Certifiable Robustness on GraphsCode1
Combinatorial Optimization Perspective based Framework for Multi-behavior RecommendationCode1
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