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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 101–125 of 1277 papers

TitleStatusHype
Unsupervised Learning for Combinatorial Optimization with Principled Objective RelaxationCode1
Reinforced Lin-Kernighan-Helsgaun Algorithms for the Traveling Salesman ProblemsCode1
Learning to Control Local Search for Combinatorial OptimizationCode1
Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent setCode1
MIP-GNN: A Data-Driven Framework for Guiding Combinatorial SolversCode1
Sym-NCO: Leveraging Symmetricity for Neural Combinatorial OptimizationCode1
DOGE-Train: Discrete Optimization on GPU with End-to-end TrainingCode1
Decomposition Strategies and Multi-shot ASP Solving for Job-shop SchedulingCode1
Equivariant quantum circuits for learning on weighted graphsCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionsCode1
BILP-Q: Quantum Coalition Structure GenerationCode1
MAP-Elites based Hyper-Heuristic for the Resource Constrained Project Scheduling ProblemCode1
Learning to Solve Travelling Salesman Problem with Hardness-adaptive CurriculumCode1
Pareto Set Learning for Neural Multi-objective Combinatorial OptimizationCode1
S-Rocket: Selective Random Convolution Kernels for Time Series ClassificationCode1
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and InsightsCode1
Instance-wise algorithm configuration with graph neural networksCode1
L0Learn: A Scalable Package for Sparse Learning using L0 RegularizationCode1
ML4CO-KIDA: Knowledge Inheritance in Dataset AggregationCode1
The First AI4TSP Competition: Learning to Solve Stochastic Routing ProblemsCode1
What's Wrong with Deep Learning in Tree Search for Combinatorial OptimizationCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionCode1
Solving Dynamic Graph Problems with Multi-Attention Deep Reinforcement LearningCode1
Reconstructing Compact Building Models from Point Clouds Using Deep Implicit FieldsCode1
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with DroneCode1
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