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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 101–150 of 1277 papers

TitleStatusHype
Simulation-guided Beam Search for Neural Combinatorial OptimizationCode1
Reinforced Lin-Kernighan-Helsgaun Algorithms for the Traveling Salesman ProblemsCode1
Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent setCode1
Learning to Control Local Search for Combinatorial OptimizationCode1
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
The First AI4TSP Competition: Learning to Solve Stochastic Routing ProblemsCode1
ML4CO-KIDA: Knowledge Inheritance in Dataset AggregationCode1
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
Decision-Focused Learning: Through the Lens of Learning to RankCode1
Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming SeedingCode1
Active Learning Meets Optimized Item SelectionCode1
One model Packs Thousands of Items with Recurrent Conditional Query LearningCode1
Symbolic Regression via Neural-Guided Genetic Programming Population SeedingCode1
OpenABC-D: A Large-Scale Dataset For Machine Learning Guided Integrated Circuit SynthesisCode1
RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender SystemCode1
Hybrid Pointer Networks for Traveling Salesman Problems OptimizationCode1
Learning the Markov Decision Process in the Sparse Gaussian EliminationCode1
Rationales for Sequential PredictionsCode1
RAMA: A Rapid Multicut Algorithm on GPUCode1
Learning a Large Neighborhood Search Algorithm for Mixed Integer ProgramsCode1
Maximum Entropy Weighted Independent Set Pooling for Graph Neural NetworksCode1
Combinatorial Optimization with Physics-Inspired Graph Neural NetworksCode1
Learning Primal Heuristics for Mixed Integer ProgramsCode1
Matrix Encoding Networks for Neural Combinatorial OptimizationCode1
Contingency-Aware Influence Maximization: A Reinforcement Learning ApproachCode1
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on GraphsCode1
Efficient Active Search for Combinatorial Optimization ProblemsCode1
Noisy intermediate-scale quantum algorithm for semidefinite programmingCode1
Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable ApproachCode1
Self-Supervision is All You Need for Solving Rubik's CubeCode1
Implicit MLE: Backpropagating Through Discrete Exponential Family DistributionsCode1
Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization ProblemsCode1
Solve routing problems with a residual edge-graph attention neural networkCode1
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