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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 151–200 of 1277 papers

TitleStatusHype
A Reinforcement Learning Environment For Job-Shop SchedulingCode1
SeaPearl: A Constraint Programming Solver guided by Reinforcement LearningCode1
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* NeighborhoodCode1
CLIPPER: A Graph-Theoretic Framework for Robust Data AssociationCode1
Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization SolversCode1
Geometric Deep Reinforcement Learning for Dynamic DAG SchedulingCode1
A Reinforcement Learning Approach to the Orienteering Problem with Time WindowsCode1
FireCommander: An Interactive, Probabilistic Multi-agent Environment for Heterogeneous Robot TeamsCode1
POMO: Policy Optimization with Multiple Optima for Reinforcement LearningCode1
Reinforcement Learning with Combinatorial Actions: An Application to Vehicle RoutingCode1
Feature Importance Ranking for Deep LearningCode1
Deep Graph Matching via Blackbox Differentiation of Combinatorial SolversCode1
Adversarial Immunization for Certifiable Robustness on GraphsCode1
Belief Propagation Neural NetworksCode1
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on GraphsCode1
Learning What to Defer for Maximum Independent SetsCode1
Learning the Travelling Salesperson Problem Requires Rethinking GeneralizationCode1
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
Dynamic Partial Removal: A Neural Network Heuristic for Large Neighborhood SearchCode1
Exploring the Loss Landscape in Neural Architecture SearchCode1
A Learning-based Iterative Method for Solving Vehicle Routing ProblemsCode1
Rethinking Differentiable Search for Mixed-Precision Neural NetworksCode1
Deep Graph Matching via Blackbox Differentiation of Combinatorial SolversCode1
A Bayesian algorithm for retrosynthesisCode1
Incremental Sampling Without Replacement for Sequence ModelsCode1
Learn to Design the Heuristics for Vehicle Routing ProblemCode1
Reinforcement Learning Enhanced Quantum-inspired Algorithm for Combinatorial OptimizationCode1
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing ProblemsCode1
Targeted sampling of enlarged neighborhood via Monte Carlo tree search for TSPCode1
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement LearningCode1
Graph Neural Networks for Maximum Constraint SatisfactionCode1
Exact Combinatorial Optimization with Graph Convolutional Neural NetworksCode1
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
Combinatorial Optimization with Graph Convolutional Networks and Guided Tree SearchCode1
Attention, Learn to Solve Routing Problems!Code1
Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization AlgorithmsCode1
Neural Combinatorial Optimization with Reinforcement LearningCode1
Generative Adversarial Networks in Estimation of Distribution Algorithms for Combinatorial OptimizationCode1
Deep Boltzmann Machines in Estimation of Distribution Algorithms for Combinatorial OptimizationCode1
Pointer NetworksCode1
Denoising Autoencoders for fast Combinatorial Black Box OptimizationCode1
LRM-1B: Towards Large Routing Model—0
Large Language Models for Combinatorial Optimization: A Systematic Review—0
Higher-Order Neuromorphic Ising Machines -- Autoencoders and Fowler-Nordheim Annealers are all you need for Scalability—0
On Training-Test (Mis)alignment in Unsupervised Combinatorial Optimization: Observation, Empirical Exploration, and AnalysisCode0
GreedyPrune: Retenting Critical Visual Token Set for Large Vision Language Models—0
Synthesizing Min-Max Control Barrier Functions For Switched Affine Systems—0
Large Language Models for Design Structure Matrix Optimization—0
Synergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization—0
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