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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

Showing 5175 of 1277 papers

TitleStatusHype
A Bayesian algorithm for retrosynthesisCode1
BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial OptimizationCode1
A Fast Task Offloading Optimization Framework for IRS-Assisted Multi-Access Edge Computing SystemCode1
CLIPPER: A Graph-Theoretic Framework for Robust Data AssociationCode1
DHRL-FNMR: An Intelligent Multicast Routing Approach Based on Deep Hierarchical Reinforcement Learning in SDNCode1
Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable ApproachCode1
Combinatorial Optimization enriched Machine Learning to solve the Dynamic Vehicle Routing Problem with Time WindowsCode1
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement LearningCode1
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on GraphsCode1
Combinatorial Optimization Perspective based Framework for Multi-behavior RecommendationCode1
A Comprehensive Evaluation of Contemporary ML-Based Solvers for Combinatorial OptimizationCode1
DOGE-Train: Discrete Optimization on GPU with End-to-end TrainingCode1
Deep Graph Matching via Blackbox Differentiation of Combinatorial SolversCode1
ASP: Learn a Universal Neural Solver!Code1
Deep Graph Matching via Blackbox Differentiation of Combinatorial SolversCode1
DeepACO: Neural-enhanced Ant Systems for Combinatorial OptimizationCode1
A Reinforcement Learning Environment For Job-Shop SchedulingCode1
Attention, Learn to Solve Routing Problems!Code1
Deep Boltzmann Machines in Estimation of Distribution Algorithms for Combinatorial OptimizationCode1
Active Learning Meets Optimized Item SelectionCode1
D2Match: Leveraging Deep Learning and Degeneracy for Subgraph MatchingCode1
A Learning-based Iterative Method for Solving Vehicle Routing ProblemsCode1
A Reinforcement Learning Approach to the Orienteering Problem with Time WindowsCode1
Are Graph Neural Networks Optimal Approximation Algorithms?Code1
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint ProgrammingCode1
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