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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 6170 of 1277 papers

TitleStatusHype
A Comprehensive Evaluation of Contemporary ML-Based Solvers for Combinatorial OptimizationCode1
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement LearningCode1
Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent setCode1
Are Graph Neural Networks Optimal Approximation Algorithms?Code1
D2Match: Leveraging Deep Learning and Degeneracy for Subgraph MatchingCode1
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
A Reinforcement Learning Approach to the Orienteering Problem with Time WindowsCode1
Contingency-Aware Influence Maximization: A Reinforcement Learning ApproachCode1
DAG Matters! GFlowNets Enhanced Explainer For Graph Neural NetworksCode1
Active Learning Meets Optimized Item SelectionCode1
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