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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 7180 of 1277 papers

TitleStatusHype
A Fast Task Offloading Optimization Framework for IRS-Assisted Multi-Access Edge Computing SystemCode1
Monte Carlo Policy Gradient Method for Binary OptimizationCode1
Automatic Truss Design with Reinforcement LearningCode1
D2Match: Leveraging Deep Learning and Degeneracy for Subgraph MatchingCode1
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint ProgrammingCode1
Discovering Dynamic Causal Space for DAG Structure LearningCode1
Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial OptimizationCode1
Towards Omni-generalizable Neural Methods for Vehicle Routing ProblemsCode1
DHRL-FNMR: An Intelligent Multicast Routing Approach Based on Deep Hierarchical Reinforcement Learning in SDNCode1
Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNetsCode1
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