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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
Deep Graph Matching via Blackbox Differentiation of Combinatorial SolversCode1
Discovering Dynamic Causal Space for DAG Structure LearningCode1
Are Graph Neural Networks Optimal Approximation Algorithms?Code1
A Reinforcement Learning Approach to the Orienteering Problem with Time WindowsCode1
Active Learning Meets Optimized Item SelectionCode1
DHRL-FNMR: An Intelligent Multicast Routing Approach Based on Deep Hierarchical Reinforcement Learning in SDNCode1
DeepACO: Neural-enhanced Ant Systems for Combinatorial OptimizationCode1
A Two-stage Reinforcement Learning-based Approach for Multi-entity Task AllocationCode1
A Bayesian algorithm for retrosynthesisCode1
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint ProgrammingCode1
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