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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 131140 of 1277 papers

TitleStatusHype
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming ProblemCode1
BILP-Q: Quantum Coalition Structure GenerationCode1
BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial OptimizationCode1
A Reinforcement Learning Environment For Job-Shop SchedulingCode1
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement LearningCode1
Attention, Learn to Solve Routing Problems!Code1
Combinatorial Optimization Perspective based Framework for Multi-behavior RecommendationCode1
Are Graph Neural Networks Optimal Approximation Algorithms?Code1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionsCode1
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