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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

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Showing 131–140 of 1277 papers

TitleStatusHype
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
Attention, Learn to Solve Routing Problems!Code1
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
A Two-stage Reinforcement Learning-based Approach for Multi-entity Task AllocationCode1
A Reinforcement Learning Environment For Job-Shop SchedulingCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionsCode1
BILP-Q: Quantum Coalition Structure GenerationCode1
Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization AlgorithmsCode1
Generative Adversarial Networks in Estimation of Distribution Algorithms for Combinatorial OptimizationCode1
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