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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 121130 of 1277 papers

TitleStatusHype
What's Wrong with Deep Learning in Tree Search for Combinatorial OptimizationCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionCode1
Solving Dynamic Graph Problems with Multi-Attention Deep Reinforcement LearningCode1
Reconstructing Compact Building Models from Point Clouds Using Deep Implicit FieldsCode1
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with DroneCode1
Decision-Focused Learning: Through the Lens of Learning to RankCode1
Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming SeedingCode1
Active Learning Meets Optimized Item SelectionCode1
One model Packs Thousands of Items with Recurrent Conditional Query LearningCode1
Symbolic Regression via Neural-Guided Genetic Programming Population SeedingCode1
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