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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 141150 of 1277 papers

TitleStatusHype
A Reinforcement Learning Environment For Job-Shop SchedulingCode1
Hybrid Pointer Networks for Traveling Salesman Problems OptimizationCode1
FireCommander: An Interactive, Probabilistic Multi-agent Environment for Heterogeneous Robot TeamsCode1
CLIPPER: A Graph-Theoretic Framework for Robust Data AssociationCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionsCode1
ASP: Learn a Universal Neural Solver!Code1
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement LearningCode1
Combinatorial Optimization enriched Machine Learning to solve the Dynamic Vehicle Routing Problem with Time WindowsCode1
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
Feature Importance Ranking for Deep LearningCode1
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