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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 151160 of 1277 papers

TitleStatusHype
A Reinforcement Learning Environment For Job-Shop SchedulingCode1
SeaPearl: A Constraint Programming Solver guided by Reinforcement LearningCode1
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* NeighborhoodCode1
CLIPPER: A Graph-Theoretic Framework for Robust Data AssociationCode1
Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization SolversCode1
Geometric Deep Reinforcement Learning for Dynamic DAG SchedulingCode1
A Reinforcement Learning Approach to the Orienteering Problem with Time WindowsCode1
FireCommander: An Interactive, Probabilistic Multi-agent Environment for Heterogeneous Robot TeamsCode1
POMO: Policy Optimization with Multiple Optima for Reinforcement LearningCode1
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