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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 171180 of 1277 papers

TitleStatusHype
A Deep Instance Generative Framework for MILP Solvers Under Limited Data AvailabilityCode1
Belief Propagation Neural NetworksCode1
Learning to Solve Combinatorial Optimization under Positive Linear Constraints via Non-Autoregressive Neural NetworksCode1
CLIPPER: A Graph-Theoretic Framework for Robust Data AssociationCode1
Combinatorial Optimization enriched Machine Learning to solve the Dynamic Vehicle Routing Problem with Time WindowsCode1
Automatic Truss Design with Reinforcement LearningCode1
Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNetsCode1
Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNetsCode1
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with DroneCode1
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
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