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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
Learning a Large Neighborhood Search Algorithm for Mixed Integer ProgramsCode1
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
Learning Large Neighborhood Search for Vehicle Routing in Airport Ground HandlingCode1
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
Contingency-Aware Influence Maximization: A Reinforcement Learning ApproachCode1
Learning the Markov Decision Process in the Sparse Gaussian EliminationCode1
JoinGym: An Efficient Query Optimization Environment for Reinforcement LearningCode1
Learning to Solve Combinatorial Optimization under Positive Linear Constraints via Non-Autoregressive Neural NetworksCode1
A Bayesian algorithm for retrosynthesisCode1
Learning with Combinatorial Optimization Layers: a Probabilistic ApproachCode1
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