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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 161170 of 1277 papers

TitleStatusHype
Reinforcement Learning with Combinatorial Actions: An Application to Vehicle RoutingCode1
Feature Importance Ranking for Deep LearningCode1
Deep Graph Matching via Blackbox Differentiation of Combinatorial SolversCode1
Adversarial Immunization for Certifiable Robustness on GraphsCode1
Belief Propagation Neural NetworksCode1
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on GraphsCode1
Learning What to Defer for Maximum Independent SetsCode1
Learning the Travelling Salesperson Problem Requires Rethinking GeneralizationCode1
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
Dynamic Partial Removal: A Neural Network Heuristic for Large Neighborhood SearchCode1
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