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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 831840 of 1277 papers

TitleStatusHype
Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph MatchingCode2
Neural Knapsack: A Neural Network Based Solver for the Knapsack ProblemCode0
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
Fixed Priority Global Scheduling from a Deep Learning Perspective0
Divide and Learn: A Divide and Conquer Approach for Predict+Optimize0
Content Provider Dynamics and Coordination in Recommendation Ecosystems0
Dynamic Submodular Maximization0
Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* NeighborhoodCode1
CLIPPER: A Graph-Theoretic Framework for Robust Data AssociationCode1
Distributed Injection-Locking in Analog Ising Machines to Solve Combinatorial Optimizations0
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