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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 391400 of 1277 papers

TitleStatusHype
Combining Reinforcement Learning and Optimal Transport for the Traveling Salesman ProblemCode0
Efficient Heuristics Generation for Solving Combinatorial Optimization Problems Using Large Language ModelsCode0
Efficient Combinatorial Optimization via Heat DiffusionCode0
Ecole: A Library for Learning Inside MILP SolversCode0
Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise ConstraintsCode0
Reheated Gradient-based Discrete Sampling for Combinatorial OptimizationCode0
Graph-SCP: Accelerating Set Cover Problems with Graph Neural NetworksCode0
Protecting Geolocation Privacy of Photo CollectionsCode0
Combining Learned Representations for Combinatorial Optimization0
Application of Decision Tree Classifier in Detection of Specific Denial of Service Attacks with Genetic Algorithm Based Feature Selection on NSL-KDD0
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